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| 1 | Computational imaging without a computer:seeing through random diffusers at the speed of light显示文摘Imaging through diffusers presents a challenging problem with various digital image reconstruction solutions demonstrated to date using computers.Here,we present a computer-free,all-optical image reconstruction method to see through random diffusers at the speed of light.Using deep learning,a set of transmissive diffractive surfaces are trained to all-optically reconstruct images of arbitrary objects that are completely covered by unknown,random phase diffusers.After the training stage,which is a one-time effort,the resulting diffractive surfaces are fabricated and form a passive optical network that is physically positioned between the unknown object and the image plane to all-optically reconstruct the object pattern through an unknown,new phase diffuser.We experimentally demonstrated this concept using coherent THz illumination and all-optically reconstructed objects distorted by unknown,random diffusers,never used during training.Unlike digital methods,all-optical diffractive reconstructions do not require power except for the illumination light.This diffractive solution to see through diffusers can be extended to other wavelengths,and might fuel various applications in biomedical imaging,astronomy,atmospheric sciences,oceanography,security,robotics,autonomous vehicles,among many others. | Yi Luo Yifan Zhao Jingxi Li Ege Çetintaş Yair Rivenson Mona Jarrahi Aydogan Ozcan | 2022 | eLight2022，2，1： | 16 |
| 2 | Influence of N on precipitation behavior,associated corrosion and mechanical properties of super austenitic stainless steel S32654显示文摘The influence of N on the precipitation behavior,associated corrosion,and mechanical properties of S32654 were investigated by microstructural,electrochemical,and mechanical analyses.Increasing the N content results in several alterations:(1) grain refinement,which promotes intergranular precipitation;(2) a linear increase in the driving force for Cr2 N and Mo activity,which accelerates the precipitation of intergranular Cr2 N and π phase,respectively;(3) a linear decrease in the driving force for σ phase and Cr activity,which suppresses the formation of intragranular σ phase.The total amount of precipitates first decreased and then increased with the N content increasing.Furthermore,the intergranular corrosion susceptibility depended substantially on the total amount of precipitates and also first exhibited a decreasing and then an increasing trend as the N content increased.In addition,aging precipitation caused a considerable decrement in the ultimate tensile strength(UTS) and a remarkable increment in the yield strength(YS).Both the UTS and YS always increased with N content increasing throughout the solution and aging process.Whereas the elongation was considerably sensitive to the aging treatment,it exhibited marginal variation with the N content increasing. | Shucai Zhang Huabing Li Zhouhua Jiang Zhixing Li Jingxi Wu Binbin Zhang Fei Duan Hao Feng Hongchun Zhu | 2020 | Journal of Materials Science & Technology2020，42，7： | 10 |
| 3 | Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue显示文摘Histological staining is a vital step in diagnosing various diseases and has been used for more than a century to provide contrast in tissue sections,rendering the tissue constituents visible for microscopic analysis by medical experts.However,this process is time consuming,labour intensive,expensive and destructive to the specimen.Recently,the ability to virtually stain unlabelled tissue sections,entirely avoiding the histochemical staining step,has been demonstrated using tissue-stain-specific deep neural networks.Here,we present a new deep-learning-based framework that generates virtually stained images using label-free tissue images,in which different stains are merged following a micro-structure map defined by the user.This approach uses a single deep neural network that receives two different sources of information as its input:(1)autofluorescence images of the label-free tissue sample and(2)a“digital staining matrix”,which represents the desired microscopic map of the different stains to be virtually generated in the same tissue section.This digital staining matrix is also used to virtually blend existing stains,digitally synthesizing new histological stains.We trained and blindly tested this virtual-staining network using unlabelled kidney tissue sections to generate micro-structured combinations of haematoxylin and eosin(H&E),Jones’silver stain,and Masson’s trichrome stain.Using a single network,this approach multiplexes the virtual staining of label-free tissue images with multiple types of stains and paves the way for synthesizing new digital histological stains that can be created in the same tissue cross section,which is currently not feasible with standard histochemical staining methods. | Yijie Zhang Kevin de Haan Yair Rivenson Jingxi Li Apostolos Delis Aydogan Ozcan | 2020 | Light(Science & Applications)2020，9，1： | 7 |
