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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 *** and scalability are key challenges faced by current physical random bit ***,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 *** bit rate can be easily enhanced by further increasing the number of comb lines *** 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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Role of Direct Plasma Irradiation, Plasma-Activated Liquid, and Plasma-Treated Soil in Plasma Agriculture

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Plasma Medicine 2023年第3期13卷 33-52页

作者： Attri, Pankaj Okumura, Takamasa Takeuchi, Nozomi Razzokov, Jamoliddin Zhang, Quanzhi Kamataki, Kunihiro Shiratani, Masaharu Koga, Kazunori Center of Plasma Nano-Interface Engineering Kyushu University Fukuoka819-0395 Japan Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka819-0395 Japan Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka819-0395 Japan Department of Electrical and Electronic Engineering Tokyo Institute of Technology Tokyo152-8552 Japan Institute of Material Sciences Academy of Sciences Chingiz Aytmatov 2b Tashkent100084 Uzbekistan School of Physics Dalian University of Technology Dalian China Quantum and Photonics Technology Research Center Kyushu University Fukuoka819-0395 Japan Institute for Advanced Study Kyushu University Fukuoka819-0395 Japan

Seed treatment with non-thermal plasma has seen a tremendous increase in both direct and indirect applications recently. In this review, we examined the effects of direct plasma irradiation, plasma-activated water (PAW), plasma-activated Ringer’s lactate solution, and plasma-treated soil on seeds, resulting in positive, negative, and neutral changes. Furthermore, we will compare the impact of pressure and feed gases on seed germination and seedling growth. Addition-ally, we focused on the types of reactive oxygen and nitrogen species (RONS) and their concentrations produced in the gas and liquid phases, as these play a crucial role in germination percentage and seedling growth. In conclusion, we find that plasma agriculture’s success is contingent on seed morphology, the types and concentrations of reactive species, and specific plasma characteristics. © 2023 by Begell House, Inc.

关键词： Atmospheric pressure

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Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability

arXiv

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arXiv 2024年

作者： Zhang, Jiasheng Ying, Rex Shao, Jie School of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Computer Science Yale University United States Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China

Temporal knowledge graphs (TKGs) are valuable resources for capturing evolving relationships among entities, yet they are often plagued by noise, necessitating robust anomaly detection mechanisms. Existing dynamic graph anomaly detection approaches struggle to capture the rich semantics introduced by node and edge categories within TKGs, while TKG embedding methods lack interpretability, undermining the credibility of anomaly detection. Moreover, these methods falter in adapting to pattern changes and semantic drifts resulting from knowledge updates. To tackle these challenges, we introduce AnoT, an efficient TKG summarization method tailored for interpretable online anomaly detection in TKGs. AnoT begins by summarizing a TKG into a novel rule graph, enabling flexible inference of complex patterns in TKGs. When new knowledge emerges, AnoT maps it onto a node in the rule graph and traverses the rule graph recursively to derive the anomaly score of the knowledge. The traversal yields reachable nodes that furnish interpretable evidence for the validity or the anomalous of the new knowledge. Overall, AnoT embodies a detector-updater-monitor architecture, encompassing a detector for offline TKG summarization and online scoring, an updater for real-time rule graph updates based on emerging knowledge, and a monitor for estimating the approximation error of the rule graph. Experimental results on four real-world datasets demonstrate that AnoT surpasses existing methods significantly in terms of accuracy and interoperability. All of the raw datasets and the implementation of AnoT are provided in https://***/zjs123/ANoT. © 2024, CC BY.

