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物理化学学报 2021年第12期37卷 1-151页

作者：常诚陈伟陈也陈永华陈雨丁峰樊春海范红金范战西龚成宫勇吉何其远洪勋胡晟胡伟达黄维黄元季威李德慧李连忠李强林立凌崇益刘鸣华刘楠刘庄 Kian Ping Loh 马建民缪峰彭海琳邵明飞宋礼苏邵孙硕谭超良唐智勇王定胜王欢王金兰王欣王欣然 Andrew T.S.Wee 魏钟鸣吴宇恩吴忠帅熊杰熊启华徐伟高尹鹏曾海波曾志远翟天佑张晗张辉张其春张铁锐张翔赵立东赵美廷赵伟杰赵运宣周凯歌周兴周喻朱宏伟张华刘忠范 Institute of Science and Technology Austria Am Campus 13400 KlosterneuburgAustria Department of Chemistry National University of Singapore3 Science Drive 3117543Singapore Department of Chemistry The Chinese University of Hong KongShatinNew TerritoriesHong KongChina Key Laboratory of Flexible Electronics and Institute of Advanced Materials Nanjing Tech UniversityNanjing 211816China School of Life Sciences Shanghai UniversityShanghai 200444China Centre for Multidimensional Carbon Materials Institute for Basic ScienceUlsan 44919Korea School of Chemistry and Chemical Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Physical and Mathematical Sciences Nanyang Technological UniversitySingapore 637371Singapore Department of Chemistry City University of Hong KongKowloonHong KongChina Department of Electrical and Computer Engineering and Quantum Technology Center University of MarylandCollege ParkMaryland 20742USA School of Materials Science and Engineering Beihang UniversityBeijing 100191China Department of Materials Science and Engineering City University of Hong KongKowloonHong KongChina Center of Advanced Nanocatalysis(CAN) Hefei National Laboratory for Physical Sciences at the MicroscaleDepartment of Applied ChemistryUniversity of Science and Technology of ChinaHefei 230026China College of Chemistry and Chemical Engineering State Key Laboratory of Physical Chemistry of Solid SurfacesCollaborative Innovation Center of Chemistry for Energy Materials(iChEM)Xiamen UniversityXiamen361005Fujian ProvinceChina State Key Laboratory of Infrared Physics Shanghai Institute of Technical PhysicsChinese Academy of SciencesShanghai 200083China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing100081China Beijing Key Laboratory of Optoelectronic Functional Materials&Micro-Nano Devices Department of PhysicsRenmin University of ChinaBeijing 100872China School of Optical and Electronic Information Wuhan National Laboratory

Research on two-dimensional(2D) materials has been explosively increasing in last seventeen years in varying subjects including condensed matter physics, electronic engineering, materials science, and chemistry since the mechanical exfoliation of graphene in 2004. Starting from graphene, 2D materials now have become a big family with numerous members and diverse categories. The unique structural features and physicochemical properties of 2D materials make them one class of the most appealing candidates for a wide range of potential applications. In particular, we have seen some major breakthroughs made in the field of 2D materials in last five years not only in developing novel synthetic methods and exploring new structures/properties but also in identifying innovative applications and pushing forward commercialisation. In this review, we provide a critical summary on the recent progress made in the field of 2D materials with a particular focus on last five years. After a brief backgroundintroduction, we first discuss the major synthetic methods for 2D materials, including the mechanical exfoliation, liquid exfoliation, vapor phase deposition, and wet-chemical synthesis as well as phase engineering of 2D materials belonging to the field of phase engineering of nanomaterials(PEN). We then introduce the superconducting/optical/magnetic properties and chirality of 2D materials along with newly emerging magic angle 2D superlattices. Following that, the promising applications of 2D materials in electronics, optoelectronics, catalysis, energy storage, solar cells, biomedicine, sensors, environments, etc. are described sequentially. Thereafter, we present the theoretic calculations and simulations of 2D materials. Finally, after concluding the current progress, we provide some personal discussions on the existing challenges and future outlooks in this rapidly developing field.