| 4 | Class-specific differential detection in diffractive optical neural networks improves inference accuracy显示文摘Optical computing provides unique opportunities in terms of parallelization,scalability,power efficiency,and computational speed and has attracted major interest for machine learning.Diffractive deep neural networks have been introduced earlier as an optical machine learning framework that uses task-specific diffractive surfaces designed by deep learning to all-optically perform inference,achieving promising performance for object classification and imaging.We demonstrate systematic improvements in diffractive optical neural networks,based on a differential measurement technique that mitigates the strict nonnegativity constraint of light intensity.In this differential detection scheme,each class is assigned to a separate pair of detectors,behind a diffractive optical network,and the class inference is made by maximizing the normalized signal difference between the photodetector pairs.Using this differential detection scheme,involving 10 photodetector pairs behind 5 diffractive layers with a total of 0.2 million neurons,we numerically achieved blind testing accuracies of 98.54%,90.54%,and 48.51%for MNIST,Fashion-MNIST,and grayscale CIFAR-10 datasets,respectively.Moreover,by utilizing the inherent parallelization capability of optical systems,we reduced the cross-talk and optical signal coupling between the positive and negative detectors of each class by dividing the optical path into two jointly trained diffractive neural networks that work in parallel.We further made use of this parallelization approach and divided individual classes in a target dataset among multiple jointly trained diffractive neural networks.Using this class-specific differential detection in jointly optimized diffractive neural networks that operate in parallel,our simulations achieved blind testing accuracies of 98.52%,91.48%,and 50.82%for MNIST,Fashion-MNIST,and grayscale CIFAR-10 datasets,respectively,coming close to the performance of some of the earlier generations of all-electronic deep neural networks,e.g.,LeNet,which achieves classification accuracies of 98.77%,90.27%,and 55.21%corresponding to the same datasets,respectively.In addition to these jointly optimized diffractive neural networks,we also independently optimized multiple diffractive networks and utilized them in a way that is similar to ensemble methods practiced in machine learning;using 3 independently optimized differential diffractive neural networks that optically project their light onto a common output/detector plane,we numerically achieved blind testing accuracies of 98.59%,91.06%,and 51.44%for MNIST,Fashion-MNIST,and grayscale CIFAR-10 datasets,respectively.Through these systematic advances in designing diffractive neural networks,the reported classification accuracies set the state of the art for all-optical neural network design.The presented framework might be useful to bring optical neural network-based low power solutions for various machine learning applications and help us design new computational cameras that are task-specific. | Jingxi Li Deniz Mengu Yi Luo Yair Rivenson Aydogan Ozcan | 2019 | Advanced Photonics2019，1，4： | 6 |
| 5 | Response of the sediment geochemistry of the Changjiang River(Yangtze River) to the impoundment of the Three Gorges Dam显示文摘Based on the measurement of major and trace elements in suspended sediments in the low reaches of the Changjiang River during throughout a whole hydrologic year, the origins, seasonal variations, and fluxes of multielements and the human impacts on multielements transport processes have been analyzed along with the influence of weathering in the Changjiang River basin. The results show that most element contents were high in both autumn and winter and low in summer, which was largely caused by the dilution of discharge. Weathering detritus in the Changjiang River basin is the main source of most elements in suspended sediments. However, riverine pollution could bring more loadings of Cd, Pb, As and Zn into river water than it did a few decades ago. The annual average fluxes of Cd, Pb and Zn, which are major contamination