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arXiv 2020年

作者： Jiao, Yuheng He, Yuchen R. Kandel, Mikhail E. Liu, Xiaojun Lu, Wenlong Popescu, Gabriel Quantitative Light Imaging Laboratory Department of Electrical and Computer Engineering Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign IL61801 United States State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical and Engineering Huazhong University of Science and Technology Wuhan430074 China

Quantitative phase imaging (QPI) has been widely applied in characterizing cells and tissues. Spatial light interference microscopy (SLIM) is a highly sensitive QPI method, due to its partially coherent illumination and common path interferometry geometry. However, SLIM’s acquisition rate is limited because of the four-frame phase-shifting scheme. On the other hand, off-axis methods like diffraction phase microscopy (DPM), allows for single-shot QPI. However, the laser-based DPM system is plagued by spatial noise due to speckles and multiple reflections. In a parallel development, deep learning was proven valuable in the field of bioimaging, especially due to its ability to translate one form of contrast into another. Here, we propose using deep learning to produce synthetic, SLIM-quality, high-sensitivity phase maps from DPM, single-shot images as input. We used an inverted microscope with its two ports connected to the DPM and SLIM modules, such that we have access to the two types of images on the same field of view. We constructed a deep learning model based on U-net and trained on over 1,000 pairs of DPM and SLIM images. The model learned to remove the speckles in laser DPM and overcame the background phase noise in both the test set and new data. Furthermore, we implemented the neural network inference into the live acquisition software, which now allows a DPM user to observe in real-time an extremely low-noise phase image. We demonstrated this principle of computational interference microscopy (CIM) imaging using blood smears, as they contain both erythrocytes and leukocytes, in static and dynamic conditions. © 2020, CC BY-NC-ND.

关键词： Speckle

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Binary Channel Fuzzy Self-Adjusted Neural Network for Solving Time-Changing QP Problems

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IEEE Transactions on Neural Networks and Learning systems 2025年 PP卷 PP页

作者： Luo, Yamei Ren, Qingyi Zheng, Zihao Chen, Siyuan Ma, Xin Liu, Yu Li, Xiaoli Yu, Junzhi Zhang, Zhijun The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Shatin Hong Kong South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southern University of Science and Technology Institute for Robotics Shenzhen518055 China Peking University College of Engineering Beijing100871 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Laboratory Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong 723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

A novel binary channel fuzzy self-adjusted neural network (BCF-SANN) is proposed and researched for solving time-changing quadratic programming (QP) problems in this article. Unlike the fixed parameters of the typical zeroing neural network, the main parameters of the proposed BCF-SANN are time-changing, and its errors are adaptively quickly convergent. The biggest advantage of the novel neural network is that it combines a fuzzy self-adjusted controller, which takes the errors and derivatives of errors as fuzzy inputs and neural networks, further improving the convergence and robustness of the neural networks. To design the novel neural network, a time-changing QP problem is first established;then, using Lagrange’s law, the time-changing QP problem is transformed into a time-changing matrix equation;and finally, based on the time-changing parameter neural dynamics method, a novel BCF-SANN is proposed. The detailed design process is given in this article, and the convergence and robustness of the proposed BCF-SANN are proved by theoretical analysis. Through comparative experiments, it is demonstrated that the proposed BCF-SANN has a faster convergence rate and stronger robustness than the traditional zeroing neural network and 1-D fuzzy recurrent neural network (RNN). © 2012 IEEE.

关键词： Recurrent neural networks

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Model based reinforcement learning for robot grasping trajectory generation 4

Model based reinforcement learning for robot grasping trajec...

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4th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2019

作者： Xue, Hongxiang Wen, Shuhuan Yang, Chao Liu, Huaping Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China Department of Computer Science and Technology State Key Laboratory of Intelligent Technology and System Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)9781728100647

Trajectory generation is a fundamental problem for successful robotic grasping. However, most of the existing work dealt with this problem using supervised learning with a prescribed model. It prevents the developed grasp strategies from being used for new unknown scenarios. In this paper, inspired by the success of the reinforcement learning method, we developed a new method which optimizes grasp trajectories using a policy search method. An important advantage of the proposed method is that the complicated 3D model could be used to generate the multi-dimensional grasp configurations. The developed algorithm has been validated on some typical robotic hand. The experimental results show that the proposed method achieves excellent performance. © 2019 IEEE.

