Neural organoids and confocal microscopy have the potential to play an important role in microconnectome research to understand neural patterns.We present PLayer,a plug-and-play embedded neural system,which demonstrat...Neural organoids and confocal microscopy have the potential to play an important role in microconnectome research to understand neural patterns.We present PLayer,a plug-and-play embedded neural system,which demonstrates the utilization of sparse confocal microscopy layers to interpolate continuous axial resolution.With an embedded system focused on neural network pruning,image scaling,and post-processing,PLayer achieves high-performance metrics with an average structural similarity index of 0.9217 and a peak signal-to-noise ratio of 27.75 dB,all within 20 s.This represents a significant time saving of 85.71%with simplified image processing.By harnessing statistical map estimation in interpolation and incorporating the Vision Transformer–based Restorer,PLayer ensures 2D layer consistency while mitigating heavy computational dependence.As such,PLayer can reconstruct 3D neural organoid confocal data continuously under limited computational power for the wide acceptance of fundamental connectomics and pattern-related research with embedded devices.展开更多
Machine learning potentials are promising in atomistic simulations due to their comparable accuracy to first-principles theory but much lower computational cost.However,the reliability,speed,and transferability of ato...Machine learning potentials are promising in atomistic simulations due to their comparable accuracy to first-principles theory but much lower computational cost.However,the reliability,speed,and transferability of atomistic machine learning potentials depend strongly on the way atomic configurations are represented.A wise choice of descriptors used as input for the machine learning program is the key for a successful machine learning representation.Here we develop a simple and efficient strategy to automatically select an optimal set of linearly-independent atomic features out of a large pool of candidates,based on the correlations that are intrinsic to the training data.Through applications to the construction of embedded atom neural network potentials for several benchmark molecules with less redundant linearly-independent embedded density descriptors,we demonstrate the efficiency and accuracy of this new strategy.The proposed algorithm can greatly simplify the initial selection of atomic features and vastly improve the performance of the atomistic machine learning potentials.展开更多
基金supported by the National Key R&D Program of China(Grant No.2021YFA1001000)the National Natural Science Foundation of China(Grant Nos.82111530212,U23A20282,and 61971255)+2 种基金the Natural Science Founda-tion of Guangdong Province(Grant No.2021B1515020092)the Shenzhen Bay Laboratory Fund(Grant No.SZBL2020090501014)the Shenzhen Science,Technology and Innovation Commission(Grant Nos.KJZD20231023094659002,JCYJ20220530142809022,and WDZC20220811170401001).
文摘Neural organoids and confocal microscopy have the potential to play an important role in microconnectome research to understand neural patterns.We present PLayer,a plug-and-play embedded neural system,which demonstrates the utilization of sparse confocal microscopy layers to interpolate continuous axial resolution.With an embedded system focused on neural network pruning,image scaling,and post-processing,PLayer achieves high-performance metrics with an average structural similarity index of 0.9217 and a peak signal-to-noise ratio of 27.75 dB,all within 20 s.This represents a significant time saving of 85.71%with simplified image processing.By harnessing statistical map estimation in interpolation and incorporating the Vision Transformer–based Restorer,PLayer ensures 2D layer consistency while mitigating heavy computational dependence.As such,PLayer can reconstruct 3D neural organoid confocal data continuously under limited computational power for the wide acceptance of fundamental connectomics and pattern-related research with embedded devices.
基金supported by CAS Project for Young Scientists in Basic Research(YSBR-005)the National Natural Science Foundation of China(No.22073089 and No.22033007)+1 种基金Anhui Initiative in Quantum Information Technologies(AHY090200)the Fundamental Research Funds for Central Universities(WK2060000017)。
文摘Machine learning potentials are promising in atomistic simulations due to their comparable accuracy to first-principles theory but much lower computational cost.However,the reliability,speed,and transferability of atomistic machine learning potentials depend strongly on the way atomic configurations are represented.A wise choice of descriptors used as input for the machine learning program is the key for a successful machine learning representation.Here we develop a simple and efficient strategy to automatically select an optimal set of linearly-independent atomic features out of a large pool of candidates,based on the correlations that are intrinsic to the training data.Through applications to the construction of embedded atom neural network potentials for several benchmark molecules with less redundant linearly-independent embedded density descriptors,we demonstrate the efficiency and accuracy of this new strategy.The proposed algorithm can greatly simplify the initial selection of atomic features and vastly improve the performance of the atomistic machine learning potentials.