关键词： Two-dimensional materials Transition metal dichalcogenides Phase engineering of nanomaterials Electronics Optoelectronics Catalysis Energy storage and conversion

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Model-Based Reinforcement Learning for Quantized Federated Learning Performance Optimization

Model-Based Reinforcement Learning for Quantized Federated L...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Christopher G. Brinton Changchuan Yin Walid Saad Shuguang Cui Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory Of Networking And Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Bradley Department of Electrical and Computer Engineering Virginia Tech Arlington VA USA Shenzhen Research Institute of Big Data (SRIBD) and the Future Network of Intelligence Institute (FNii) Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781665435413

This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which, in turn, aggregates them into a quantized global model and synchronizes the devices. With the goal of jointly determining the set of participating devices in each training iteration and the bitwidths employed at the devices, we pose an optimization problem for minimizing the training loss of quantized FL under a device sampling budget and delay requirement. Our analytical results show that the improvement of FL training loss between two consecutive iterations depends on not only the device selection and quantization scheme, but also on several parameters inherent to the model being learned. As a result, we propose, a model-based reinforcement learning (RL) method to optimize action selection over iterations. Compared to model-free RL, the proposed approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead. Numerical evaluations show that the proposed FL framework can achieve the same classification performance while reducing the number of training iterations needed for convergence by 20% compared to model-free RL-based FL.

关键词： Training Performance evaluation Wireless communication Quantization (signal) Computational modeling Reinforcement learning mathematical models

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Automated identification and quantification of myocardial inflammatory infiltration in digital histological images to diagnose myocarditis

arXiv

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arXiv 2023年

作者： Liu, Yanyun Hua, Xiumeng Zhu, Shouping Wang, Congrui Chen, Xiao Shi, Yu Song, Jiangping Zhou, Weihua School of Life Science and Technology Xidian University Engineering Research Center of Molecular and Neuro Imaging Ministry of Education Shaanxi Xi'An China Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment School of Life Science and Technology Xidian University Shaanxi Xi'An China Innovation Center for Advanced Medical Imaging and Intelligent Medicine Guangzhou Institute of Technology Xidian University Guangdong Guangzhou China Beijing Key Laboratory of Preclinical Research and Evaluation for Cardiovascular Implant Materials Animal Experimental Centre Fuwai Hospital National Centre for Cardiovascular Disease Chinese Academy of Medical Sciences Peking Union Medical College Beijing China State Key Laboratory of Cardiovascular Disease Fuwai Hospital National Center for Cardiovascular Diseases Chinese Academy of Medical Sciences Peking Union Medical College 167A Beilishi Road Xi Cheng District Beijing China Department of Cardiovascular Surgery Fuwai Hospital National Center for Cardiovascular Diseases National Clinical Research Center of Cardiovascular Diseases State Key Laboratory of Cardiovascular Disease Chinese Academy of Medical Sciences Peking Union Medical College Beijing China The Cardiomyopathy Research Group Fuwai Hospital Beijing China Department of Applied Computing Michigan Technological University HoughtonMI United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI United States

