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NATURE MICROBIOLOGY 2021年第5期6卷 697-698页

作者： Wang, Panpan Liu, Lu Liu, Aijun Yan, Liming He, Yong Shen, Shu Hu, Mingxu Guo, Yu Liu, Haiguang Liu, Chuang Lu, Yinying Wang, Peiyi Deng, Fei Rao, Zihe Lou, Zhiyong MOE Key Laboratory of Protein Science and Collaborative Innovation Center of Biotherapy School of Medicine Tsinghua University Beijing China School of Life Sciences Tsinghua University Beijing China State Key Laboratory of Medicinal Chemical Biology College of Life Science Nankai University Tianjin China Drug Discovery Center for Infectious Disease College of Pharmacy Nankai University Tianjin China National Laboratory of Macromolecules Institute of Biophysics Chinese Academy of Sciences Beijing China State Key Laboratory of Virology and National Virus Resource Center Wuhan Institute of Virology Center for Biosafety Mega-Science Chinese Academy of Sciences Hubei China Complex Systems Division Beijing Computational Science Research Center Beijing China Department of Biology Southern University of Science and Technology Shenzhen China 302 Hospital Peking University Beijing China

Two dimensional vertex-decorated Lieb lattice with exact mobility edges and robust flat bands

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arXiv 2022年

作者： Wang, Yucheng Zhang, Long Wan, Yuhao He, Yu Wang, Yongjian Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China International Quantum Academy Shenzhen518048 China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of Physics Institute for Quantum Science and Engineering Huazhong University of Science and Technology Wuhan430074 China International Center for Quantum Materials School of Physics Peking University Beijing100871 China School of Mathematical Sciences Laboratory of Mathematics and Complex Systems MOE Beijing Normal University Beijing100875 China

The mobility edge (ME) that marks the energy separating extended and localized states is a most important concept in understanding the metal-insulator transition induced by disordered or quasiperiodic potentials. MEs have been extensively studied in three dimensional disorder systems and one-dimensional quasiperiodic systems. However, the studies of MEs in two dimensional (2D) systems are rare. Here we propose a class of 2D vertex-decorated Lieb lattice models with quasiperiodic potentials only acting on the vertices of the Lieb lattice or extended Lieb lattices. By mapping these models to the 2D Aubry-André model, we obtain exact expressions of MEs and the localization lengths of localized states, and further demonstrate that the flat bands remain unaffected by the quasiperiodic potentials. Finally, we propose a highly feasible scheme to experimentally realize our model in a quantum dot array. Our results open the door to studying and realizing exact MEs and robust flat bands in 2D systems. © 2022, CC BY.

关键词： Metal insulator transition

MixedFusion: 6D Object Pose Estimation from Decoupled RGB-Depth Features

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MixedFusion: 6D Object Pose Estimation from Decoupled RGB-De...

International Conference on Pattern Recognition

作者： Hangtao Feng Lu Zhang Xu Yang Zhiyong Liu The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences

Estimating the 6D pose of objects is an important process for intelligent systems to achieve interaction with the real-world. As the RGB-D sensors become more accessible, the fusion-based methods have prevailed, since the point clouds provide complementary geometric information with RGB values. However, due to the difference in feature space between color image and depth image, the network structures that directly perform point-to-point matching fusion do not effectively fuse the features of the two. In this paper, we propose a simple but effective approach, named MixedFusion. Different from the prior works, we argue that the spatial correspondence of color and point clouds could be decoupled and reconnected, thus enabling a more flexible fusion scheme. By performing the proposed method, more informative points can be mixed and fused with rich color features. Extensive experiments are conducted on the challenging LineMod and YCB-Video datasets, which shows that our method significantly boosts the performance without introducing extra overheads. Furthermore, when the minimum tolerance of metric narrows, the proposed approach performs better for the high-precision demands.

