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Scalable parallel ultrafast optical random bit generation based on a single chaotic microcomb

Light(Science & Applications) 2024年第4期13卷 637-644页

作者： Pu Li Qizhi Li Wenye Tang Weiqiang Wang Wenfu Zhang Brent ELittle Sai Tek Chu KAlan Shore Yuwen Qin Yuncai Wang Institute of Advanced Photonics Technology School of Information EngineeringGuangdong University of TechnologyGuangzhou 51006China. Key Laboratory of Photonic Technology for Integrated Sensing and Communication Ministry of Education of ChinaGuangdong University of TechnologyGuangzhou 51006China. Guangdong Provincial Key Laboratory of Information Photonics Technology Guangdong University of TechnologyGuangzhou 51006China. Key Laboratory of Advanced Transducers andIntelligent Control System Ministry of EducationTaiyuan University of TechnologyTaiyuan 030024China. State Key Laboratory of Transient Optics and Photonics Xi’an Institute of Optics and Precision MechanicsChinese Academy of SciencesXi’an 710119China. Department of Physics and Materials Science City University of Hong KongHong KongChina. School of Electronic Engineering Bangor UniversityBangorWales LL571UTUK

random bit generators are critical for information security,cryptography,stochastic modeling,and simulations.Speed and scalability are key challenges faced by current physical random bit generation.Herein,we propose a massively parallel scheme for ultrafast random bit generation towards rates of order 100 terabit per second based on a single micro-ring resonator.A modulation-instability-driven chaotic comb in a micro-ring resonator enables the simultaneous generation of hundreds of independent and unbiased random bit streams.A proof-of-concept experiment demonstrates that using our method,random bit streams beyond 2 terabit per second can be successfully generated with only 7 comb lines.This bit rate can be easily enhanced by further increasing the number of comb lines used.Our approach provides a chip-scale solution to random bit generation for secure communication and high-performance computation,and offers superhigh speed and large scalability.

关键词： resonator random chaotic

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random fractal-based computational design of an ice-ray (IR) lattice shell structure

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Frontiers of Architectural Research 2024年第2期13卷 406-424页

作者： Iasef Md Rian Department of Architecture Xi'an Jiaotong-Liverpool UniversitySuzhou 215123China

Chinese ice-ray (IR) lattices, known for their intricate and visually fascinating random patterns as decorative elements in traditional 18th-century Chinese window design, exhibit underlying stiffness as latticed window fences. Such unique patterns represent a new morphology within the family of stochastic lattices. However, the latent structural potential within the random patterns of ice-ray lattices remains largely unexplored, particularly in the context of lattice shell design. This study systematically studies the geometric qualities of ice-ray lattice patterns and develops an algorithm to model these patterns for ice-ray lattice shell design. Subsequently, it assesses the structural feasibility and effectiveness of these lattice shells in comparison to conventional gridshells. The practicality of constructing random lattice shells using digital fabrication tools is also explored. Employing fractal geometry as a foundational framework, this research not only offers insights into the potential of ice-ray lattices for innovative lattice shell design but also introduces a new structural morphology to the field, expanding the possibilities of incorporating stochastic patterns in lattice shell design. Ultimately, it opens up new opportunities for innovative lattice shell designs, emphasizing the potential of stochastic patterns in structural applications.

关键词： Ice-ray pattern Fractals random Lattice shells Computational design Digital fabrication

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Prediction of large vessel occlusion for ischaemic stroke by using the machine learning model random forests

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Stroke & Vascular Neurology 2022年第2期7卷 94-100页

作者： Jianan Wang Jungen Zhang Xiaoxian Gong Wenhua Zhang Ying Zhou Min Lou Department of Neurology Zhejiang University School of Medicine Second Affiliated Hospital Department of NeurologyHangzhouZhejiangChina