Background Given the dimensionality of WSIs and the increase in the number of potential cases, the manual histological diagnosis of myocarditis based on Dallas Criteria is time-consuming, experience-dependent. Objectives To overcome the limitations of manual diagnosis methods, this study aims to develop a new computational pathology approach that automates the identification and quantification of myocardial inflammatory infiltration in digital hematoxylin and eosin (HE)-stained images to assist pathologists in diagnosing myocarditis. Methods 898 HE-stained whole slide images (WSIs) of myocardium from 154 heart transplant patients diagnosed with myocarditis or dilated cardiomyopathy (DCM) were included in this study. An automated deep-learning (DL)-based computational pathology approach was developed to identify nuclei and detect myocardial inflammatory infiltration, enabling the quantification of the lymphocyte nuclear density (LND) on myocardial WSIs. A cutoff value based on the quantification of LND was proposed to determine if the myocardial inflammatory infiltration was present. The performance of our approach was evaluated with a five-fold cross-validation experiment, tested with an internal test set from the myocarditis group, and confirmed by an external test from a double-blind trial group. Results An LND of 1.02/mm2 could distinguish WSIs with myocarditis from those without. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) in the five-fold cross-validation experiment were 0.899 ± 0.035, 0.971 ± 0.017, 0.728 ± 0.073 and 0.849 ± 0.044, respectively. For the internal test set, the accuracy, sensitivity, specificity, and AUC were 0.887, 0.971, 0.737, and 0.854, respectively. The accuracy, sensitivity, specificity, and AUC for the external test set reached 0.853, 0.846, 0.858, and 0.852, respectively. Conclusion Our new approach provides accurate and reliable quantification of the LND of myocardial WSIs bas

关键词： Pathology

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Multi-sensor Fusion Detection Method for Vehicle Target Based on Kalman Filter and Data Association Filter 7th

Multi-sensor Fusion Detection Method for Vehicle Target Base...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Duan, Xuting Sun, Chengming Tian, Daxin Zheng, Kunxian Zhou, Gang E, Wenjuan Zhang, Yundong Beijing Advanced Innovation Center for Big Data and Brain Computing School of Transpotation Science and Engineering Beihang University Beijing100191 China Research Institute of Highway Ministry of Transport of the People’s Republic of China Beijing100088 China School of Rail Transportation Soochow University Suzhou215000 China Suzhou Automotive Research Institute Tsinghua University Suzhou215000 China State Key Laboratory of Digital Multimedia Chip Technology Chongqing Vimicro AI Chip Technology Co. Ltd. Chongqing400722 China

ISBN: (纸本)9783030786175

Multi-sensor data fusion is an emerging technology, which has been widely used in medical diagnosis, remote sensing, inertial navigation and many other fields. What’s more, the implementation and application of automatic driving system rely heavily on target detection technology. Due to the high mobility and unpredictability of vehicle-mounted equipment, for automatic vehicles, it is arduous to achieve real-time and accurate vehicle target detection by a single sensor means, thus it is difficult to reliably guarantee the safety and stability. This paper proposes a novel object detection method based on a multi-sensor fusion mechanism, which considers the real-time sensing data from two types of sensors including radar and camera. It collects multi-vehicle speed and position information efficiently and reliably. Then, it filters and integrates data according to Extended Kalman Filter, Data Association Filter and some other methods. Furthermore, vehicle-borne equipment makes intelligent decision based on the data. In addition to theoretical support, the designed simulation results also show that the multi-sensor fusion mechanism can detect target vehicles efficiently and accurately, and it has superiority in the stability and accuracy of perception than single sensor sensing method. © 2021, Springer Nature Switzerland AG.

关键词： Extended Kalman filters

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A Robust Deep Learning-Based DOA Super-Resolution Estimation Method With Single Snapshot

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IEEE Transactions on Vehicular Technology 2025年

作者： Luo, Tao Chen, Peng Yang, Zihan Zheng, Le Zhang, Yudong Liu, Jun Southeast University State Key Laboratory of Millimeter Waves Nanjing210096 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China University of Leicester School of Computing and Mathematical Sciences LeicesterLE1 7RH United Kingdom Army Engineering University of PLA National Key Laboratory on Electromagnetic Environment Effects and Electro-optical Engineering Nanjing210007 China

At present, deep learning (DL) has attracted widespread attention and has been widely applied in different areas of wireless communication, and signal processing. Due to its excellent feature extraction capability, it has been extensively used in the direction of arrival (DOA) estimation. In this paper, a robust network for super-resolution single-snapshot DOA estimation called DeepDOA is proposed. Different from other network-based DOA algorithms, DeepDOA takes the normalized real and imaginary parts of the received signal as inputs. Meanwhile, the network output is a vector instead of a spatial spectrum, and the corresponding spatial spectrum can be obtained by a simple transformation, which makes the spectrum smoother. Moreover, a sparse constraint is first introduced into the loss function, which improves the accuracy of the algorithm. Numerical simulations are conducted to verify its excellent performance. Finally, a frequency-modulated continuous wave (FMCW) radar location system is built to validate the proposed algorithm. © 1967-2012 IEEE.

