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2022生物物理大会

作者： Liqing Liu Shuxin Yang Yang Liu Xixia Li Junjie Hu Li Xiao Tao Xu National Laboratory of Biomacromolecules Institute of Biophysics Chinese Academy of Sciences Center for Biological Imaging Institute of Biophysics Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences College of Life Science University of Chinese Academy of Sciences Ningbo HuaMei Hospital University of Chinese Academy of Sciences School of Biomedical Engineering Guangzhou Medical University

Membrane contact site (MCS)-mediated organelle interactions plays essential roles in the cell. Quantitative analysis of the MCS reveals vital clues for cellular responses under various physiological and pathological conditions. However, an efficient tool is lacking. Here, we developed "DeepContact", a deep learning protocol for optimizing organelle segmentation and contact analysis based on label-free electron microscopy(EM).

关键词： DeepContact High throughput quantification of membrane contact site based on electron microscopy imaging

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Research on Attention-Based Predictive Model in Multi-Agent Reinforcement Learning

Research on Attention-Based Predictive Model in Multi-Agent ...

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Chinese Automation Congress (CAC)

作者： Weixin Hong Huaiyu Wu Zhen Shen Gang Xiong Bin Hu Xisong Dong Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

Agent actions planning is a challenging problem in multi-agent reinforcement learning. Recent methods typically build their predictive models by full connection layers, but the shortage of utilizing action and observation information in complex multi-agent environment limits the performance of those methods. To address this issue, we propose a novel predictive model based on self-attention mechanism which can effectively use information in the environment. Specifically, we use the MADDPG algorithm to make a dataset where the input is the observations and actions and the output is the predicted observations and rewards. We also introduce the multi-head attention mechanism into the predictive model so that the predictive model can better combine different information for prediction. We perform extensive experiments to assess the proposed method and compare it with the representative baseline approach. Results demonstrate that our method outperforms competitors with higher accuracy in different tasks. Our implementation will be publicly available.

关键词： Automation Reinforcement learning Predictive models Prediction algorithms Planning Task analysis

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Entity-enhanced Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding

arXiv

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arXiv 2022年

作者： Liu, Xuejing Li, Liang Wang, Shuhui Zha, Zheng-Jun Li, Zechao Tian, Qi Huang, Qingming Institute of Computing Technology CAS Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518066 China The School of Information Science and Technology University of Science and Technology of China Hefei230027 China The School of Computer Science Nanjing University of Science and Technology Nanjing210094 China Huawei Cloud & AI Shenzhen518129 China The School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing100190 China The Institute of Computing Technology CAS Beijing100190 China

Weakly supervised Referring Expression Grounding (REG) aims to ground a particular target in an image described by a language expression while lacking the correspondence between target and expression. Two main problems exist in weakly supervised REG. First, the lack of region-level annotations introduces ambiguities between proposals and queries. Second, most previous weakly supervised REG methods ignore the discriminative location and context of the referent, causing difficulties in distinguishing the target from other same-category objects. To address the above challenges, we design an entity-enhanced adaptive reconstruction network (EARN). Specifically, EARN includes three modules: entity enhancement, adaptive grounding, and collaborative reconstruction. In entity enhancement, we calculate semantic similarity as supervision to select the candidate proposals. Adaptive grounding calculates the ranking score of candidate proposals upon subject, location and context with hierarchical attention. Collaborative reconstruction measures the ranking result from three perspectives: adaptive reconstruction, language reconstruction and attribute classification. The adaptive mechanism helps to alleviate the variance of different referring expressions. Experiments on five datasets show EARN outperforms existing state-of-the-art methods. Qualitative results demonstrate that the proposed EARN can better handle the situation where multiple objects of a particular category are situated together. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Construction of Cu7s4@Cuco2o4 Yolk-Shell Microspheres Composite and Elucidation of its Enhanced Photocatalytic Activity, Mechanism, and Pathway for Carbamazepine Degradation

SSRN

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SSRN 2022年

作者： He, Zuming Yang, Hanpei Wong, Ngie Hing Ernawati, Lusi Sunarso, Jaka Huang, Zhengyi Xia, Yongmei Wang, Yong Su, Jiangbin Fu, Xiaofei Wu, Mi Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes Ministry of Education College of Environment Hohai University Nanjing210098 China Huaide School Changzhou University Jingjiang214500 China School of Microelectronics and Control Engineering Changzhou University Changzhou213164 China Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Sarawak Kuching93350 Malaysia Department of Chemical Engineering Institut Teknologi Kalimantan Balikpapan76127 Indonesia School of Materials and Engineering Jiangsu University of Technology Changzhou213001 China School of Pharmaceutical and Materials Engineering Taizhou University Jiaojiang318000 China

