We designed the window function of the optimal Gabor transform based on the time-frequency rotation property of the fractional Fourier transform. Thus, we obtained the adaptive optimal Gabor transform in the fractiona...We designed the window function of the optimal Gabor transform based on the time-frequency rotation property of the fractional Fourier transform. Thus, we obtained the adaptive optimal Gabor transform in the fractional domain and improved the time-frequency concentration of the Gabor transform. The algorithm first searches for the optimal rotation factor, then performs the p-th FrFT of the signal and, finally, performs time and frequency analysis of the FrFT result. Finally, the algorithm rotates the plane in the fractional domain back to the normal time-frequency plane. This promotes the application of FrFT in the field of high-resolution reservoir prediction. Additionally, we proposed an adaptive search method for the optimal rotation factor using the Parseval principle in the fractional domain, which simplifies the algorithm. We carried out spectrum decomposition of the seismic signal, which showed that the instantaneous frequency slices obtained by the proposed algorithm are superior to the ones obtained by the traditional Gabor transform. The adaptive time frequency analysis is of great significance to seismic signal processing.展开更多
In this paper,an algorithm based on a fractional time-frequency spectrum feature is proposed to improve the accuracy of synthetic aperture radar(SAR)target detection.By extending the fractional Gabor transform(FrGT)in...In this paper,an algorithm based on a fractional time-frequency spectrum feature is proposed to improve the accuracy of synthetic aperture radar(SAR)target detection.By extending the fractional Gabor transform(FrGT)into two dimensions,the fractional time-frequency spectrum feature of an image can be obtained.In the achievement process,we search for the optimal order and design the optimal window function to accomplish the two-dimensional optimal FrGT.Finally,the energy attenuation gradient(EAG)feature of the optimal time-frequency spectrum is extracted for high-frequency detection.The simulation results show the proposed algorithm has a good performance in SAR target detection and lays the foundation for recognition.展开更多
With the booming development of the digital economy,integrated media marketing has become an important means for enterprises to promote products and services.This study aims to explore the role and optimization strate...With the booming development of the digital economy,integrated media marketing has become an important means for enterprises to promote products and services.This study aims to explore the role and optimization strategies of integrated media marketing in the transformation of digital financial products,with a particular focus on how new media audiovisual works can empower digital financial products and promote their development towards more efficient and personalized directions.展开更多
The paper discusses how to reach the equilibrium and optimization GI during the period of economic transformation. The market economy might not work because of its mechanism flaws, based on the assumption that the gov...The paper discusses how to reach the equilibrium and optimization GI during the period of economic transformation. The market economy might not work because of its mechanism flaws, based on the assumption that the government is the supplier and the market economy is the demander Of GI, there is an equilibrium and optimization issue. The theory suggests that GI could reach equilibrium through adjusting the government revenue, thus leads to the result of functional complement between the market economy and the GI, and the optimum economic efficiency.展开更多
Corynebacterium stationis,a high-GC Gram-positive bacterium with significant industrial potential,has faced limitations due to the lack of efficient genetic tools.In this study,we developed a CRISPR/Cas9-based genome ...Corynebacterium stationis,a high-GC Gram-positive bacterium with significant industrial potential,has faced limitations due to the lack of efficient genetic tools.In this study,we developed a CRISPR/Cas9-based genome editing platform specifically tailored for C.stationis.First,electroporation efficiency was optimized to 1.81±0.16×10^(5) CFU(colony forming units)/μg plasmid DNA through medium selection,pulse parameter adjustments(2.5 kV,2 pulses),and concentration optimization of cell wall-weakening agents(3.0%glycine,0.25%isoni-azid).Three functional shuttle vectors(p99E-pCG1,p19-Kan,p19-Spe)were constructed,enabling stable het-erologous gene expression.By engineering a tightly regulated Cas9 expression system(Plac promoter with dual LacO*operators),we achieved high-efficiency genome editing,with deletion efficiencies of 81.2-98.6%for 1.7-50 kb fragments and insertion efficiencies of 27.5-65.2%for 1-5 kb fragments.CRISPR/Cas9-assisted ssDNA recombineering facilitated single/triple nucleotide changes with>90%efficiency.Applying this toolbox,we engineered C.stationis for hypoxanthine biosynthesis by combining purA deletion with integration of heterologous feedback-resistant prs^(D128A) and endogenous purF deregulation(purF^(K334Q)),achieving a titer of 0.047 g/L.This study establishes a robust genetic platform for C.stationis,accelerating its industrial application in the production of biochemicals and biofuels.展开更多
Transformer models have become a cornerstone of various natural language processing(NLP)tasks.However,the substantial computational overhead during the inference remains a significant challenge,limiting their deployme...Transformer models have become a cornerstone of various natural language processing(NLP)tasks.However,the substantial computational overhead during the inference remains a significant challenge,limiting their deployment in practical applications.In this study,we address this challenge by minimizing the inference overhead in transformer models using the controlling element on artificial intelligence(AI)accelerators.Our work is anchored by four key contributions.First,we conduct a comprehensive analysis of the overhead composition within the transformer inference process,identifying the primary bottlenecks.Second,we leverage the management processing element(MPE)of the Shenwei AI(SWAI)accelerator,implementing a three-tier scheduling framework that significantly reduces the number of host-device launches to approximately 1/10000 of the original PyTorch-GPU setup.Third,we introduce a zero-copy memory management technique using segment-page fusion,which significantly reduces memory access latency and improves overall inference efficiency.Finally,we develop a fast model loading method that eliminates redundant computations during model verification and initialization,reducing the total loading time for large models from 22128.31 ms to 1041.72 ms.Our contributions significantly enhance the optimization of transformer models,enabling more efficient and expedited inference processes on AI accelerators.展开更多
基金supported by national natural science foundation of China(No.41274127,41301460,40874066,and 40839905)
文摘We designed the window function of the optimal Gabor transform based on the time-frequency rotation property of the fractional Fourier transform. Thus, we obtained the adaptive optimal Gabor transform in the fractional domain and improved the time-frequency concentration of the Gabor transform. The algorithm first searches for the optimal rotation factor, then performs the p-th FrFT of the signal and, finally, performs time and frequency analysis of the FrFT result. Finally, the algorithm rotates the plane in the fractional domain back to the normal time-frequency plane. This promotes the application of FrFT in the field of high-resolution reservoir prediction. Additionally, we proposed an adaptive search method for the optimal rotation factor using the Parseval principle in the fractional domain, which simplifies the algorithm. We carried out spectrum decomposition of the seismic signal, which showed that the instantaneous frequency slices obtained by the proposed algorithm are superior to the ones obtained by the traditional Gabor transform. The adaptive time frequency analysis is of great significance to seismic signal processing.
