This contribution describes the synthesis and structural characterization of a triaminoguanidinium (TAG)-based ligand [H6(OMe)3Limin]BF4 (1) containing imine bonds (Limin) and its reduction with a dimethylamino borane...This contribution describes the synthesis and structural characterization of a triaminoguanidinium (TAG)-based ligand [H6(OMe)3Limin]BF4 (1) containing imine bonds (Limin) and its reduction with a dimethylamino borane complex to the corresponding amine compound (Lamin)[H9(OMe)3Lamin]OTs (2). In solution, both ligands are C3-symmetric but crystal structures show the great influence of the reduction on the molecular structure. We show that the planar imine ligand is converted to a highly flexible compound which has nine potential coordination sites, three phenoxy and six amine donors, for binding metal ions. First solid state structures of 1:1 (metal:ligand) coordination compounds with SnIV and ZrIV are presented. SnIV exhibits an octahedral coordination sphere and is bound in a facial [ONN] coordination pocket. ZrIV is pentagonal bipyramidal coordinated and the ligand stabilizes this with its’ [ONNO] binding sites.展开更多
Neural networks(NNs),especially electronic-based NNs,have been rapidly developed in the past few decades.However,the electronic-based NNs rely more on highly advanced and heavy power-consuming hardware,facing its bott...Neural networks(NNs),especially electronic-based NNs,have been rapidly developed in the past few decades.However,the electronic-based NNs rely more on highly advanced and heavy power-consuming hardware,facing its bottleneck due to the slowdown of Moore's law.Optical neural networks(ONNs),in which NNs are realized via optical components with information carried by photons at the speed of light,are drawing more attention nowadays.Despite the advantages of higher processing speed and lower system power consumption,one major challenge is to realize reliable and reusable algorithms in physical approaches,particularly nonlinear functions,for higher accuracy.In this paper,a versatile parametric-process-based ONN is demonstrated with its adaptable nonlinear computation realized using the highly nonlinear fiber(HNLF).With the specially designed modelocked laser(MLL)and dispersive Fourier transform(DFT)algorithm,the overall computation frame rate can reach up to 40 MHz.Compared to ONNs using only linear computations,this system is able to improve the classification accuracies from 81.8%to 88.8%for the MNIST-digit dataset,and from 80.3%to 97.6%for the Vowel spoken audio dataset,without any hardware modifications.展开更多
The rapid development of optical neural networks(ONNs)has led to the introduction of new research avenues for computing power enhancement.Because of the characteristics of optical signals,which include low power consu...The rapid development of optical neural networks(ONNs)has led to the introduction of new research avenues for computing power enhancement.Because of the characteristics of optical signals,which include low power consumption,low latency,high parallelism,and large bandwidths,optical computing based on neural network architectures is showing promise for processing of spatial signals,temporal signals,and on-chip information.At present,there is a lack of a unified ONN computing architecture,and because of the limitations of the physical characteristics of these networks,different application scenarios have led to proposals of different requirements for the structural design,device selection,integration method,and signal processing method of the network.In this paper,we systematically elaborate on the practical value of ONNs,analyze their computational fundamentals in depth,discuss the challenges faced in computational and astrophotonics applications in detail,and simultaneously emphasize the important position and broad prospects of optical computing in the future information society.展开更多
Brain cancer is the premier reason for cancer deaths all over the world.The diagnosis of brain cancer at an initial stage is mediocre,as the radiologist is ineffectual.Different experiments have been conducted and dem...Brain cancer is the premier reason for cancer deaths all over the world.The diagnosis of brain cancer at an initial stage is mediocre,as the radiologist is ineffectual.Different experiments have been conducted and demonstrated clearly that the algorithms for nodule segmentation are unsuccessful.Therefore,the research has consolidated incremental clustering focused on superpixel segmentation as an appropriate optimization approach for the accurate segmentation of pulmonary nodules.The key aim of the research is to refine brain CT images to accurately distinguish tumors and the segmentation of small-scale anomalous nodules in the brain region.In the beginning stage,an anisotropic diffusion filters(ADF)method with un-sharp intensification masking is utilized to eliminate the noise discernment in images.In the following stage,within the improved nodule image sequence,a Superpixel Segmentation Based Iterative Clustering(SSBIC)algorithm is proposed for irregular brain tissue prediction.Subsequently,the brain nodule samples are captured using deep learning methods:Advanced Grey Wolf Optimization(AGWO)with ONN(AGWO-ONN)and Advanced GWO with CNN-based(AGWOCNN).The proposed technique indicates that the sensitivity is increased and the calculation time is decreased.Consequently,the proposed methodology manifests that the advanced Computer-Assisted Diagnosis(CAD)system has outstanding potential for automatic brain tumor diagnosis.The average segmentation time of the nodule slice order is 1.06s,and 97%of AGWO-ONN and 97.6%of AGWO-CNN achieve the best classification reliability.展开更多
文摘This contribution describes the synthesis and structural characterization of a triaminoguanidinium (TAG)-based ligand [H6(OMe)3Limin]BF4 (1) containing imine bonds (Limin) and its reduction with a dimethylamino borane complex to the corresponding amine compound (Lamin)[H9(OMe)3Lamin]OTs (2). In solution, both ligands are C3-symmetric but crystal structures show the great influence of the reduction on the molecular structure. We show that the planar imine ligand is converted to a highly flexible compound which has nine potential coordination sites, three phenoxy and six amine donors, for binding metal ions. First solid state structures of 1:1 (metal:ligand) coordination compounds with SnIV and ZrIV are presented. SnIV exhibits an octahedral coordination sphere and is bound in a facial [ONN] coordination pocket. ZrIV is pentagonal bipyramidal coordinated and the ligand stabilizes this with its’ [ONNO] binding sites.
