To deal with automated reasoning of linguistic truth-valued lattice-valued logic system, a lattice implication algebra with 14 elements, L14, was defined, and the J-resolution fields of constants, propositional variab...To deal with automated reasoning of linguistic truth-valued lattice-valued logic system, a lattice implication algebra with 14 elements, L14, was defined, and the J-resolution fields of constants, propositional variables and some generalized literals of L14P（X）, which is a lattice-valued propositional logic system with truth-values in L14, were discussed. There are 4 filters in L14. For any constant a not belonging to J, a and g （generalized literal of L14P（X）） form a J-resolution pair. For a propositional variable x, if x belongs to J and g does not belong to J, then x and g form a J-resolution pair.展开更多
The CUG_CLMFM3D series comprises high-resolution three-dimensional lithospheric magnetic field models for China and its surroundings.The first version,CUG_CLMFM3Dv1,is a spherical cap harmonic model integrating the WD...The CUG_CLMFM3D series comprises high-resolution three-dimensional lithospheric magnetic field models for China and its surroundings.The first version,CUG_CLMFM3Dv1,is a spherical cap harmonic model integrating the WDMAMv2(World Digital Magnetic Anomaly Map version 2)global magnetic anomaly grid and nearly a decade of CHAMP(Challenging Minisatellite Payload for Geophysical Research and Application)satellite vector data.It achieves a~5.7 km resolution but has limitations:the WDMAMv2 grid lacks high-resolution data in the southern Xinjiang and Tibet regions,which leads to missing small-to medium-scale anomalies,and unfiltered CHAMP data introduce low-frequency conflicts with global spherical harmonic models.Above the altitude of 150 km,correlations with global models drop below 0.9.The second version,CUG_CLMFM3Dv2,addresses these issues by incorporating 5-km-resolution aeromagnetic data and rigorously processed satellite data from CHAMP,Swarm,CSES-1(China Seismo-Electromagnetic Satellite 1),and MSS-1(Macao Science Satellite 1).The comparison analysis shows that the CUG_CLMFM3Dv2 captures finer high-frequency details and more stable long-wavelength signals,offering improved magnetic anomaly maps for further geological and geophysical studies.展开更多
The New Vacuum Solar Telescope （NVST） is a one meter vacuum solar telescope that aims to observe fine structures on the Sun. The main goals of NVST are high resolution imaging and spectral observations, including me...The New Vacuum Solar Telescope （NVST） is a one meter vacuum solar telescope that aims to observe fine structures on the Sun. The main goals of NVST are high resolution imaging and spectral observations, including measurements of the solar magnetic field. NVST is the primary ground-based facility used by the Chinese solar research community in this solar cycle. It is located by Fuxian Lake in southwest China, where the seeing is good enough to perform high resolution observations. We first introduce the general conditions at the Fuxian Solar Observatory and the primary science cases of NVST. Then, the basic structures of this telescope and instruments are described in detail. Finally, some typical high resolution data of the solar photosphere and chromosphere are also shown.展开更多
Light-field imaging has wide applications in various domains,including microscale life science imaging,mesoscale neuroimaging,and macroscale fluid dynamics imaging.The development of deep learning-based reconstruction...Light-field imaging has wide applications in various domains,including microscale life science imaging,mesoscale neuroimaging,and macroscale fluid dynamics imaging.The development of deep learning-based reconstruction methods has greatly facilitated high-resolution light-field image processing,however,current deep learning-based light-field reconstruction methods have predominantly concentrated on the microscale.Considering the multiscale imaging capacity of light-field technique,a network that can work over variant scales of light-field image reconstruction will significantly benefit the development of volumetric imaging.Unfortunately,to our knowledge,no one has reported a universal high-resolution light-field image reconstruction algorithm that is compatible with microscale,mesoscale,and macroscale.To fill this gap,we present a real-time and universal network(RTU-Net)to reconstruct high-resolution light-field images at any scale.RTU-Net,as the first network that works over multiscale light-field image reconstruction,employs an adaptive loss function based on generative adversarial theory and consequently exhibits strong generalization capability.We comprehensively assessed the performance of RTU-Net through the reconstruction of multiscale light-field images,including microscale tubulin and mitochondrion dataset,mesoscale synthetic mouse neuro dataset,and macroscale light-field particle imaging velocimetry dataset.The results indicated that RTU-Net has achieved real-time and high-resolution light-field image reconstruction for volume sizes ranging from 300μm×300μm×12μm to 25 mm×25 mm×25 mm,and demonstrated higher resolution when compared with recently reported light-field reconstruction networks.The high-resolution,strong robustness,high efficiency,and especially the general applicability of RTU-Net will significantly deepen our insight into high-resolution and volumetric imaging.展开更多
基金The Nationl Natural Science Foundation of China (No.60474022)
文摘To deal with automated reasoning of linguistic truth-valued lattice-valued logic system, a lattice implication algebra with 14 elements, L14, was defined, and the J-resolution fields of constants, propositional variables and some generalized literals of L14P（X）, which is a lattice-valued propositional logic system with truth-values in L14, were discussed. There are 4 filters in L14. For any constant a not belonging to J, a and g （generalized literal of L14P（X）） form a J-resolution pair. For a propositional variable x, if x belongs to J and g does not belong to J, then x and g form a J-resolution pair.