基金supported by the Natural Science Foundation of Sichuan Province of China under Grant No.2022NSFSC40574partially supported by the National Natural Science Foundation of China under Grants No.61571096 and No.61775030.
文摘In this paper,an algorithm based on a fractional time-frequency spectrum feature is proposed to improve the accuracy of synthetic aperture radar(SAR)target detection.By extending the fractional Gabor transform(FrGT)into two dimensions,the fractional time-frequency spectrum feature of an image can be obtained.In the achievement process,we search for the optimal order and design the optimal window function to accomplish the two-dimensional optimal FrGT.Finally,the energy attenuation gradient(EAG)feature of the optimal time-frequency spectrum is extracted for high-frequency detection.The simulation results show the proposed algorithm has a good performance in SAR target detection and lays the foundation for recognition.
文摘With the booming development of the digital economy,integrated media marketing has become an important means for enterprises to promote products and services.This study aims to explore the role and optimization strategies of integrated media marketing in the transformation of digital financial products,with a particular focus on how new media audiovisual works can empower digital financial products and promote their development towards more efficient and personalized directions.
文摘The paper discusses how to reach the equilibrium and optimization GI during the period of economic transformation. The market economy might not work because of its mechanism flaws, based on the assumption that the government is the supplier and the market economy is the demander Of GI, there is an equilibrium and optimization issue. The theory suggests that GI could reach equilibrium through adjusting the government revenue, thus leads to the result of functional complement between the market economy and the GI, and the optimum economic efficiency.
基金support.This work was supported by Guangdong S&T Program(2024B1111150001).
文摘Corynebacterium stationis,a high-GC Gram-positive bacterium with significant industrial potential,has faced limitations due to the lack of efficient genetic tools.In this study,we developed a CRISPR/Cas9-based genome editing platform specifically tailored for C.stationis.First,electroporation efficiency was optimized to 1.81±0.16×10^(5) CFU(colony forming units)/μg plasmid DNA through medium selection,pulse parameter adjustments(2.5 kV,2 pulses),and concentration optimization of cell wall-weakening agents(3.0%glycine,0.25%isoni-azid).Three functional shuttle vectors(p99E-pCG1,p19-Kan,p19-Spe)were constructed,enabling stable het-erologous gene expression.By engineering a tightly regulated Cas9 expression system(Plac promoter with dual LacO*operators),we achieved high-efficiency genome editing,with deletion efficiencies of 81.2-98.6%for 1.7-50 kb fragments and insertion efficiencies of 27.5-65.2%for 1-5 kb fragments.CRISPR/Cas9-assisted ssDNA recombineering facilitated single/triple nucleotide changes with>90%efficiency.Applying this toolbox,we engineered C.stationis for hypoxanthine biosynthesis by combining purA deletion with integration of heterologous feedback-resistant prs^(D128A) and endogenous purF deregulation(purF^(K334Q)),achieving a titer of 0.047 g/L.This study establishes a robust genetic platform for C.stationis,accelerating its industrial application in the production of biochemicals and biofuels.
文摘Transformer models have become a cornerstone of various natural language processing(NLP)tasks.However,the substantial computational overhead during the inference remains a significant challenge,limiting their deployment in practical applications.In this study,we address this challenge by minimizing the inference overhead in transformer models using the controlling element on artificial intelligence(AI)accelerators.Our work is anchored by four key contributions.First,we conduct a comprehensive analysis of the overhead composition within the transformer inference process,identifying the primary bottlenecks.Second,we leverage the management processing element(MPE)of the Shenwei AI(SWAI)accelerator,implementing a three-tier scheduling framework that significantly reduces the number of host-device launches to approximately 1/10000 of the original PyTorch-GPU setup.Third,we introduce a zero-copy memory management technique using segment-page fusion,which significantly reduces memory access latency and improves overall inference efficiency.Finally,we develop a fast model loading method that eliminates redundant computations during model verification and initialization,reducing the total loading time for large models from 22128.31 ms to 1041.72 ms.Our contributions significantly enhance the optimization of transformer models,enabling more efficient and expedited inference processes on AI accelerators.