关键词： Measurement Three-dimensional displays Image color analysis Fuses Pose estimation Color Pattern recognition

A pigeon-inspired optimization algorithm for many-objective optimization problems

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science China(Information sciences) 2019年第7期62卷 131-138页

作者： Zhihua CUI Jiangjiang ZHANG Yechuang WANG Yang CAO Xingjuan CAI Wensheng ZHANG Jinjun CHEN Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Beijing Institute of Petrochemical Technology Faculty of Information Technology Beijing University of Technology State Key Laboratory of Intelligent Control and Management of Complex Systems Institute of Automation Chinese Academy of Sciences School of Software University of Technology Sydney

Dear editor,Swarm intelligence optimization algorithms are inspired by the behaviour of biological groups in nature. Such algorithms have the advantages of a clear structure, simple operation, comprehensible principles, strong parallelism, effective search abilities, and strong robustness. They can effectively solve difficult problems that traditional methods cannot. Pigeon-inspired optimization (PIO), a novel biomimetic swarm intelligence optimization algorithm, was proposed by Duan and Qiao in

关键词： A pigeon-inspired optimization algorithm for many-objective optimization problems Pareto

Embedding Theory of Reservoir Computing and Reducing Reservoir Network Using Time Delays

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arXiv 2023年

作者： Duan, Xing-Yue Ying, Xiong Leng, Si-Yang Kurths, Jürgen Lin, Wei Ma, Huan-Fei School of Mathematical Sciences Soochow University Suzhou215006 China School of Mathematical Sciences SCMS and SCAM Fudan University Shanghai200433 China Research Institute of Intelligent Complex Systems Centre for Computational Systems Biology Fudan University Shanghai200433 China Academy for Engineering and Technology Fudan University Shanghai200433 China State Key Laboratory of Medical Neurobiology LCNBI MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University Shanghai200032 China Potsdam14473 Germany Shanghai Artificial Intelligence Laboratory Shanghai200232 China

Reservoir computing (RC), a particular form of recurrent neural network, is under explosive development due to its exceptional efficacy and high performance in reconstruction or/and prediction of complex physical systems. However, the mechanism triggering such effective applications of RC is still unclear, awaiting deep and systematic exploration. Here, combining the delayed embedding theory with the generalized embedding theory, we rigorously prove that RC is essentially a high dimensional embedding of the original input nonlinear dynamical system. Thus, using this embedding property, we unify into a universal framework the standard RC and the time-delayed RC where we novelly introduce time delays only into the network’s output layer, and we further find a trade-off relation between the time delays and the number of neurons in RC. Based on these findings, we significantly reduce the RC’s network size and promote its memory capacity in completing systems reconstruction and prediction. More surprisingly, only using a single-neuron reservoir with time delays is sometimes sufficient for achieving reconstruction and prediction tasks, while the standard RC of any large size but without time delay cannot complete them yet. © 2023, CC BY.

关键词： Time delay

Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting

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arXiv 2022年

作者： Gao, Jiaqi Huang, Zhizhong Lei, Yiming Shan, Hongming Wang, James Z. Wang, Fei-Yue Zhang, Junping The Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China The Institute of Science and Technology for Brain-inspired Intelligence MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China The Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai201210 China The College of Information Sciences and Technology The Pennsylvania State University University ParkPA16802 United States The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Institute of Systems Engineering Macau University of Science and Technology China Qingdao Academy of Intelligent Industries Qingdao266109 China

Most conventional crowd counting methods utilize a fully-supervised learning framework to establish a mapping between scene images and crowd density maps. They usually rely on a large quantity of costly and time-intensive pixel-level annotations for training supervision. One way to mitigate the intensive labeling effort and improve counting accuracy is to leverage large amounts of unlabeled images. This is attributed to the inherent self-structural information and rank consistency within a single image, offering additional qualitative relation supervision during training. Contrary to earlier methods that utilized the rank relations at the original image level, we explore such rank-consistency relation within the latent feature spaces. This approach enables the incorporation of numerous pyramid partial orders, strengthening the model representation capability. A notable advantage is that it can also increase the utilization ratio of unlabeled samples. Specifically, we propose a Deep Rank-consistEnt pyrAmid Model (DREAM), which makes full use of rank consistency across coarse-to-fine pyramid features in latent spaces for enhanced crowd counting with massive unlabeled images. In addition, we have collected a new unlabeled crowd counting dataset, FUDAN-UCC, comprising 4,000 images for training purposes. Extensive experiments on four benchmark datasets, namely UCF-QNRF, ShanghaiTech PartA and PartB, and UCF-CC-50, show the effectiveness of our method compared with previous semi-supervised methods. The codes are available at https://***/bridgeqiqi/DREAM. Copyright © 2022, The Authors. All rights reserved.