Backgrounds The timely identification of large vessel occlusion(LVO)in the prehospital stage is extremely important given the disease morbidity and narrow time window for intervention.The current evaluation strategies still remain challenging.The goal of this study was to develop a machine learning(ML)model to predict LVO using prehospital accessible data.Methods Consecutive acute ischaemic stroke patients who underwent CT or MR angiography and received reperfusion therapy within 8 hours from symptom onset in the Computer-based Online Database of Acute Stroke Patients for Stroke Management Quality Evaluation-II dataset from January 2016 to August 2021 were included.We developed eight ML models to integrate National Institutes of Health Stroke Scale(NIHSS)items with demographics,medical history and vascular risk factors to identify LVO and validate its efficiency.Results Finally,15365 patients were included in the training set and 4215 patients were included in the test set.On the test set,random forests(RF),gradient boosting machine and extreme gradient boosting presented area under the curve(AUC)of 0.831(95%CI 0.819 to 0.843),which were higher than other models,and RF presented the highest specificity(0.827).In addition,the AUC of RF was higher than other scales,and the accuracy of the model was improved by 6.4%compared with NIHSS.We also found the top five items of identifying LVO were total NIHSS score,gaze deviation,level of consciousness(LOC),LOC commands and motor left leg.Conclusions Our proposed model could be a useful screening tool to predict LVO based on the prehospital accessible medical data.

关键词： boosting random narrow

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Pressure sensing recognition of FBG array based on random forest algorithm

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Optoelectronics Letters 2023年第5期19卷 262-268页

作者： WANG Yan ZHU Wei GE Ziyang WANG Junliang XU Haoyu JIANG Chao School of Electrical and Information Engineering Anhui University of TechnologyMaanshan 243000China

In order to improve the precision of static load pressure recognition and identify the position of the applied force accurately,a fiber Bragg grating(FBG)flexible sensor array is proposed in this work.Numerical analysis for the package thickness(4 mm)and package position(2 mm from the bottom)of the FBG flexible sensor is performed using COMSOL,and optimal package thickness(4 mm)and package position(2 mm from the bottom)are selected in the analysis.By using 12-FBGs layout method and random forest algorithm,the position and load prediction model is established.The results show that the average error of the distance between the prediction points of coordinates X-Y and static load F and the real sample points is 0.092.Finally,to verify the proposed models,the pressure sensing experiments of the flexible FBG array are carried out on this basis.The weights of 100 g to 1000 g are applied to different regions of the flexible sensor array one by one in accordance with a certain trajectory.The variation of each FBG wavelength was taken as the input of the stochastic forest prediction model,and the coordinate position and the static load size F were taken as the output to establish the prediction model.The minimum distance error between the actual point and the predicted point was calculated by experiment as 0.03491.The maximum is 0.2481,and the mean error is 0.1515.It is concluded that the random forest prediction model has a good prediction effect on the pressure sensing of the flexible FBG sensing array.

关键词： prediction random algorithm

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A controlled random gene perturbation method identifies ARPC1B gene as a key regulator of cancer metastasis

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Genes & Diseases 2023年第3期10卷 687-689页

作者： De Chang Hongzhen Du Xiaowei Chen Xiaocui Bian Wenjia Tian Jing Shen Yiju Wei Yunpeng Jiang Charles Dela Cruz Lixin Xie Lokesh Sharma Kailong Li College of Pulmonary and Critical Care Medicine Chinese PLA General HospitalBeijing 100083China Institute of Basic Medical Sciences Chinese Academy of Medical SciencesSchool of Basic Medicine Peking Union Medical CollegeBeijing 100005China Section of Pulmonary and Critical Care and Sleep Medicine Department of MedicineYale University School of MedicineNew HavenCT 06520USA Department of Biochemistry and Biophysics Peking University Health Science CenterBeijing 100191China

The discovery of clinically useful targets to treat specific pathological conditions can provide novel therapeutic approaches.To unbiasedly identify key genes related to tumor metastasis,we developed a random gene perturbation method using a piggyBac transposon system under the control of a doxycycline regulated promoter.Using this random gene perturbation method and utilizing a mouse model of metastatic pancreatic cancer,we identified genes dysregulated in metastasized cells from a random mutagenesis library after multiple rounds of in vivo selection.Analysis of these metastasized clones revealed the downregulation of ARPC1B gene.Our further mechanistic studies revealed that ARPC1B gene and its closely related gene ARPC1A worked in a regulatory loop to control tumor metastasis.These findings validate that piggyBac transposon mediated random gene perturbation is a powerful tool to investigate the functional relevance of novel genes and the ARPC1A/B axis is a potential key regulator of tumor metastasis.