关键词： Trajectories

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The Deep-Time digital Earth program: data-driven discovery in geosciences

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National Science Review 2021年第9期8卷 156-166页

作者： Chengshan Wang Robert M.Hazen Qiuming Cheng Michael H.Stephenson Chenghu Zhou Peter Fox Shu-zhong Shen Roland Oberhansli Zengqian Hou Xiaogang Ma Zhiqiang Feng Junxuan Fan Chao Ma Xiumian Hu Bin Luo Juanle Wang Craig M.Schiffries State Key Laboratory of Biogeology and Environmental Geology China University of Geosciences School of the Earth Science and Resources China University of Geosciences Earth and Planets Laboratory Carnegie Institution for Science State Key Laboratory of Geological Processes and Mineral Resources China University of Geosciences British Geological Survey State Key Laboratory of Resources and Environment Information System Institute of Geographical Science and Natural Resources Chinese Academy of Sciences Tetherless World Constellation Rensselaer Polytechnic Institute School of Earth Sciences and Engineering Nanjing University Institute of Earth and Environmental Sciences University of Potsdam Institute of Geology Chinese Academy of Geological Sciences Department of Computer Science University of Idaho Petroleum Exploration and Production Research Institute SINOPEC DDE Center of Excellence(Suzhou)

Current barriers hindering data-driven discoveries in deep-time Earth(DE) include: substantial volumes of DE data are not digitized; many DE databases do not adhere to FAIR(findable, accessible, interoperable and reusable) principles; we lack a systematic knowledge graph for DE; existing DE databases are geographically heterogeneous; a significant fraction of DE data is not in open-access formats; tailored tools are needed. These challenges motivate the Deep-Time digital Earth(DDE) program initiated by the International Union of Geological Sciences and developed in cooperation with national geological surveys,professional associations, academic institutions and scientists around the world. DDE's mission is to build on previous research to develop a systematic DE knowledge graph, a FAIR data infrastructure that links existing databases and makes dark data visible, and tailored tools for DE data, which are universally accessible. DDE aims to harmonize DE data, share global geoscience knowledge and facilitate data-driven discovery in the understanding of Earth's evolution.

关键词： Deep-Time digital Earth data-driven discovery big data Earth evolution cyberinfrastructure

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Improving read performance of lsm-tree based KV stores via dual grained caches 21

Improving read performance of lsm-tree based KV stores via d...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and systems, HPCC/SmartCity/DSS 2019

作者： Li, Xiang Xu, Guangping Fan, Hao Lu, Hongli Tang, Bo Xue, Yanbing Zong, Ziliang School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Department of Computer Science Texas State University San MarcosTX United States

ISBN: (纸本)9781728120584

Key-value (KV) stores based on the Log-Structure Merge tree (LSM-tree) have been widely used in modern storage systems (e.g. LevelDB and RocksDB) to achieve high write throughput. However, conventional LSM-tree design has high latency for random read operations, especially in the workloads with concurrent read and write operations. This paper proposes a dual-grained cache scheme to improve the performance of LSM-tree based KV stores, in which a coarse-grained cache improves read performance by leveraging spatial locality and a fine-grained cache tracks the frequently accessed KV pairs. We implement the dual-grained caching on a LSM-tree simulator and evaluate its performance using various Yahoo! Cloud Service Benchmark (YCSB) workloads. Our experimental results show that the proposed way of dual-grained caches can significantly improve the read performance of KV stores without sacrificing the advantages of the LSM-tree for write-intensive workloads. © 2019 IEEE.

关键词： Compaction Throughput Conferences Indexes computer science Benchmark testing Distributed databases

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Topology inference for multi-agent cooperation under unmeasurable latent input

arXiv

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arXiv 2020年

作者： Jiao, Qing Li, Yushan He, Jianping Shi, Ling Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

Topology inference is a crucial problem for cooperative control in multi-agent systems. Different from most prior works, this paper is dedicated to inferring the directed network topology from the observations that consist of a single, noisy and finite time-series system trajectory, where the cooperation dynamics is stimulated with the initial network state and the unmeasurable latent input. The unmeasurable latent input refers to intrinsic system signal and extrinsic environment interference. Considering the time-invariant/varying nature of the input, we propose two-layer optimization-based and iterative estimation-based topology inference algorithms (TO-TIA and IE-TIA), respectively. TO-TIA allows us to capture the separability of global agent state and eliminates the unknown influence of time-invariant input on system dynamics. IE-TIA further exploits the identifiability and estimability of more general time-varying input and provides an asymptotic solution with desired convergence properties, with higher computation cost compared with TO-TIA. Our novel algorithms relax the dependence of observation scale and leverage the empirical risk reformulation to improve the inference accuracy in terms of the topology structure and edge weight. Comprehensive theoretical analysis and simulations for various topologies are provided to illustrate the inference feasibility and the performance of the proposed algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Multi agent systems