关键词： Image enhancement

Necessary and sufficient conditions on existence of radial solutions for exterior Dirichlet problem of fully nonlinear elliptic equations

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arXiv 2022年

作者： Dai, Limei Bao, Jiguang Wang, Bo School of Mathematics and Information Science Weifang University Weifang261061 China School of Mathematical Sciences Beijing Normal University Laboratory of Mathematics and Complex Systems Ministry of Education Beijing100875 China School of Mathematics and Statistics Beijing Institute of Technology Beijing100081 China

In this paper, we study the exterior Dirichlet problem for the fully nonlinear elliptic equation f(λ(D2u)) = 1. We obtain the necessary and sufficient conditions of existence of radial solutions with prescribed asymp... 详细信息

关键词： Nonlinear equations

The Quality Evaluation of Academic Papers in Carbon Capture, Utilization, and Storage

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The Quality Evaluation of Academic Papers in Carbon Capture,...

Machine Learning, Control, and Robotics (MLCR), International Conference on

作者： Hangyue Zhang Jie Bai School of Computer Science and Technology Sun Yat-sen University Guangzhou P. R. China City State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing P. R. China

ISBN: (纸本)9781665454605

Carbon capture, utilization, and storage (CCUS) technology is a research hotspot worldwide owing to global climate change and warm gas control. CCUS papers from the last twenty years, derived from the Web of science database, were analyzed to investigate the impact and citation of academic papers using machine learning methods. Firstly, nine author characteristics, keywords, and three other categories were extracted from the literature. Then, the natural logarithm distribution of the annual average number of citations was obtained, and five evaluation grades were decided based on the citations. Next, the keyword evolution of papers in the CCUS field is analyzed, and a model on the keyword evolution trend index is proposed. Data integration and imbalance processing were conducted after random sampling. A citation prediction model was proposed to evaluate the quality of CCUS academic papers and was verified on the recent five years' CCUS papers by comparing it with nine other mainstream machine learning models. The results indicate that the random forest model exhibits the best performance, with an accuracy rate reaching 88.7%; an ablation experiment was also conducted using the same algorithm. The results prove the effectiveness of our proposed method, shedding light on the quality of academic papers and technology development in CCUS.

关键词： Machine learning Climate change Random forests Modeling Energy storage

The exterior Dirichlet problems of Hessian quotient equations

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arXiv 2022年

In this paper, we study the Dirichlet problem of Hessian quotient equations in exterior domains. By estimating the eigenvalues of the solution, the necessary and sufficient conditions on existence of radial solutions are obtained. Applying the solutions of ODE, the viscosity subsolutions and supersolutions are constructed and then the existence of viscosity solutions for exterior problems is established by the Perron's method. © 2022, CC BY.

关键词： Viscosity

Graph-based reinforced multi-objective optimization for distributed heterogeneous flexible job shop scheduling problem under nonidentical time-of-use electricity tariffs

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Expert systems with Applications 2025年 290卷

作者： Qichen Zhang Weishi Shao Zhongshi Shao Dechang Pi School of Computer and Electronic Information/School of Artificial Intelligence Nanjing Normal University Nanjing China Key Laboratory of Numerical Simulation for Large Scale Complex Systems Ministry of Education China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Computer Science Shaanxi Normal University Xi’an China

With the rapid development of industrial globalization and diversification, the application of distributed and heterogeneous systems in production scheduling has become increasingly widespread. This paper studies a distributed heterogeneous flexible job shop scheduling problem under nonidentical time-of-use electricity tariffs (DHFJSP-NTOU) that uniquely integrates three underexplored dimensions: heterogeneous factories with time-dependent processing capabilities, geographically varying time-of-use electricity tariffs, and dual optimization of production efficiency and energy sustainability. A mixed-integer linear programming (MILP) model for DHFJSP-NTOU is established. To solve the DHFJSP-NTOU, a graph-reinforced multi-objective optimization algorithm (GRMO) is developed, which features three innovations: a hybrid initialization strategy balancing greedy heuristics and solution diversity, a graph neural network (GNN) framework dynamically encoding operational interdependencies and factory-specific constraints, and a reinforcement learning-driven adaptive operator selection mechanism using proximal policy optimization (PPO) for intelligent search guidance. Finally, comprehensive experiments are carried out to assess the performance of both the MILP model and the components of the GRMO. The experimental outcomes indicate that the GRMO outperforms several of the most recent high-performance methods in solving the DHFJSP-NTOU problem. The structural analysis further validates that the GNN-based feature extraction enhances search efficiency compared to conventional methods. These innovations provide a new paradigm for addressing the challenges of sustainable scheduling in distributed manufacturing systems with heterogeneous resources.

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