关键词： Frequency modulation

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Variational Quantum Algorithm and Its Application on Non-Linear Equations

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Journal of Physics: Conference Series 2021年第1期1883卷

作者： Yihang Yang Zheng Shan Bo Zhao Le Xu School of Information Engineering Zhengzhou University Zhengzhou Henan 450000 China State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou Henan 450000 China

Quantum algorithms of factoring problem have been paid more and more attention since Shor's algorithm was proposed. Combining the current quantum computer hardware level and integer factorization quantum algorithms, this paper proposes a 'classical + quantum' hybrid solution. This scheme first adopts classical methods and corresponding rules in the preprocessing step to simplify the nonlinear equations, which is used to reduce the number of qubits needed for the cost Hamiltonian. Then the variational quantum algorithm is used to find the approximate ground state of the cost Hamiltonian, which encodes the solution of the nonlinear equations. The program was verified on the IBM QX4 quantum machine, and the results showed that this hybrid solution can effectively reduce the quantum resources required to solve the nonlinear equations.

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Research on Quantum Programming Framework Based on NISQ Function

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Journal of Physics: Conference Series 2021年第1期1827卷

作者： Xiaonan Liu Le Xu Chunchao Hu Lei Jin State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou Henan 450000 China School of Information Engineering Zhengzhou University Zhengzhou Henan 450000 China

In the past two decades, the field of quantum computing has developed rapidly, is gradually able to solve some difficult classical problems. With the development of the facilities for Noisy Intermediate-Scale Quantum(NISQ), the NISQ technology addresses the existing Quantum programming languages or frameworks, suggesting that they cannot fully meet the requirements of Quantum computers. In recent years, some quantum programming languages or frameworks have emerged which claim to support the classical computing algorithms of heterogeneous quantum. However, the code is too cumbersome and redundant, and it is difficult to map the code to heterogeneous quantum classical computation while meeting the requirements of timing. Therefore, a modular programming framework of heterogeneous quantum classical computing algorithm with NISQ function is proposed. The framework optimizes the quantum code and retains the quantum classical interactions that satisfy the temporal constraints.

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Demonstration of topological wireless power transfer

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Science Bulletin 2021年第10期66卷 974-980,M0003页

作者： Li Zhang Yihao Yang Zhao Jiang Qiaolu Chen Qinghui Yan Zhouyi Wu Baile Zhang Jiangtao Huangu Hongsheng Chen Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical InstrumentationCollege of Information Science and Electronic EngineeringZhejiang UniversityHangzhou 310027China ZJU-Hangzhou Global Science and Technology Innovation Center Key Laboratory of Advanced Micro/Nano Electronic Devices&Smart Systems of ZhejiangZhejiang UniversityHangzhou 310027China International Joint Innovation Center ZJU-UIUC InstituteZhejiang UniversityHaining 314400China Division of Physics and Applied Physics School of Physical and Mathematical SciencesNanyang Technological UniversitySingapore 637371Singapore Centre for Disruptive Photonic Technologies The Photonics InstituteNanyang Technological UniversitySingapore 639798Singapore Laboratory of Applied Research on Electromagnetics(ARE) Zhejiang UniversityHangzhou 310027China