elements, to the sea were 179 ± 21 tons/year, 7810 ± 675 tons/year, and 12,000 ± 1320 tons/year,respectively, in which approximately 8.7%, 11.9% and 2.7% of their loadings, respectively,were contributed by pollution inputs. Element exports mainly occurred in the summer(44.4%–57.4%) in the lower part of the Changjiang River. A general relationship between sediment retention and element content suggests a positive feedback mechanism for the decreased number of particles, in which element riverine loadings are reduced due to the enhanced trapping effect by the Three Gorges Dam(TGD). Compared to those in 1980,current element shares of the Changjiang River compared to the global budget have declined due to the construction of the TGD. | Hao Wang Xiangbin Ran Jingxi Li Jun Liu Wentao Wu Menglu Li Jiaye Zang | 2019 | Journal of Environmental Sciences2019，31，9： | 6 |
| 6 | Lipid accumulation and CO_2 utilization of two marine oil-rich microalgal strains in response to CO_2 aeration显示文摘Biological CO_2 sequestration by microalgae is a promising and environmentally friendly technology applied to sequester CO_2. The characteristics of neutral lipid accumulation by two marine oil-rich microalgal strains,namely, Isochrysis galbana and Nannochloropsis sp., through CO_2 enrichment cultivation were investigated in this study. The optimum culture conditions of the two microalgal strains are 10% CO_2 and f medium. The maximum biomass productivity, total lipid content, maximum lipid productivity, carbon content, and CO_2 fixation ability of the two microalgal strains were obtained. The corresponding parameters of the two strains were as follows:((142.42±4.58) g/(m^2·d),(149.92±1.80) g/(m^2·d)),((39.95±0.77)%,(37.91±0.58)%),((84.47±1.56) g/(m^2·d),(89.90±1.98) g/(m^2·d)),((45.98±1.75)%,(46.88±2.01)%), and((33.74±1.65) g/(m^2·d),(34.08±1.32) g/(m^2·d)). Results indicated that the two marine microalgal strains with high CO_2 fixation ability are potential strains for marine biodiesel development coupled with CO_2 emission reduction. | WANG Shuai ZHENG Li HAN Xiaotian YANG Baijuan LI Jingxi SUN Chengjun | 2018 | Acta Oceanologica Sinica2018，37，2： | 4 |
| 7 | Polarization multiplexed diffractive computing:all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network显示文摘Research on optical computing has recently attracted significant attention due to the transformative advances in machine learning.Among different approaches,diffractive optical networks composed of spatially-engineered transmissive surfaces have been demonstrated for all-optical statistical inference and performing arbitrary linear transformations using passive,free-space optical layers.Here,we introduce a polarization-multiplexed diffractive processor to all-optically perform multiple,arbitrarily-selected linear transformations through a single diffractive network trained using deep learning.In this framework,an array of pre-selected linear polarizers is positioned between trainable transmissive diffractive materials that are isotropic,and different target linear transformations(complex-valued)are uniquely assigned to different combinations of input/output polarization states.The transmission layers of this polarization-multiplexed diffractive network are trained and optimized via deep learning and error-backpropagation by using thousands of examples of the input/output fields corresponding to each one of the complex-valued linear transformations assigned to diffferent input/output polarization combinations.Our results and analysis reveal that a single diffractive network can successfully approximate and all-optically implement a group of arbitrarily-selected target transformations with a negligible error when the number of trainable diffractive features/neurons(N)approaches N_(p)N_(i)N_(o),where Ni and N_(o) represent the number of pixels at the input and output fields-of-view,respectively,and N_(p) refers to the number of unique linear transformations assigned to different input/output polarization combinations.This polarization-multiplexed all-optical diffractive processor can find various applications in optical computing and polarization-based machine vision tasks. | Jingxi Li Yi-Chun Hung Onur Kulce Deniz Mengu Aydogan Ozcan | 2022 | Light(Science & Applications)2022，11，7： | 3 |
