Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem....Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem.As the state of art 3D super-resolution localization algorithm based on deep learning,FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing,even though it has greatly improved the data processing throughput.In this paper,a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM.This new algorithm uses the feature compression method to reduce the parameters of the model,and combines it with pipeline programming to accelerate the inference process of the deep learning model.The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy,which can realize real-time processing of 256×256 pixels size images.The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering,and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.展开更多
The real-time risk-averse dispatch problem of an integrated electricity and natural gas system(IEGS)is studied in this paper.It is formulated as a real-time conditional value-at-risk(CVaR)-based risk-averse dis-patch ...The real-time risk-averse dispatch problem of an integrated electricity and natural gas system(IEGS)is studied in this paper.It is formulated as a real-time conditional value-at-risk(CVaR)-based risk-averse dis-patch model in the Markov decision process framework.Because of its stochasticity,nonconvexity and nonlinearity,the model is difficult to analyze by traditional algorithms in an acceptable time.To address this non-deterministic polynomial-hard problem,a CVaR-based lookup-table approximate dynamic programming(CVaR-ADP)algo-rithm is proposed,and the risk-averse dispatch problem is decoupled into a series of tractable subproblems.The line pack is used as the state variable to describe the impact of one period’s decision on the future.This facilitates the reduction of load shedding and wind power curtailment.Through the proposed method,real-time decisions can be made according to the current information,while the value functions can be used to overview the whole opti-mization horizon to balance the current cost and future risk loss.Numerical simulations indicate that the pro-posed method can effectively measure and control the risk costs in extreme scenarios.Moreover,the decisions can be made within 10 s,which meets the requirement of the real-time dispatch of an IEGS.Index Terms—Integrated electricity and natural gas system,approximate dynamic programming,real-time dispatch,risk-averse,conditional value-at-risk.展开更多
Real-time rendering applications leverage heterogeneous computing to optimize performance.However,software development across multiple devices presents challenges,including data layout inconsistencies,synchronization ...Real-time rendering applications leverage heterogeneous computing to optimize performance.However,software development across multiple devices presents challenges,including data layout inconsistencies,synchronization issues,resource management complexities,and architectural disparities.Additionally,the creation of such systems requires verbose and unsafe programming models.Recent developments in domain-specific and unified shading languages aim to mitigate these issues.Yet,current programming models primarily address data layout consistency,neglecting other persistent challenges.In this paper,we introduce RenderKernel,a programming model designed to simplify the development of real-time rendering systems.Recognizing the need for a high-level approach,RenderKernel addresses the specific challenges of real-time rendering,enabling development on heterogeneous systems as if they were homogeneous.This model allows for early detection and prevention of errors due to system heterogeneity at compile-time.Furthermore,RenderKernel enables the use of common programming patterns from homogeneous environments,freeing developers from the complexities of underlying heterogeneous systems.Developers can focus on coding unique application features,thereby enhancing productivity and reducing the cognitive load associated with real-time rendering system development.展开更多
Assessment of the current status of Lake Baikal proved to be based on changes in natural (“preindustrial”) chemical content in basic abiotic and biological compartments of the Lake geosystem. This approach was used ...Assessment of the current status of Lake Baikal proved to be based on changes in natural (“preindustrial”) chemical content in basic abiotic and biological compartments of the Lake geosystem. This approach was used to evaluate background “base-line levels” of 6 major and about 50 minor and trace ele-ments in the Lake Baikal water body using a number of most reliable data re-ported within 1992-2012. In terms of environment geochemistry Baikal is one of the purest water reservoirs on the Earth. A simple mass balance model was proposed for assessing possible anthropogenic impact on Baikal water geo-chemistry. Estimations of change trends showed that only for Na+, SO42-, Cl- and Mo growth rate of their average concentrations in the Lake occurred to be 1%, 3%, 7% and 2% in every 10 years. Space-time monitoring schedules for all water body compartments of the Lake are proposed as well as similar moni-toring programs for tributaries, precipitations, bottom sediments, aquatic biota.展开更多
At present, transcription analysis of gene expression commonly uses housekeeping genes as control for normalization. In this study, the expression levels of three housekeeping genes including GAPDH, β-actin, and 18S ...At present, transcription analysis of gene expression commonly uses housekeeping genes as control for normalization. In this study, the expression levels of three housekeeping genes including GAPDH, β-actin, and 18S rRNA in six tissues and five developmental stages of the Mandarin fish Siniperca chuatsi were assayed with quantitative real-time PCR （qPCR）. Differences in expression levels were analyzed using geNorm program. The results demonstrate that β-actin is the most stable gene at developmental stages and GAPDH is the most stable in different tissues. While 18S rRNA expression during development is differentially regulated, which indicates it is suitable as an internal control for gene expression normalization at the developmental level. Overall, the data suggest that the two most stable housekeeping genes are enough to accurately calibrate gene expression in S. chuatsi. The significance of this study provided convincing references and methodology for housekeeping gene selection and normalization in gene expression analysis with regular PCR or qPCR.展开更多
基金supported by the Start-up Fund from Hainan University(No.KYQD(ZR)-20077)。
文摘Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem.As the state of art 3D super-resolution localization algorithm based on deep learning,FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing,even though it has greatly improved the data processing throughput.In this paper,a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM.This new algorithm uses the feature compression method to reduce the parameters of the model,and combines it with pipeline programming to accelerate the inference process of the deep learning model.The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy,which can realize real-time processing of 256×256 pixels size images.The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering,and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.