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Energy efficient robust beamforming and cooperative jamming design for IRS-Assisted MISO networks

arXiv

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arXiv 2020年

作者： Wang, Qun Zhou, Fuhui Hu, Rose Qingyang Qian, Yi Department of Electrical and Computer Engineering Utah State University LoganUT84322 United States College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing210000 China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space of Nanjing University of Aeronautics and Astronautics China Department of Electrical and Computer Engineering University of Nebraska–Lincoln OmahaNE68182 United States

Energy-efficient design and secure communications are of crucial importance in wireless communication networks. However, the energy efficiency achieved by using physical layer security can be limited by the channel conditions. In order to tackle this problem, an intelligent reflecting surface (IRS) assisted multiple input single output (MISO) network with independent cooperative jamming is studied. The energy efficiency is maximized by jointly designing the transmit and jamming beamforming and IRS phase-shift matrix under both the perfect channel state information (CSI) and the imperfect CSI. In order to tackle the challenging non-convex fractional problems, an algorithm based on semidefinite programming (SDP) relaxation is proposed for solving energy efficiency maximization problem under the perfect CSI case while an alternate optimization algorithm based on S-procedure is used for solving the problem under the imperfect CSI case. Simulation results demonstrate that the proposed design outperforms the benchmark schemes in term of energy efficiency. Moreover, the tradeoff between energy efficiency and the secrecy rate is found in the IRS-assisted MISO network. Furthermore, it is shown that IRS can help improve energy efficiency even with the uncertainty of the CSI. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy efficiency

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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Ultrahigh-Q AlGaAs-on-insulator microresonators for integrated nonlinear photonics

arXiv

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arXiv 2020年

作者： Xie, Weiqiang Chang, Lin Shu, Haowen Norman, Justin C. Peters, Jon D. Wang, Xingjun Bowers, John E. Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States State Key Laboratory of Advanced Optical Communications System and Networks Department of Electronics School of Electronics Engineering and Computer Science Peking University Beijing100871 China Institute for Energy Efficiency University of California Santa BarbaraCA93106 United States

Aluminum gallium arsenide (AlGaAs) and related III-V semiconductors have excellent optoelectronic properties. They also possess strong material nonlinearity as well as high refractive indices. In view of these properties, AlGaAs is a promising candidate for integrated photonics, including both linear and nonlinear devices, passive and active devices, and associated applications. For integrated photonics low propagation loss is essential, particularly in nonlinear applications. However, achieving low-loss and high-confinement AlGaAs photonic integrated circuits poses a challenge. Here we show an effective reduction of surface-roughness-induced scattering loss in fully etched high-confinement AlGaAs-on-insulator nanowaveguides, by using a heterogeneous wafer-bonding approach and optimizing fabrication techniques. We demonstrate ultrahigh-quality AlGaAs microring resonators and realize quality factors up to 3.52×106 and finesses as high as 1.4×104. We also show ultra-efficient frequency comb generations in those resonators and achieve record-low threshold powers on the order of ~20 µW and ~120 µW for the resonators with 1 THz and 90 GHz free-spectral ranges, respectively. Our result paves the way for the implementation of AlGaAs as a novel integrated material platform specifically for nonlinear photonics, and opens a new window for chip-based efficiency-demanding practical applications. Copyright © 2020, The Authors. All rights reserved.

关键词： III-V semiconductors

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Adaptive Dynamic Surface Control of Constrained Nonlinear systems

Adaptive Dynamic Surface Control of Constrained Nonlinear Sy...

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第32届中国控制与决策会议

作者： Ailiang Xing Biao Song Yixin Yin School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Demonstration and Research Department of Network Information System Beijing Institute of Computer Application

This paper presents an adaptive dynamic surface control for uncertain nonlinear strict feedback systems with constraints on both input and *** technology is employed in the controller *** performance method with asymmetric barrier Lyapunov function(BLF) is used to deal with the output *** input saturation is solved by using the Nussbaum *** dynamic surface control(DSC) technique is employed to reduce the complexity of the virtual *** is illustrated that all closed-loop signals are semi-global uniformly ultimately bounded(SGUUB).Finally,a simulation example proves the proposed results.

关键词： Input saturation Output constraints Nussbaum function Dynamic surface control

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