| 8 | Opaque virtual network mapping algorithms based on available spectrum adjacency for elastic optical networks显示文摘Optical network virtualization enables multiple virtual optical networks constructed for different infrastructure users(renters) or applications to coexist over a physical infrastructure. Virtual optical network(VON) mapping algorithm is used to allocate necessary resources in the physical infrastructure to the VON requests(VRs). In this paper, we investigate the opaque VON mapping problems in elastic optical networks(EONs). Based on the concept of available spectrum adjacency(AvS A) on links or paths, we consider both node resource and AvS A on links for node mapping, and present a link mapping method which chooses the routing and spectrum plan whose AvS A on paths is the largest among all the candidates. Finally, the overall VON mapping algorithm(i.e., AvS A-opaque VON mapping, AvS A-OVONM) coordinated node and link mapping is proposed. Extensive simulations are conducted and the results show that AvS A-OVONM has better performance of blocking probability and revenue-to-cost ratio than current algorithms. | Hongxiang WANG Jingxi ZHAO Hui LI Yuefeng JI | 2016 | Science China(Information Sciences)2016，59，4： | 3 |
| 9 | Effect of hydrogen combustion in the primary pump compartment显示文摘Hydrogen combustion in a nuclear power plant may threaten the integrity of some important systems and components.In this paper,the effect of hydrogen combustion in the primary pump compartment is analyzed by different initial hydrogen concentration and igniter locations using Computational Fluid Dynamics method.The results show that the combustion is confined to a limited area without pump damage at about 6.6%hydrogen volume fraction.Once igniting the hydrogen,the combustion affects the whole compartment at the 12%hydrogen volume fraction.The stress caused by the great temperature gradient or high temperature may damage the primary pump. Igniters at the lower location accelerate the combustion process and cause a threat to the pump integrity. | LI Jingxi TONG Lili CAO Xuewu | 2012 | Nuclear Science and Techniques2012，23，6： | 3 |
| 10 | Model comparisons on molten core-concrete interactions显示文摘Based on the plant model of 600-MW PWR,the molten core-concrete interactions(MCCI) under different models are studied.Station blackout (SBO) with steam-driven auxiliary feed water pump failure is selected as the case for the model comparisons analysis.The result shows that thermal resistance model between debris and concrete has much influence on the consequence of MCCI.The concrete erosion rate calculated with gas film model is much higher than that of slag film model.Some other model comparisons such as the chemical reaction heat and the configuration molten pool are also discussed. | TONG Lili CAO Xuewu LI Jingxi YANG Yajun | 2009 | Nuclear Science and Techniques2009，20，4： | 2 |
| 11 | Core cooling in pressurized-water reactor during water injection显示文摘In this paper,the reactor core cooling and its melt progression terminating is evaluated,and the initiation criterion for reactor cavity flooding during water injection is determined.The core cooling in pressurized-water reactor of severe accident is simulated with the thermal hydraulic and severe accident code of SCDAP/RELAP5.The results show that the core melt progression is terminated by water injection,before the core debris has formed at bottom of core,and the initiation of reactor cavity flooding is indicated by the core exit temperature. | TAO Jun LI Jingxi TONG Lili CAO Xuewu | 2011 | Nuclear Science and Techniques2011，22，1： | 2 |
| 12 | Removal of Congo red from aqueous solution using Moringa oleifera seed cake as natural coagulant 显示文摘 | Jingxi Tie Peipei Li Zhibo Xu | 2015 | Desalination and Water Treatment2015，，54： | 1 |
| 13 | Biopsy-free in vivo virtual histology of skin using deep learning显示文摘An invasive biopsy followed by histological staining is the benchmark for pathological diagnosis of skin tumors.The process is cumbersome and time-consuming,often leading to unnecessary biopsies and scars.Emerging noninvasive optical technologies such as reflectance confocal microscopy(RCM)can provide label-free,cellular-level resolution,in vivo images of skin without performing a biopsy.Although RCM is a useful diagnostic tool,it requires specialized training because the acquired images are grayscale,lack nuclear features,and are difficult to correlate with tissue pathology.Here,we present a deep learning-based framework that uses a convolutional neural network to rapidly transform in vivo RCM images of unstained skin into virtually-stained hematoxylin and eosin-like images with microscopic resolution,enabling visualization of the epidermis,dermal-epidermal junction,and superficial dermis layers.The network was trained under an adversarial learning scheme,which takes ex vivo RCM images of excised unstained/label-free tissue as inputs and uses the microscopic images of the same tissue labeled