关键词： metastasis cancer random

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A micromirror array with annular partitioning for high-speed random-access axial focusing

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Light(Science & Applications) 2020年第1期9卷 241-255页

作者： Nathan Tessema Ersumo Cem Yalcin Nick Antipa Nicolas Pegard Laura Waller Daniel Lopez Rikky Muller The University of California Berkeley and University of CaliforniaSan Francisco Graduate Program in BioengineeringBerkeleyCA 94720USA Department of Electrical Engineering&Computer Sciences University of CaliforniaBerkeleyCA 94720USA Department of Applied Physical Sciences University of North Carolina at Chapel HillChapel HillNC 27514USA Chan Zuckerberg Biohub San FranciscoCA 94158USA Physical Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD 20899USA

Dynamic axial focusing functionality has recently experienced widespread incorporation in microscopy,augmented/virtual reality(AR/VR),adaptive optics and material processing.However,the limitations of existing varifocal tools continue to beset the performance capabilities and operating overhead of the optical systems that mobilize such functionality.The varifocal tools that are the least burdensome to operate(e.***.liquid crystal,elastomeric or optofluidic lenses)suffer from low(≈100 Hz)refresh rates.Conversely,the fastest devices sacrifice either critical capabilities such as their dwelling capacity(e.***.acoustic gradient lenses or monolithic micromechanical mirrors)or low operating overhead(e.***.deformable mirrors).Here,we present a general-purpose random-access axial focusing device that bridges these previously conflicting features of high speed,dwelling capacity and lightweight drive by employing lowrigidity micromirrors that exploit the robustness of defocusing phase profiles.Geometrically,the device consists of an 8.2mm diameter array of piston-motion and 48-μm-pitch micromirror pixels that provide 2πphase shifting for wavelengths shorter than 1100 nm with 10-90% settling in 64.8μs(i.e.,15.44 kHz refresh rate).The pixels are electrically partitioned into 32 rings for a driving scheme that enables phase-wrapped operation with circular symmetry and requires<30 V per channel.Optical experiments demonstrated the array’s wide focusing range with a measured ability to target 29 distinct resolvable depth planes.Overall,the features of the proposed array offer the potential for compact,straightforward methods of tackling bottlenecked applications,including high-throughput single-cell targeting in neurobiology and the delivery of dense 3D visual information in AR/VR.

关键词： mirror axial random

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XFEL and HHG interaction with matter:Effects of ultrashort pulses and random spikes

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Matter and Radiation at Extremes 2021年第3期6卷 1-7页

作者： F.B.Rosmej V.A.Astapenko E.S.Khramov Sorbonne University Faculty of Science and EngineeringUMR 7605Case 1284 Place JussieuF-75252 Paris Cedex 05France LULI Ecole PolytechniqueCEACNRSLaboratoire pour l’Utilisation des Lasers IntensesPhysique Atomique dans les Plasmas DensesF-91128 PalaiseauFrance Moscow Institute of Physics and Technology-MIPT Institutskii per.9Dolgoprudnyi 141700Russia National Research Nuclear University MEPhI(Moscow Engineering Physics Institute) Kashirskoe sh.31Moscow 115409Russia

The theory of photoionization describing the interaction of x-ray free-electron laser(XFEL)pulses and high-harmonic-generated(HHG)radiation is generalized to ultrashort laser pulses,where the concept of the standard ionization probability per unit time in Fermi’s golden rule and in Einstein’s theory breaks down.Numerical calculations carried out in terms of a generalized photoionization probability for the total duration of pulses in the near-threshold regime demonstrate essentially nonlinear behavior,while absolute values may change by orders of magnitude for typical XFEL and HHG pulses.XFEL self-amplified spontaneous emission pulses are analyzed to reveal general features of photoionization for random and regular spikes:the dependences of the nonlinear photoionization probability on carrier frequency and spike duration are very similar,allowing an analytical expectation value approach that is valid even when there is only limited knowledge of random and regular parameters.Numerical simulations carried out for typical parameters demonstrate excellent agreement.