关键词： Knowledge graph

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Universal materials model of deep-learning density functional theory Hamiltonian

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science Bulletin 2024年第16期69卷 2514-2521页

作者： Yuxiang Wang Yang Li Zechen Tang He Li Zilong Yuan Honggeng Tao Nianlong Zou Ting Bao Xinghao Liang Zezhou Chen Shanghua Xu Ce Bian Zhiming Xu Chong Wang Chen Si Wenhui Duan Yong Xu State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics Tsinghua UniversityBeijing 100084China Institute for Advanced Study Tsinghua UniversityBeijing 100084China Frontier Science Center for Quantum Information Beijing 100084China RIKEN Center for Emergent Matter Science(CEMS) Wako 351-0198Japan School of Materials Science and Engineering Beihang UniversityBeijing 100191China

Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence,but how to achieve this fantastic and challenging objective remains ***,we propose a feasible pathway to address this paramount pursuit by developing universal materials models of deep-learning density functional theory Hamiltonian(Deep H),enabling computational modeling of the complicated structure-property relationship of materials in *** constructing a large materials database and substantially improving the Deep H method,we obtain a universal materials model of Deep H capable of handling diverse elemental compositions and material structures,achieving remarkable accuracy in predicting material *** further showcase a promising application of fine-tuning universal materials models for enhancing specific materials *** work not only demonstrates the concept of Deep H's universal materials model but also lays the groundwork for developing large materials models,opening up significant opportunities for advancing artificial intelligencedriven materials discovery.

关键词： Large materials model Universal materials model Deep-learning density functional theory Artificial intelligence-driven materials discovery

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Attitude Takeover control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning

arXiv

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arXiv 2023年

作者： Liu, Yuhan Wang, Pengyu Lee, Chang-Hun Tóth, Roland The Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands The Flight Dynamics and Control Laboratory Department of Aerospace Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of The Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

One major challenge for autonomous attitude takeover control for on-orbit servicing of spacecraft is that an accurate dynamic motion model of the combined vehicles is highly nonlinear, complex and often costly to identify online, which makes traditional model-based control impractical for this task. To address this issue, a recursive online sparse Gaussian Process (GP)-based learning strategy for attitude takeover control of noncooperative targets with maneuverability is proposed, where the unknown dynamics are online compensated based on the learnt GP model in a semi-feedforward manner. The method enables the continuous use of on-orbit data to successively improve the learnt model during online operation and has reduced computational load compared to standard GP regression. Next to the GP-based feedforward, a feedback controller is proposed that varies its gains based on the predicted model confidence, ensuring robustness of the overall scheme. Moreover, rigorous theoretical proofs of Lyapunov stability and boundedness guarantees of the proposed method-driven closed-loop system are provided in the probabilistic sense. A simulation study based on a high-fidelity simulator is used to show the effectiveness of the proposed strategy and demonstrate its high performance. © 2023, CC BY-NC-SA.

关键词： Maneuverability

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Ground-State Energy and Related Properties Estimation in Quantum Chemistry with Linear Dependence on the Number of Atoms

Ground-State Energy and Related Properties Estimation in Qua...

引用

Quantum Computing and engineering (QCE), IEEE International Conference on

作者： Taehee Ko Xiantao Li Chunhao Wang School of Computational sciences Korea Institute for Advanced Study South Korea Department of Mathematics Pennsylvania State University USA Department of Computer Science and Engineering Pennsylvania State University USA

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

At the heart of quantum chemistry and materials science lies the critical task of estimating ground-state properties. We present a quantum algorithm for this task by quantizing the density-functional theory (DFT). A key aspect of imple-menting DFT faithfully is the requirement for self-consistent calculations, which involve repeated di-agonalizations of the Hamiltonian. This procedure, however, creates a significant bottleneck, as a classical algorithm generally demands a computational complexity that grows cubically with the number of electrons, restricting the scalability of DFT for tackling large-scale problems that involve complex chemical environments and microstructures. This article presents the first quantum algorithm that has provided substantial speedup for the ground state computation, by improving the complexity to one with a linear scaling with the number of atoms. The algorithm leverages the exponential speedup by the quantum singular value transformation to generate a quantum circuit to encode the density-matrix, followed by an efficient estimation method for the output electron density, which constitutes a simple hybrid approach for achieving self-consistency. Moreover, the algorithm produces the ground state Hamiltonian, from which the ground state energy and band structures can be efficiently computed. The proposed framework is accompanied by a rigorous error analysis that establishes the convergence and quantifies various sources of error and the overall computational complexity. The combination of effi-ciency and precision opens new avenues for exploring large-scale physical systems.

