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
Human brain organoids are 3-dimensional brain-like tissues derived from human pluripotent stem cells and hold promising potential for modeling neurological,psychiatric,and developmental disorders.While the molecular a...Human brain organoids are 3-dimensional brain-like tissues derived from human pluripotent stem cells and hold promising potential for modeling neurological,psychiatric,and developmental disorders.While the molecular and cellular aspects of human brain organoids have been intensively studied,their functional properties such as organoid neural networks(ONNs)are largely understudied.Here,we summarize recent research advances in understanding,characterization,and application of functional ONNs in human brain organoids.We first discuss the formation of ONNs and follow up with characterization strategies including microelectrode array(MEA)technology and calcium imaging.Moreover,we highlight recent studies utilizing ONNs to investigate neurological diseases such as Rett syndrome and Alzheimer’s disease.Finally,we provide our perspectives on the future challenges and opportunities for using ONNs in basic research and translational applications.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
Ports and jetties complex operations come with various forms of pollutions. The analysis of marine pollution from ports becomes very necessary and complicated due to the various types of pollution, sources, effects an...Ports and jetties complex operations come with various forms of pollutions. The analysis of marine pollution from ports becomes very necessary and complicated due to the various types of pollution, sources, effects and different characteristics. The sources of environmental pollution other than ships and from industrial activities in port and jetties were critically looked at and analyzed. A complete review of the environmental pollution in ports and the tools to assess and minimize such negative environmental impact are analyzed. The instrument of questionnaires was employed and distributed among two seaports and one jetty;Onne, Okrika and Port Harcourt to collect respondents’ opinions on effects, sources and causes of marine pollution. The chi-square test for independence was used with 180 respondents from Onne port, Port Harcourt port and Okrika jetty. Water sample was collected from Onne seaport and pollution contents such as total petroleum hydrocarbon (TPH), bio-chemical oxygen demand (BOD), turbidity, pH and salinity were tested in the laboratory. The result shows that Onne water had a salinity level of 20,790 (mg/l) which under the salinity range of water is considered saline, a turbidity level of 4.00 (NTU) which was considered average comparing with a 5.00 (NTU) bench mark, BOD5 level of 0.48 (mg/l) which was considered pristine because most pristine seawater will have BOD below 1 (mg/l), pH level of 7.77 which falls under the range of sea water being alkaline (7.2 - 8.4), TPH level of 2.98 (mg/l) since all conditions of sampling and sample preservations were observed and the value is less than the DPR limit (10 mg/l). It was concluded that the activities in Onne port are within the acceptable limits. It was also observed from the questionnaire that a larger population of respondents in Onne, Okrika and Port Harcourt ports where conscious of the sources and effects of environmental pollution from their respective ports.展开更多
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
Deep learning has rapidly advanced amidst the proliferation of large models,leading to challenges in computational resources and power consumption.Optical neural networks(ONNs)offer a solution by shifting computation ...Deep learning has rapidly advanced amidst the proliferation of large models,leading to challenges in computational resources and power consumption.Optical neural networks(ONNs)offer a solution by shifting computation to optics,thereby leveraging the benefits of low power consumption,low latency,and high parallelism.The current training paradigm for ONNs primarily relies on backpropagation(BP).However,the reliance is incompatible with potential unknown processes within the system,which necessitates detailed knowledge and precise mathematical modeling of the optical process.In this paper,we present a pre-sensor multilayer ONN with nonlinear activation,utilizing a forward-forward algorithm to directly train both optical and digital parameters,which replaces the traditional backward pass with an additional forward pass.Our proposed nonlinear optical system demonstrates significant improvements in image classification accuracy,achieving a maximum enhancement of 9.0%.It also validates the efficacy of training parameters in the presence of unknown nonlinear components in the optical system.The proposed training method addresses the limitations of BP,paving the way for applications with a broader range of physical transformations in ONNs.展开更多
Optical neural networks(ONNs)are a class of emerging computing platforms that leverage the properties of light to perform ultra-fast computations with ultra-low energy consumption.ONNs often use CCD cameras as the out...Optical neural networks(ONNs)are a class of emerging computing platforms that leverage the properties of light to perform ultra-fast computations with ultra-low energy consumption.ONNs often use CCD cameras as the output layer.In this work,we propose the use of perovskite solar cells as a promising alternative to imaging cameras in ONN designs.Solar cells are ubiquitous,versatile,highly customizable,and can be fabricated quickly in laboratories.Their large acquisition area and outstanding efficiency enable them to generate output signals with a large dynamic range without the need for amplification.Here we have experimentally demonstrated the feasibility of using perovskite solar cells for capturing ONN output states,as well as the capability of single-layer random ONNs to achieve excellent performance even with a very limited number of pixels.Our results show that the solar-cell-based ONN setup consistently outperforms the same setup with CCD cameras of the same resolution.These findings highlight the potential of solar-cell-based ONNs as an ideal choice for automated and battery-free edge-computing applications.