Water pollution caused by the massive use of medicines has caused major environmental problems. This work first reported the synthesis and characterization of the Cu7S4/CuCo2O4 (CS/CCO) yolk-shell microspheres via hydrothermal and annealing methods, then investigated their photocatalytic performance in removing organic water pollutants. The 10-CS/CCO composite with yolk-shell microspheres exhibited the highest photodegradation rate of carbamazepine (CBZ), reaching 96.3% within 2 h. The 10-CS/CCO also demonstrated more than 2 times higher photodegradation rates than the pure (Cu7S4) CS and (CuCo2O4) CCO. This outstanding photocatalytic performance can be attributed to the unique yolk-shell structure and the Z-scheme charge transfer pathway, reducing multiple reflections of the acting light. These factors enhanced the light absorption efficiency and efficiently transferred photoexcited charge carriers. In-depth photocatalytic degradation pathways of CBZ were systematically evaluated via the identification of degradation intermediates with Fukui index calculation. © 2022, The Authors. All rights reserved.

关键词： Microspheres

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A Learning Convolutional Neural Network Approach for Network Robustness Prediction

arXiv

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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： Complex networks

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Zhang Neurodynamics for Cholesky Decomposition of Matrix Stream Using Pseudo-Inverse with Transpose of Unknown

Zhang Neurodynamics for Cholesky Decomposition of Matrix Str...

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第40届中国控制会议

作者： Xiu Zeng Min Yang Jinjin Guo Yihong Ling Yunong Zhang School of Computer Science and Engineering Sun Yat-sen University School of Information and Communication Engineering Guangzhou Maritime University School of Electronics and Information Technology Sun Yat-sen University Guangdong Key Laboratory of Modern Control Technology Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

Cholesky decomposition is a well-known decomposition for positive definite matrix. On account of its significantly fundamental roles in linear algebra and matrix theory, there are a lot of researches and applications based on it. In recent years,solving time-varying problems has been a research hotspot, but Cholesky decomposition of matrix stream（i.e., continuous timevarying matrix） in a simple, direct and effective equation-solving manner remains a challenging issue. In this paper, the problem of Cholesky decomposition of matrix stream is attempted and solved. First, with the aid of Zhang neurodynamics（ZN）, the objective equation at time-derivative level, including the time derivatives of matrix variable and its transpose, is obtained. In order to handle the objective equation with transpose of unknown, Kronecker product, vectorization technique and vectorized transpose matrix are utilized for better derivation. Thus, a ZN solution model using pseudo-inverse is proposed and numerically experimented. Finally, numerical experiment results substantiate the efficacy of the pseudo-inverse type ZN solution model.

关键词： Cholesky decomposition Matrix stream Transpose of unknown Zhang neurodynamics Vectorized transpose matrix

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Dynamics and resonance fluorescence from a superconducting artificial atom doubly driven by quantized and classical fields

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Physical Review Research 2024年第3期6卷 033064-033064页

作者： Xinhui Ruan Jia-Heng Wang Dong He Pengtao Song Shengyong Li Qianchuan Zhao L. M. Kuang Jaw-Shen Tsai Chang-Ling Zou Jing Zhang Dongning Zheng O. V. Astafiev Yu-xi Liu Zhihui Peng Department of Automation Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center of Quantum Effects and Applications School of Integrated Circuits School of Physical Sciences Center for Quantum Computing Graduate School of Science CAS Key Laboratory of Quantum Information Hefei National Laboratory Hefei 230088 China School of Automation Science and Engineering MOE Key Lab for Intelligent Networks and Network Security Nobel Street 3 143026 Moscow Russia Institutskiy Pereulok 9 141701 Dolgoprudny Russia Egham Surrey TW20 0EX United Kingdom

We report an experimental demonstration of resonance fluorescence in a two-level superconducting artificial atom under two driving fields coupled to a detuned cavity. One of the fields is classical and the other is varied from quantum (vacuum fluctuations) to classical by controlling the photon number inside the cavity. The device consists of a transmon qubit strongly coupled to a one-dimensional transmission line and a coplanar waveguide resonator. We observe a sideband anticrossing and asymmetry in the emission spectra of the system through a one-dimensional transmission line, which is fundamentally different from the weak-coupling case. By changing the photon number inside the cavity, the emission spectrum of our doubly driven system approaches the case when the atom is driven by two classical bichromatic fields. We also measure the dynamical evolution of the system through the transmission line and study the properties of the first-order correlation function, Rabi oscillations, and energy relaxation in the system. The study of resonance fluorescence from an atom driven by two fields promotes understanding decoherence in superconducting quantum circuits and may find applications in superconducting quantum computing and quantum networks.