关键词： Quantum algorithm Quantum chemistry Scalability Stationary state Discrete Fourier transforms Estimation Atoms Computational complexity Quantum circuit Electrons

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Identification and semisynthesis of(-)-anisomelic acid as oral agent against SARS-CoV-2 in mice

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National science Review 2022年第11期9卷 29-36页

作者： Hai-Xin Yu Nan Zheng Chi-Tai Yeh Chien-Ming Lee Qi Zhang Wen-Lv Zheng Qing Chang Yuan-He Li Yu-Jun Li Gui-Zhen Wu Jun-Min Quan Lin-Qi Zhang Yew-Min Tzeng Zhen Yang Laboratory of Chemical Genomics School of Chemical Biology and Biotechnology Peking University Shenzhen Graduate School Department of Medicinal Research and Education Taipei Medical University-Shuang Ho Hospital Department of Applied Science Taitung University Center for Global Health and Infectious Diseases Comprehensive AIDS Research Center and Beijing Advanced Innovation Center for Structural Biology School of Medicine Tsinghua University Lanzhou Institute of Separation Science State Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education and Beijing National Laboratory for Molecular Science(BNLMS) College of Chemistry and Molecular EngineeringPeking University Shenzhen Bay Laboratory NHC Key Laboratory of Biosafety National Institute for Viral Disease Control and Prevention Chinese Center for Disease Control and Prevention Department of Applied Chemistry Chaoyang University of Technology

(-)-Anisomelic acid, isolated from Anisomeles indica(L.) Kuntze(Labiatae) leaves, is a macrocyclic cembranolide with a trans-fused α-methylene-γ-lactone motif. Anisomelic acid effectively inhibits SARS-Co V-2 replication and viral-induced cytopathic effects with an EC50 of 1.1 and 4.3 μM, *** studies of SARS-CoV-2-infected K18-hACE2 mice showed that oral administration of anisomelic acid and subcutaneous dosing of remdesivir can both reduce the viral titers in the lung tissue at the same level. To facilitate drug discovery, we used a semisynthetic approach to shorten the project timelines. The enantioselective semisynthesis of anisomelic acid from the naturally enriched and commercially available starting material(+)-costunolide was achieved in five steps with a 27% overall *** developed chemistry provides opportunities for developing anisomelic-acid-based novel ligands for selectively targeting proteins involved in viral infections.

关键词： identification enantioselective semisynthesis (−)-anisomelic acid oral agent anti-SARS-CoV-2

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Copper acetate-facilitated transfer-free growth of high-quality graphene for hydrovoltaic generators

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National science Review 2022年第7期9卷 71-79页

作者： Jingyuan Shan Sunmiao Fang Wendong Wang Wen Zhao Rui Zhang Bingzhi Liu Li Lin Bei Jiang Haina Ci Ruojuan Liu Wen Wang Xiaoqin Yang Wenyue Guo Mark H.Rümmeli Wanlin Guo Jingyu Sun Zhongfan Liu Center for Nanochemistry (CNC) Beijing Science and Engineering Center for Nanocarbons Beijing National Laboratory for Molecular Sciences College of Chemistry and Molecular Engineering Peking University Academy for Advanced Interdisciplinary Studies Peking University Key Laboratory for Intelligent Nano Materials and Devices of the Ministry of Education State Key Laboratory of Mechanics and Control of Mechanical Structures Institute of Nanoscience Nanjing University of Aeronautics and Astronautics Department of Physics and Astronomy University of Manchester School of Materials Science and Engineering China University of Petroleum (East China) College of Energy Soochow Institute for Energy and Materials Innovations (SIEMIS)Jiangsu Provincial Key Laboratory for Advanced Carbon Materials and Wearable Energy TechnologiesSoochow University Beijing Graphene Institute (BGI)