关键词： random interaction nonlinear

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The 2018 Nobel Prize in Chemistry: Engineering proteins(enzymes/peptide/antibodies) towards desired properties via the construction of random libraries

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Science China(Life Sciences) 2019年第5期62卷 713-724页

作者： Zengyi Chang School of Life Sciences State Key Laboratory of Protein and Plant Engineering Research Center for Protein Science Center of History and Philosophy of Science Peking University

The 2018 Nobel Prize in Chemistry was awarded one half to Dr. Frances Arnold (of California Institute of Technology,USA) for her work on 'directed evolution of enzymes', the other half to Dr. George Smith (of the University of Missouri, USA) and Sir Gregory Winter (of MRC Laboratory of Molecular Biology, UK) for their work on 'phage display of peptides and antibodies'.

关键词： The 2018 Nobel Prize in Chemistry towards desired properties via the construction of random libraries Engineering proteins

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Vortex random fiber laser with controllable orbital angular momentum mode

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Photonics Research 2021年第2期9卷 266-271页

作者： XIAOYA MA JUN YE YANG ZHANG JIANGMING XU JIAN WU TIANFU YAO JINYONG LENG PU ZHOU College of Advanced Interdisciplinary Studies National University of Defense TechnologyChangsha 410073China

In this paper, we propose and experimentally demonstrate a vortex random fiber laser(RFL) with a controllable orbital angular momentum(OAM) mode. The topological charge of the vortex RFL can range from-50 to 50 with nearly watt-level output power. A triangular toroidal interferometer is constructed to verify the spiral phase structure of the generated vortex random laser with a special coherence property. Vortex RFLs with fractional topological charge are also performed in this work. As the first demonstration of a vortex RFL with a controllable OAM mode(to the best of our knowledge), this work may not only offer a valuable reference on temporal modulation of a vortex beam and optical field control of an RFL but also provide a potential vortex laser source for applications in imaging, sensing, and communication.

关键词： fiber interferometer random

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All-optical image classification through unknown random diffusers using a single-pixel diffractive network

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Light(Science & Applications) 2023年第4期12卷 570-584页

作者： Bijie Bai Yuhang Li Yi Luo Xurong Li Ege Cetintas Mona Jarrrahi Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCalifornia 90095USA Bioengineering Department University of CaliforniaLos AngelesCalifornia 90095USA California Nano Systems Institute(CNSI) University of CaliforniaLos AngelesCalifornia 90095USA

Classification of an object behind a random and unknown scattering medium sets a challenging task for computational imaging and machine vision fields.Recent deep learning-based approaches demonstrated the classification of objects using diffuser-distorted patterns collected by an image sensor.These methods demand relatively large-scale computing using deep neural networks running on digital computers.Here,we present an all-optical processor to directly classify unknown objects through unknown,random phase diffusers using broadband illumination detected with a single pixel.A set of transmissive diffractive layers,optimized using deep learning,forms a physical network that all-optically maps the spatial information of an input object behind a random diffuser into the power spectrum of the output light detected through a single pixel at the output plane of the diffractive network.We numerically demonstrated the accuracy of this framework using broadband radiation to classify unknown handwritten digits through random new diffusers,never used during the training phase,and achieved a blind testing accuracy of 87.74±1.12%.We also experimentally validated our single-pixel broadband diffractive network by classifying handwritten digits"0"and"1"through a random diffuser using terahertz waves and a 3D-printed diffractive network.This single-pixel all-optical object classification system through random diffusers is based on passive diffractive layers that process broadband input light and can operate at any part of the electromagnetic spectrum by simply scaling the diffractive features proportional to the wavelength range of interest.These results have various potential applications in,e.g.,biomedical imaging,security,robotics,and autonomous driving.

关键词： network processor random

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