基金Research Grants Council of the Hong Kong Special Administrative Region of China(HKU 17212824,HKU 17210522,HKU C7074-21G,HKU R7003-21,HKU 17205321)Innovation and Technology Commission—Hong Kong(MHP/073/20,MHP/057/21,Health@Inno HK programc)。
文摘Neural networks(NNs),especially electronic-based NNs,have been rapidly developed in the past few decades.However,the electronic-based NNs rely more on highly advanced and heavy power-consuming hardware,facing its bottleneck due to the slowdown of Moore's law.Optical neural networks(ONNs),in which NNs are realized via optical components with information carried by photons at the speed of light,are drawing more attention nowadays.Despite the advantages of higher processing speed and lower system power consumption,one major challenge is to realize reliable and reusable algorithms in physical approaches,particularly nonlinear functions,for higher accuracy.In this paper,a versatile parametric-process-based ONN is demonstrated with its adaptable nonlinear computation realized using the highly nonlinear fiber(HNLF).With the specially designed modelocked laser(MLL)and dispersive Fourier transform(DFT)algorithm,the overall computation frame rate can reach up to 40 MHz.Compared to ONNs using only linear computations,this system is able to improve the classification accuracies from 81.8%to 88.8%for the MNIST-digit dataset,and from 80.3%to 97.6%for the Vowel spoken audio dataset,without any hardware modifications.
基金supported by the National Key R&D Program of China(2021YFC2202100)the National Natural Science Foundation of China(12473090).
文摘The rapid development of optical neural networks(ONNs)has led to the introduction of new research avenues for computing power enhancement.Because of the characteristics of optical signals,which include low power consumption,low latency,high parallelism,and large bandwidths,optical computing based on neural network architectures is showing promise for processing of spatial signals,temporal signals,and on-chip information.At present,there is a lack of a unified ONN computing architecture,and because of the limitations of the physical characteristics of these networks,different application scenarios have led to proposals of different requirements for the structural design,device selection,integration method,and signal processing method of the network.In this paper,we systematically elaborate on the practical value of ONNs,analyze their computational fundamentals in depth,discuss the challenges faced in computational and astrophotonics applications in detail,and simultaneously emphasize the important position and broad prospects of optical computing in the future information society.
文摘Brain cancer is the premier reason for cancer deaths all over the world.The diagnosis of brain cancer at an initial stage is mediocre,as the radiologist is ineffectual.Different experiments have been conducted and demonstrated clearly that the algorithms for nodule segmentation are unsuccessful.Therefore,the research has consolidated incremental clustering focused on superpixel segmentation as an appropriate optimization approach for the accurate segmentation of pulmonary nodules.The key aim of the research is to refine brain CT images to accurately distinguish tumors and the segmentation of small-scale anomalous nodules in the brain region.In the beginning stage,an anisotropic diffusion filters(ADF)method with un-sharp intensification masking is utilized to eliminate the noise discernment in images.In the following stage,within the improved nodule image sequence,a Superpixel Segmentation Based Iterative Clustering(SSBIC)algorithm is proposed for irregular brain tissue prediction.Subsequently,the brain nodule samples are captured using deep learning methods:Advanced Grey Wolf Optimization(AGWO)with ONN(AGWO-ONN)and Advanced GWO with CNN-based(AGWOCNN).The proposed technique indicates that the sensitivity is increased and the calculation time is decreased.Consequently,the proposed methodology manifests that the advanced Computer-Assisted Diagnosis(CAD)system has outstanding potential for automatic brain tumor diagnosis.The average segmentation time of the nodule slice order is 1.06s,and 97%of AGWO-ONN and 97.6%of AGWO-CNN achieve the best classification reliability.