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 growth of deep learning applications has sparked a revolution in computing paradigms,with optical neural networks(ONNs)emerging as a promising platform for achieving ultra-high computing power and energy eff...The rapid growth of deep learning applications has sparked a revolution in computing paradigms,with optical neural networks(ONNs)emerging as a promising platform for achieving ultra-high computing power and energy efficiency.Despite great progress in analog optical computing,the lack of scalable optical nonlinearities and losses in photonic devices pose considerable challenges for power levels,energy efficiency,and signal latency.Here,we report an end-to-end all-optical nonlinear activator that utilizes the energy conversion of Brillouin scattering to perform efficient nonlinear processing.The activator exhibits an ultra-low activation threshold(24 nW),a wide transmission bandwidth(over 40 GHz),strong robustness,and high energy transfer efficiency.These advantages provide a feasible solution to overcome the existing bottlenecks in ONNs.As a proof-of-concept,a series of tasks is designed to validate the capability of the proposed activator as an activation unit for ONNs.Simulations show that the experiment-based nonlinear model outperforms classical activation functions in classification(97.64%accuracy for MNIST and 87.84%for Fashion-MNIST)and regression(with a symbol error rate as low as 0%)tasks.This work provides valuable insights into the innovative design of all-optical neural networks.展开更多
The oxovanadium（IV） complexes of the Schiff base hydrazones, synthesized from 3-hydrazinoquinoxaline-2- one （HQO） with salicylaldehyde （HSHQO）, o-hydroxyacetophenone （HHAHQO）, dehydroacetic acid （HDHAHQO） an...The oxovanadium（IV） complexes of the Schiff base hydrazones, synthesized from 3-hydrazinoquinoxaline-2- one （HQO） with salicylaldehyde （HSHQO）, o-hydroxyacetophenone （HHAHQO）, dehydroacetic acid （HDHAHQO） and o-nitrobenzaldehyde （NBHQO） were synthesized and characterized on the basis of analytical, conductance, magnetic moment, infrared, NMR, ESR and electronic spectral data. The ligands HSHQO, HDHAHQO behaved as monobasic tridentate ONN donors through phenolic oxygen, azomethine nitrogens. The ligand HAHQO acted as a monobasic bidentate ON donor through the phenolic oxygen, azomethine （free） nitrogen and the ligand NBHQO acted as neutral bidentate ON donor through oxygen of the nitro group and azomethine （free） nitrogen.展开更多
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.
基金supported by the National Institutes of Health(awards DP2AI160242 and U01DA056242).
文摘Human brain organoids are 3-dimensional brain-like tissues derived from human pluripotent stem cells and hold promising potential for modeling neurological,psychiatric,and developmental disorders.While the molecular and cellular aspects of human brain organoids have been intensively studied,their functional properties such as organoid neural networks(ONNs)are largely understudied.Here,we summarize recent research advances in understanding,characterization,and application of functional ONNs in human brain organoids.We first discuss the formation of ONNs and follow up with characterization strategies including microelectrode array(MEA)technology and calcium imaging.Moreover,we highlight recent studies utilizing ONNs to investigate neurological diseases such as Rett syndrome and Alzheimer’s disease.Finally,we provide our perspectives on the future challenges and opportunities for using ONNs in basic research and translational applications.
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.
文摘Ports and jetties complex operations come with various forms of pollutions. The analysis of marine pollution from ports becomes very necessary and complicated due to the various types of pollution, sources, effects and different characteristics. The sources of environmental pollution other than ships and from industrial activities in port and jetties were critically looked at and analyzed. A complete review of the environmental pollution in ports and the tools to assess and minimize such negative environmental impact are analyzed. The instrument of questionnaires was employed and distributed among two seaports and one jetty;Onne, Okrika and Port Harcourt to collect respondents’ opinions on effects, sources and causes of marine pollution. The chi-square test for independence was used with 180 respondents from Onne port, Port Harcourt port and Okrika jetty. Water sample was collected from Onne seaport and pollution contents such as total petroleum hydrocarbon (TPH), bio-chemical oxygen demand (BOD), turbidity, pH and salinity were tested in the laboratory. The result shows that Onne water had a salinity level of 20,790 (mg/l) which under the salinity range of water is considered saline, a turbidity level of 4.00 (NTU) which was considered average comparing with a 5.00 (NTU) bench mark, BOD5 level of 0.48 (mg/l) which was considered pristine because most pristine seawater will have BOD below 1 (mg/l), pH level of 7.77 which falls under the range of sea water being alkaline (7.2 - 8.4), TPH level of 2.98 (mg/l) since all conditions of sampling and sample preservations were observed and the value is less than the DPR limit (10 mg/l). It was concluded that the activities in Onne port are within the acceptable limits. It was also observed from the questionnaire that a larger population of respondents in Onne, Okrika and Port Harcourt ports where conscious of the sources and effects of environmental pollution from their respective ports.