基金supported by State Key Laboratory of HVDC under Grant SKLHVDC-2021-KF-09.
文摘The real-time risk-averse dispatch problem of an integrated electricity and natural gas system(IEGS)is studied in this paper.It is formulated as a real-time conditional value-at-risk(CVaR)-based risk-averse dis-patch model in the Markov decision process framework.Because of its stochasticity,nonconvexity and nonlinearity,the model is difficult to analyze by traditional algorithms in an acceptable time.To address this non-deterministic polynomial-hard problem,a CVaR-based lookup-table approximate dynamic programming(CVaR-ADP)algo-rithm is proposed,and the risk-averse dispatch problem is decoupled into a series of tractable subproblems.The line pack is used as the state variable to describe the impact of one period’s decision on the future.This facilitates the reduction of load shedding and wind power curtailment.Through the proposed method,real-time decisions can be made according to the current information,while the value functions can be used to overview the whole opti-mization horizon to balance the current cost and future risk loss.Numerical simulations indicate that the pro-posed method can effectively measure and control the risk costs in extreme scenarios.Moreover,the decisions can be made within 10 s,which meets the requirement of the real-time dispatch of an IEGS.Index Terms—Integrated electricity and natural gas system,approximate dynamic programming,real-time dispatch,risk-averse,conditional value-at-risk.
基金funded by China Scholarship Council(2020091-10135).
文摘Real-time rendering applications leverage heterogeneous computing to optimize performance.However,software development across multiple devices presents challenges,including data layout inconsistencies,synchronization issues,resource management complexities,and architectural disparities.Additionally,the creation of such systems requires verbose and unsafe programming models.Recent developments in domain-specific and unified shading languages aim to mitigate these issues.Yet,current programming models primarily address data layout consistency,neglecting other persistent challenges.In this paper,we introduce RenderKernel,a programming model designed to simplify the development of real-time rendering systems.Recognizing the need for a high-level approach,RenderKernel addresses the specific challenges of real-time rendering,enabling development on heterogeneous systems as if they were homogeneous.This model allows for early detection and prevention of errors due to system heterogeneity at compile-time.Furthermore,RenderKernel enables the use of common programming patterns from homogeneous environments,freeing developers from the complexities of underlying heterogeneous systems.Developers can focus on coding unique application features,thereby enhancing productivity and reducing the cognitive load associated with real-time rendering system development.
文摘Assessment of the current status of Lake Baikal proved to be based on changes in natural (“preindustrial”) chemical content in basic abiotic and biological compartments of the Lake geosystem. This approach was used to evaluate background “base-line levels” of 6 major and about 50 minor and trace ele-ments in the Lake Baikal water body using a number of most reliable data re-ported within 1992-2012. In terms of environment geochemistry Baikal is one of the purest water reservoirs on the Earth. A simple mass balance model was proposed for assessing possible anthropogenic impact on Baikal water geo-chemistry. Estimations of change trends showed that only for Na+, SO42-, Cl- and Mo growth rate of their average concentrations in the Lake occurred to be 1%, 3%, 7% and 2% in every 10 years. Space-time monitoring schedules for all water body compartments of the Lake are proposed as well as similar moni-toring programs for tributaries, precipitations, bottom sediments, aquatic biota.
基金国家自然科学基金(3077164430972263)Aid Program for Science and Technology Innovative Research Team in Higher Educational Instituions of Hunan Province
文摘At present, transcription analysis of gene expression commonly uses housekeeping genes as control for normalization. In this study, the expression levels of three housekeeping genes including GAPDH, β-actin, and 18S rRNA in six tissues and five developmental stages of the Mandarin fish Siniperca chuatsi were assayed with quantitative real-time PCR （qPCR）. Differences in expression levels were analyzed using geNorm program. The results demonstrate that β-actin is the most stable gene at developmental stages and GAPDH is the most stable in different tissues. While 18S rRNA expression during development is differentially regulated, which indicates it is suitable as an internal control for gene expression normalization at the developmental level. Overall, the data suggest that the two most stable housekeeping genes are enough to accurately calibrate gene expression in S. chuatsi. The significance of this study provided convincing references and methodology for housekeeping gene selection and normalization in gene expression analysis with regular PCR or qPCR.