基金supported by the National Natural Science Foundation of China(Grant Nos.42250103,42174090,42250101,42250102,and 41774091)the Macao Foundation+1 种基金the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(Grant No.GLAB2023ZR02)the MOST Special Fund from the State Key Laboratory of Geological Processes and Mineral Resources(Grant No.MSFGPMR2022-4)。
文摘The CUG_CLMFM3D series comprises high-resolution three-dimensional lithospheric magnetic field models for China and its surroundings.The first version,CUG_CLMFM3Dv1,is a spherical cap harmonic model integrating the WDMAMv2(World Digital Magnetic Anomaly Map version 2)global magnetic anomaly grid and nearly a decade of CHAMP(Challenging Minisatellite Payload for Geophysical Research and Application)satellite vector data.It achieves a~5.7 km resolution but has limitations:the WDMAMv2 grid lacks high-resolution data in the southern Xinjiang and Tibet regions,which leads to missing small-to medium-scale anomalies,and unfiltered CHAMP data introduce low-frequency conflicts with global spherical harmonic models.Above the altitude of 150 km,correlations with global models drop below 0.9.The second version,CUG_CLMFM3Dv2,addresses these issues by incorporating 5-km-resolution aeromagnetic data and rigorously processed satellite data from CHAMP,Swarm,CSES-1(China Seismo-Electromagnetic Satellite 1),and MSS-1(Macao Science Satellite 1).The comparison analysis shows that the CUG_CLMFM3Dv2 captures finer high-frequency details and more stable long-wavelength signals,offering improved magnetic anomaly maps for further geological and geophysical studies.
基金Supported by the National Natural Science Foundation of China
文摘The New Vacuum Solar Telescope （NVST） is a one meter vacuum solar telescope that aims to observe fine structures on the Sun. The main goals of NVST are high resolution imaging and spectral observations, including measurements of the solar magnetic field. NVST is the primary ground-based facility used by the Chinese solar research community in this solar cycle. It is located by Fuxian Lake in southwest China, where the seeing is good enough to perform high resolution observations. We first introduce the general conditions at the Fuxian Solar Observatory and the primary science cases of NVST. Then, the basic structures of this telescope and instruments are described in detail. Finally, some typical high resolution data of the solar photosphere and chromosphere are also shown.
基金supported by National Natural Science Foundation of China(12402336,82201637,U20A2070,and 12025202)National High-Level Talent Project(YQR23069)+6 种基金Natural Science Foundation of Jiangsu Province(BK20230876)the Young Elite Scientist Sponsorship Program by CAST(YESS20210238)Forwardlooking layout projects(1002-ILB24009)Zhejang Provincial Medical and Health Technology Project(Grant No.2024KY246,2025KY180)Scientific Research Foundation of Hangzhou City University(No.J-202402)Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence,Zhejiang University(Grant No.BMI2400025)Hangzhou Science and Technology Bureau.
文摘Light-field imaging has wide applications in various domains,including microscale life science imaging,mesoscale neuroimaging,and macroscale fluid dynamics imaging.The development of deep learning-based reconstruction methods has greatly facilitated high-resolution light-field image processing,however,current deep learning-based light-field reconstruction methods have predominantly concentrated on the microscale.Considering the multiscale imaging capacity of light-field technique,a network that can work over variant scales of light-field image reconstruction will significantly benefit the development of volumetric imaging.Unfortunately,to our knowledge,no one has reported a universal high-resolution light-field image reconstruction algorithm that is compatible with microscale,mesoscale,and macroscale.To fill this gap,we present a real-time and universal network(RTU-Net)to reconstruct high-resolution light-field images at any scale.RTU-Net,as the first network that works over multiscale light-field image reconstruction,employs an adaptive loss function based on generative adversarial theory and consequently exhibits strong generalization capability.We comprehensively assessed the performance of RTU-Net through the reconstruction of multiscale light-field images,including microscale tubulin and mitochondrion dataset,mesoscale synthetic mouse neuro dataset,and macroscale light-field particle imaging velocimetry dataset.The results indicated that RTU-Net has achieved real-time and high-resolution light-field image reconstruction for volume sizes ranging from 300μm×300μm×12μm to 25 mm×25 mm×25 mm,and demonstrated higher resolution when compared with recently reported light-field reconstruction networks.The high-resolution,strong robustness,high efficiency,and especially the general applicability of RTU-Net will significantly deepen our insight into high-resolution and volumetric imaging.