关键词： Light-matter interaction Optical pumping Quantum circuits Quantum coherence & coherence measures Quantum-to-classical transition Strong electromagnetic field effects

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Time-in-action RL

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IET Cyber-Systems and Robotics 2019年第1期1卷 28-37页

作者： Jiangcheng Zhu Zhepei Wang Douglas Mcilwraith Chao Wu Chao Xu Yike Guo Institute of Cyber-Systems and Control Department of Control Science and EngineeringZhejiang UniversityHangzhouPeople's Republic of China Data Science Institute Department of ComputingImperial College LondonLondonUK School of Public Affairs Zhejiang UniversityHangzhouPeople's Republic of China

The authors propose a novel reinforcement learning(RL)framework,where agent behaviour is governed by traditional control *** integrated approach,called time-in-action RL,enables RL to be applicable to many real-world systems,where underlying dynamics are known in their control theoretical *** key insight to facilitate this integration is to model the explicit time function,mapping the state-action pair to the time accomplishing the action by its underlying *** their framework,they describe an action by its value(action value),and the time that it takes to perform(action time).An action-value results from the policy of RL regarding a *** time is estimated by an explicit time model learnt from the measured activities of the underlying *** value network is then trained with embedded time model to predict action *** approach is tested using a variant of Atari Pong and proved to be convergent.

关键词： action policy agent

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MLAR-Net: A multilevel attention-based ResNet module for the automated recognition of emotions using single-channel EEG signals

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IEEE Access 2025年 13卷 99122-99144页

作者： Maithri, M. Raghavendra, U. Gudigar, Anjan Praharaj, Samir Kumar Karthikeyan, S. Salvi, Massimo Yeong, Chai Hong Molinari, Filippo Rajendra Acharya, U. Manipal Academy of Higher Education Manipal Institute of Technology Department of Mechatronics Manipal 576104 India Manipal Academy of Higher Education Manipal Institute of Technology Department of Instrumentation and Control Engineering Karnataka Manipal 576104 India Taylor’s University School of Medicine Faculty of Health and Medical Sciences Digital Health and Medical Advancement Impact Laboratory Subang Jaya 47500 Malaysia Kasturba Medical College Manipal Academy of Higher Education Department of Psychiatry Manipal Karnataka Manipal 576104 India Manipal Academy of Higher Education Manipal Institute of Technology Department of Electrical & Electronics Engineering Karnataka Manipal 576104 India Politecnico di Torino Biolab PoliToBIOMed Lab Department of Electronics and Telecommunications Corso Duca degli Abruzzi 24 Turin 10129 Italy University of Southern Queensland School of Mathematics Physics and Computing Springfield Australia University of Southern Queensland Centre for Health Research Australia

Human emotion recognition is important as it finds applications in multiple domains such as medicine, entertainment, and military. However, accurately identifying emotions remains challenging due to humans’ ability to hide or suppress their emotional expressions. Hence it becomes important to recognize emotions by using brain signals as they provide more reliable data. Brain signals can be captured using Electroencephalograms (EEG) electrodes. Most used EEG devices come with multiple channels. But not all channel information is important for emotion recognition. Another issue with the existing dataset is the availability of a small quantity of samples. To address these challenges, we propose MLAR-Net, a novel multi-level attention module for emotion recognition using single-channel EEG signals. Our approach converts EEG signals into spectrograms using multiple parameters to generate large set of images. This data is then processed through our proposed MLAR-Net, which integrates a multi-level attention module with ResNet18 architecture. Our study identifies channel number 24 (T7) as the most effective for emotion classification, achieving an average accuracy of 98.06% using cubic support vector machine and a maximum accuracy of 99.51% using fine K-Nearest Neighbours. The study was conducted using SEED dataset. It is a publicly available dataset developed by capturing EEG signals from fifteen subjects for three classes of emotions namely positive, negative and neutral. The results achieved by the proposed study show around 4 to 5% improvement in comparison with the state-of-the-art studies using the same channel. This performance surpasses existing state-of-the-art methods for single-channel EEG-based emotion recognition. Furthermore, we highlight the top-performing channels that can be used for real-time implementation of the system with a minimum number of channels. © 2013 IEEE.

关键词： Attention-Based ResNet Emotion Recognition Explainable AI Single-Channel EEG Spectrograms

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Mobile robot navigation for complete coverage of an environment

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IFAC-PapersOnLine 2018年第22期51卷 512-517页

作者： Šelek, Ana Seder, Marija Petrovi, Ivan Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing University of Zagreb Croatia

Inspired by the Spanning Tree Covering (STC) algorithm of Gabriely and Rimon, a novel algorithm of complete coverage for known environments is developed. Unlike the original STC algorithm, we detect dynamic elements of the environment and efficiently update the solution when changes are observed. The contribution is a path replanning algorithm that reduces overlapping when the part of the environment is changed. Experiments show that the proposed algorithm is capable of planning complete coverage robot paths with 70.27% coverage and without overlapping. © 2018

关键词： Mobile robots Collision avoidance Motion planning Robot programming Trees (mathematics) Complete coverages Dynamic elements Known environments Mobile Robot Navigation Novel algorithm Path re planning Robot path Spanning tree

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