Direct synthesis of high-quality graphene on dielectric substrates without a transfer process is of vital importance for a variety of *** strategies for boosting high-quality graphene growth,such as remote metal catalyzation,are limited by poor performance with respect to the release of metal catalysts and hence suffer from a problem with metal ***,we report an effective approach that utilizes a metal-containing species,copper acetate,to continuously supply copper clusters in a gaseous form to aid transfer-free growth of graphene over a wafer *** thus-derived graphene films were found to show reduced multilayer density and improved electrical performance and exhibited a carrier mobility of8500 ***,droplet-based hydrovoltaic electricity generator devices based on directly grown graphene were found to exhibit robust voltage output and long cyclic stability,in stark contrast to their counterparts based on transferred graphene,demonstrating the potential for emerging energy harvesting *** work presented here offers a promising solution to organize the metal catalytic booster toward transfer-free synthesis of high-quality graphene and enable smart energy generation.

关键词： chemical vapor deposition graphene copper acetate hydrovoltaic electricity generator transfer-free growth

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Coexistence of near-EF Flat Band and Van Hove Singularity in a Two-Phase Superconductor

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Physical Review X 2024年第2期14卷 021048-021048页

作者： Xuezhi Chen Le Wang Jun Ishizuka Renjie Zhang Kosuke Nogaki Yiwei Cheng Fazhi Yang Zhenhua Chen Fangyuan Zhu Zhengtai Liu Jiawei Mei Youichi Yanase Baiqing Lv Yaobo Huang Shanghai Institute of Applied Physics Tsung-Dao Lee Institute Beijing 100049 China Shenzhen Institute for Quantum Science and Engineering International Quantum Academy Shenzhen 518048 China Faculty of Engineering Beijing National Laboratory for Condensed Matter Physics and Department of Physics Shanghai Synchrotron Radiation Facility School of Physics and Astronomy Zhangjiang Institute for Advanced Study

Quantum many-body systems, particularly, the ones with large near-EF density states, are well known for exhibiting rich phase diagrams as a result of enhanced electron correlations. The recently discovered locally noncentrosymmetric heavy fermion superconductor CeRh2As2 has stimulated extensive attention due to its unusual H−T phase diagram consisting of two-phase superconductivity, antiferromagnetic order, and possible quadrupole-density wave orders. However, the critical near-EF electronic structure remains experimentally elusive. Here, we provide this key information by combining soft-x-ray and vacuum ultraviolet (VUV) angle-resolved-photoemission-spectroscopy measurements and atom-resolved density-functional-theory (DFT)+U calculations. With bulk-sensitive soft x ray, we reveal quasi-2D hole and electron pockets near the EF. On the other hand, under VUV light, the Ce flat bands are resolved with the c−f hybridization persisting up to well above the Kondo temperature. Most importantly, we observe a symmetry-protected fourfold Van Hove singularity (VHS) coexisting with the Ce 4f5/21 flat bands at the X point, which, to the best of our knowledge, has never been reported before. Such a rare coexistence is expected to lead to a large density of states at the zone edge, a large upper critical field of the odd-parity phase, as well as spin and/or charge instabilities with a vector of (1/2, 1/2, 0). Uniquely, it will also result in a new type of f-VHS hybridization that alters the order and fine electronic structure of the VHS and flat bands. Our findings provide not only key insights into the nature of multiple phases in CeRh2As2 but also open up new prospects for exploring the novelties of many-body systems with f-VHS hybridization.

关键词： Electronic structure Flat bands van Hove singularity Heavy-fermion systems Unconventional superconductors Angle-resolved photoemission spectroscopy Density functional theory

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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