with acetic acid nuclear contrast staining as the ground truth.We show that this trained neural network can be used to rapidly perform virtual histology of in vivo,label-free RCM images of normal skin structure,basal cell carcinoma,and melanocytic nevi with pigmented melanocytes,demonstrating similar histological features to traditional histology from the same excised tissue.This application of deep learning-based virtual staining to noninvasive imaging technologies may permit more rapid diagnoses of malignant skin neoplasms and reduce invasive skin biopsies. | Jingxi Li Jason Garfinkel Xiaoran Zhang Di Wu Yijie Zhang Kevin de Haan Hongda Wang Tairan Liu Bijie Bai Yair Rivenson Gennady Rubinstein Philip O.Scumpia Aydogan Ozcan | 2021 | Light(Science & Applications)2021，10，12： | 1 |
| 14 | A comparison between Moringa oleifera seed press- cake extract and polyaluminum chloride in the removal of di- rect black 19 from synthetic wastewater 显示文摘 | Jingxi Tie Mengbin Jiang Hanchao Li Shuai Zhang Xiwang Zhang | 2015 | Industrial Crops and Products2015，，74： | 1 |
| 15 | Inhalation of TGF-β1 antibody: A new method to inhibit the airway stenosis induced by the endobronchial tuberculosis显示文摘 | Jingxi Zhang Qiang Li Chong Bai Yiping Han Yi Huang | 2009 | Medical Hypotheses2009，，6： | 1 |
| 16 | Ensemble learning of diffractive optical networks显示文摘A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine learning.Specifically,there has been a revival of interest in optical computing hardware due to its potential advantages for machine learning tasks in terms of parallelization,power efficiency and computation speed.Diffractive deep neural networks(D^(2)NNs)form such an optical computing framework that benefits from deep learning-based design of successive diffractive layers to all-optically process information as the input light diffracts through these passive layers.D^(2)NNs have demonstrated success in various tasks,including object classification,the spectral encoding of information,optical pulse shaping and imaging.Here,we substantially improve the inference performance of diffractive optical networks using feature engineering and ensemble learning.After independently training 1252 D^(2)NNs that were diversely engineered with a variety of passive input filters,we applied a pruning algorithm to select an optimized ensemble of D^(2)NNs that collectively improved the image classification accuracy.Through this pruning,we numerically demonstrated that ensembles of N=14 and N=30 D^(2)NNs achieve blind testing accuracies of 61.14±0.23%and 62.13±0.05%,respectively,on the classification of GFAR-10 test images,providing an inference improvennent of>16%compared to the average performance of the individual D^(2)NNs within each ensemble.These results constitute the highest inference accuracies achieved to date by any diffractive optical neural network design on the same dataset and might provide a significant leap to extend the application space of diffractive optical image classification and machine vision systems. | Md Sadman Sakib Rahman Jingxi Li Deniz Mengu Yair Rivenson Aydogan Ozcan | 2021 | Light(Science & Applications)2021，10，1： | 1 |
| 17 | Study on mitigation of in-vessel release of fission products in severe accidents of PWR显示文摘 | HUANG Gaofeng TONG Lili LI Jingxi | 2010 | Nuclear Engineering and Design2010，240，： | 1 |
| 18 | Massively parallel universal linear transformations using a wavelength-multiplexed diffractive optical network显示文摘Large-scale linear operations are the cornerstone for performing complex computational tasks.Using optical computing to perform linear transformations offers potential advantages in terms of speed,parallelism,and scalability.Previously,the design of successive spatially engineered diffractive surfaces forming an optical network was demonstrated to perform statistical inference and compute an arbitrary complex-valued linear transformation using narrowband illumination.We report deep-learning-based design of a massively parallel broadband diffractive neural network for all-optically performing a large group of arbitrarily selected,complex-valued linear transformations between an input and output field of view,each with Ni and No pixels,respectively.This broadband diffractive processor is composed of Nw wavelength channels,each of which is uniquely assigned to a distinct target transformation;a large set of arbitrarily selected linear transformations can be individually performed through the same diffractive network at different illumination wavelengths,either simultaneously or sequentially(wavelength scanning).We demonstrate that such a broadband diffractive