Recent advances in non-radiative wireless power transfer(WPT)technique essentially relying on magnetic resonance and near-field coupling have successfully enabled a wide range of ***,WPT systems based on double resonators are severely limited to short-or mid-range distance,due to the deteriorating efficiency and power with long transfer *** systems based on multi-relay resonators can overcome this problem,which,however,suffer from sensitivity to perturbations and fabrication ***,we experimentally demonstrate a concept of topological wireless power transfer(TWPT),where energy is transferred efficiently via the near-field coupling between two topological edge states localized at the ends of a one-dimensional radiowave topological *** a TWPT system can be modelled as a parity-time-symmetric Su-Schrieffer-Heeger(SSH)chain with complex boundary ***,the coil configurations are judiciously designed,which significantly suppress the unwanted cross-couplings between nonadjacent coils that could break the chiral symmetry of the SSH *** tuning the inter-and intra-cell coupling strengths,we theoretically and experimentally demonstrate high energy transfer efficiency near the exceptional point of the topological edge states,even in the presence of *** combination of topological metamaterials,non-Hermitian physics,and WPT techniques could promise a variety of robust,efficient WPT applications over long distances in electronics,transportation,and industry.

关键词： Wireless power transfer Topological metamaterials Exceptional point Su-Schrieffer-Heeger model

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A Herculean task: Classical simulation of quantum computers

arXiv

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arXiv 2023年

作者： Xu, Xiaosi Benjamin, Simon Sun, Jinzhao Yuan, Xiao Zhang, Pan Graduate School of China Academy of Engineering Physics Beijing100193 China Department of Materials University of Oxford Parks Road OxfordOX1 3PH United Kingdom QOLS Blackett Laboratory Imperial College London LondonSW7 2AZ United Kingdom Center on Frontiers of Computing Studies Peking University Beijing100871 China School of Computer Science Peking University Beijing100871 China CAS Key Laboratory for Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing100190 China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS Hangzhou310024 China International Center for Theoretical Physics Asia-Pacific Hangzhou Beijing China

In the effort to develop useful quantum computers, simulating quantum machines with conventional computing resources is a key capability. Such simulations will always face limits, preventing the emulation of quantum computers of substantial scale;but by pushing the envelope as far as possible through optimal choices of algorithms and hardware, the value of the simulator tool is maximized. This work reviews the state-of-the-art numerical simulation methods, i.e., the classical algorithms that emulate quantum computer evolution under specific operations. We focus on the mainstream state-vector and tensor-network paradigms, while briefly mentioning alternative methods. Moreover, we review the diverse applications of simulation across different facets of quantum computer development, such as understanding the fundamental difference between quantum and classical computations, exploring algorithm design spaces for quantum advantage, predicting quantum processor performance at the design stage, and characterizing fabricated devices efficiently for fast iterations. This review complements recent surveys on today’s tools and implementations;here, we seek to acquaint the reader with an essential understanding of the theoretical basis of classical simulations, detailed discussions on the advantages and limitations of different methods, and the demands and challenges arising from practical use cases. Copyright © 2023, The Authors. All rights reserved.

关键词： Numerical methods

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Higher-Order Quantum Genetic Algorithm and Its Application on Non-Linear Equations

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Journal of Physics: Conference Series 2021年第1期1827卷

作者： Lei Jin Zheng Shan Chunchao Hu Le Xu School of Information Engineering Zhengzhou University Zhengzhou Henan 450000 China State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou Henan 450000 China

The high efficiency of quantum algorithms is caused by the quantum parallelism of the superposition principle and the quantum entangled, but traditional quantum genetic algorithms only use the quantum superposition principle. In order to further improve the performance of the algorithm, this paper proposes a new higher-order quantum genetic algorithm, which adds quantum entanglement properties on the basis of the principle of quantum superposition. Finally, the traditional quantum genetic algorithm and the higher-order quantum genetic algorithm are used to test a set of nonlinear equations many times. The results show that compared with the traditional quantum genetic algorithm, the evolutionary update operation of the higher-order quantum genetic algorithm does not involve multiple judgment conditions of the traditional quantum gate, requires less evolutionary algebra, and can converge quickly.

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