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National Key Research and Development Program of China(2024YFE0203600)National Natural Science Foundation of China(62135009)Beijing Municipal Science&Technology Commission,Administrative Commission of Zhongguancun Science Park(Z221100005322010)。
文摘Deep learning has rapidly advanced amidst the proliferation of large models,leading to challenges in computational resources and power consumption.Optical neural networks(ONNs)offer a solution by shifting computation to optics,thereby leveraging the benefits of low power consumption,low latency,and high parallelism.The current training paradigm for ONNs primarily relies on backpropagation(BP).However,the reliance is incompatible with potential unknown processes within the system,which necessitates detailed knowledge and precise mathematical modeling of the optical process.In this paper,we present a pre-sensor multilayer ONN with nonlinear activation,utilizing a forward-forward algorithm to directly train both optical and digital parameters,which replaces the traditional backward pass with an additional forward pass.Our proposed nonlinear optical system demonstrates significant improvements in image classification accuracy,achieving a maximum enhancement of 9.0%.It also validates the efficacy of training parameters in the presence of unknown nonlinear components in the optical system.The proposed training method addresses the limitations of BP,paving the way for applications with a broader range of physical transformations in ONNs.
基金support from the Carnegie Trust for the Universities of ScotlandPRIN 2022597MBS PHERMIACsupported by the European Research Council(ERC)under the European Union Horizon 2020 Research and Innovation Program(Grant Agreement No.819346)。
文摘Optical neural networks(ONNs)are a class of emerging computing platforms that leverage the properties of light to perform ultra-fast computations with ultra-low energy consumption.ONNs often use CCD cameras as the output layer.In this work,we propose the use of perovskite solar cells as a promising alternative to imaging cameras in ONN designs.Solar cells are ubiquitous,versatile,highly customizable,and can be fabricated quickly in laboratories.Their large acquisition area and outstanding efficiency enable them to generate output signals with a large dynamic range without the need for amplification.Here we have experimentally demonstrated the feasibility of using perovskite solar cells for capturing ONN output states,as well as the capability of single-layer random ONNs to achieve excellent performance even with a very limited number of pixels.Our results show that the solar-cell-based ONN setup consistently outperforms the same setup with CCD cameras of the same resolution.These findings highlight the potential of solar-cell-based ONNs as an ideal choice for automated and battery-free edge-computing applications.
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National Key Research and Development Program of China(2021YFA1401100)Innovation Group Project of Sichuan Province(20CXTD0090)。
文摘The rapid growth of deep learning applications has sparked a revolution in computing paradigms,with optical neural networks(ONNs)emerging as a promising platform for achieving ultra-high computing power and energy efficiency.Despite great progress in analog optical computing,the lack of scalable optical nonlinearities and losses in photonic devices pose considerable challenges for power levels,energy efficiency,and signal latency.Here,we report an end-to-end all-optical nonlinear activator that utilizes the energy conversion of Brillouin scattering to perform efficient nonlinear processing.The activator exhibits an ultra-low activation threshold(24 nW),a wide transmission bandwidth(over 40 GHz),strong robustness,and high energy transfer efficiency.These advantages provide a feasible solution to overcome the existing bottlenecks in ONNs.As a proof-of-concept,a series of tasks is designed to validate the capability of the proposed activator as an activation unit for ONNs.Simulations show that the experiment-based nonlinear model outperforms classical activation functions in classification(97.64%accuracy for MNIST and 87.84%for Fashion-MNIST)and regression(with a symbol error rate as low as 0%)tasks.This work provides valuable insights into the innovative design of all-optical neural networks.
文摘The oxovanadium（IV） complexes of the Schiff base hydrazones, synthesized from 3-hydrazinoquinoxaline-2- one （HQO） with salicylaldehyde （HSHQO）, o-hydroxyacetophenone （HHAHQO）, dehydroacetic acid （HDHAHQO） and o-nitrobenzaldehyde （NBHQO） were synthesized and characterized on the basis of analytical, conductance, magnetic moment, infrared, NMR, ESR and electronic spectral data. The ligands HSHQO, HDHAHQO behaved as monobasic tridentate ONN donors through phenolic oxygen, azomethine nitrogens. The ligand HAHQO acted as a monobasic bidentate ON donor through the phenolic oxygen, azomethine （free） nitrogen and the ligand NBHQO acted as neutral bidentate ON donor through oxygen of the nitro group and azomethine （free） nitrogen.