network,regardless of its material dispersion,can successfully approximate Nw unique complex-valued linear transforms with a negligible error when the number of diffractive neurons(N)in its design is≥2NwNiNo.We further report that the spectral multiplexing capability can be increased by increasing N;our numerical analyses confirm these conclusions for Nw>180 and indicate that it can further increase to Nw∼2000,depending on the upper bound of the approximation error.Massively parallel,wavelength-multiplexed diffractive networks will be useful for designing highthroughput intelligent machine-vision systems and hyperspectral processors that can perform statistical inference and analyze objects/scenes with unique spectral properties. | Jingxi Li Tianyi Gan Bijie Bai Yi Luo Mona Jarrahi Aydogan Ozcan | 2023 | Advanced Photonics2023，5，1： | 1 |
| 19 | Distribution pattern and geochemical analysis of rare earth elements in deep-ocean sediments显示文摘The content and distribution pattern of rare earth elements(REEs)in surface sediments from the Eastern and Western Pacifi c Ocean,the Northern and Southern Atlantic Ocean,and the Southwestern Indian Ocean were explored and the resources and geochemical characteristics of REEs in deep-ocean sediments from diff erent oceans were studied.The total REE abundances(ΣREE)in the diff erent oceans ranged as follows:Eastern Pacifi c,56.88–500.02μg/g;Western Pacifi c,290.68–439.94μg/g;Northern Atlantic,55.33–154.90μg/g;Southern Atlantic,40.83–69.30μg/g;and Southwestern Indian Ocean,20.24–64.76μg/g.Their corresponding LREE(La-Eu)/HREE(Gd-Lu)average values were 5.18,5.86,9.01,5.21,and 4.59,which indicated that the light REEs were all evidently enriched.δEu andδCe showed slight Eu-negative anomalies and signifi cant Ce-positive anomalies in all sediments.Although the contents of REEs in the sediments varied among the diff erent oceans,the distribution patterns of REEs were similar,and the correlation coeffi cient was greater than 0.9290.In the Eastern Pacifi c sediments,ΣREE showed a signifi cantly positive correlation with Co,Cu,Zn,Mn,Mo and a weak correlation with Fe.In the Western Pacifi c and Southern Atlantic sediments,ΣREE presented no obvious correlation and a weakly negative correlation with Co,Cu,Zn,Mn,Mo and Fe,respectively.ΣREE in the Southwestern Indian Ocean sediments positively correlated with Cu,Zn,Mn,Mo,Fe,and had a weakly negative correlation with Co. | Jingxi LI Chengjun SUN Fenghua JIANG Fenglei GAO Yifan ZHENG | 2021 | Journal of Oceanology and Limnology2021，39，1： | 1 |
| 20 | Label-Free Virtual HER2 Immunohistochemical Staining of Breast Tissue using Deep Learning显示文摘The immunohistochemical(IHC)staining of the human epidermal growth factor receptor 2(HER2)biomarker is widely practiced in breast tissue analysis,preclinical studies,and diagnostic decisions,guiding cancer treatment and investigation of pathogenesis.HER2 staining demands laborious tissue treatment and chemical processing performed by a histotechnologist,which typically takes one day to prepare in a laboratory,increasing analysis time and associated costs.Here,we describe a deep learning-based virtual HER2 IHC staining method using a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled/label-free breast tissue sections into bright-field equivalent microscopic images,matching the standard HER2 IHC staining that is chemically performed on the same tissue sections.The efficacy of this virtual HER2 staining framework was demonstrated by quantitative analysis,in which three board-certified breast pathologists blindly graded the HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images(WSIs)to reveal that the HER2 scores determined by inspecting virtual IHC images are as accurate as their immunohistochemically stained counterparts.A second quantitative blinded study performed by the same diagnosticians further revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail,membrane clearness,and absence of staining artifacts with respect to their immunohistochemically stained counterparts.This virtual HER2 staining framework bypasses the costly,laborious,and time-consuming IHC staining procedures in laboratory and can be extended to other types of biomarkers to accelerate the IHC tissue staining used in life sciences and biomedical workflow. | Bijie Bai Hongda Wang Yuzhu Li Kevin de Haan Francesco Colonnese Yujie Wan Jingyi Zuo Ngan B.Doan Xiaoran Zhang Yijie Zhang Jingxi Li Xilin Yang Wenjie Dong Morgan Angus Darrow Elham Kamangar Han Sung Lee Yair Rivenson Aydogan Ozcan | 2022 | Biomedical Engineering Frontiers2022，3，1： | 0 |
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