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Proceedings of the 2020 9th International Conference on Bioinformatics and Biomedical Science

作者： MENG KONG HAIFENG ZHAO SHAOJIE ZHANG ZHENYU TANG School of Computer Science and Technology Anhui University China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China

ISBN: (纸本)9781450388658

brain image registration (BIR) plays an important role in neuroscience. However, for the registration of brain image containing tumors, the existence of tumor could cause great influence to BIR. One possible solution for getting rid of such influence is to recover the tumor brain image to “normal” appearance brain image (no tumor). Most of existing methods for tumor brain image recovery are based on low-rank, which is time consuming and low recovery quality. In this paper, we propose a novel deep-learning based method for tumor brain image recovery. Specifically, a two-stage generative adversarial network comprising a region recovery stage and an image recovery stage is presented. For the input tumor brain image, the region recovery stage first generates a recovered brain region image containing three different regions (i.e., the gray matter, the white matter and the cerebrospinal fluid). The recovered brain region image is used in the image recovery stage as priori information to get the final “normal” appearance brain image. Both stages are trained under the generative adversarial framework. The experimental results demonstrate that the registration accuracy of tumor brain images can be significantly enhanced by our network as compared to the state-of-the-art image recovery methods.

关键词： Tumor brain image recovery Deep learning Two stage recovery GAN brain image registration

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Penetration trajectory optimization for the hypersonic gliding vehicle encountering two interceptors

arXiv

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arXiv 2022年

作者： Shen, Zhipeng Yu, Jianglong Dong, Xiwang Hua, Yongzhao Ren, Zhang School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Institute of Artificial Intelligence Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

The penetration trajectory optimization problem for the hypersonic gliding vehicle (HGV) encountering two interceptors is investigated. The HGV penetration trajectory optimization problem considering the terminal target area is formulated as a nonconvex optimal control problem. The nonconvex optimal control problem is transformed into a second-order cone programming (SOCP) problem, which can be solved by state-of-the-art interior-point methods. In addition, a penetration strategy that only requires the initial line-of-sight angle information of the interceptors is proposed. The convergent trajectory obtained by the proposed method allows the HGV to evade two interceptors and reach the target area successfully. Furthermore, a successive SOCP method with a variable trust region is presented, which is critical to balancing the trade-off between time consumption and optimality. Finally, the effectiveness and performance of the proposed method are verified by numerical simulations. Copyright © 2022, The Authors. All rights reserved.

关键词： Numerical methods

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Post-Quantum Blockchain over Lattice

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Computers, Materials & Continua 2020年第5期63卷 845-859页

作者： Xiao Zhang Faguo Wu Wang Yao Wenhua Wang Zhiming Zheng School of Mathematics and Systems Science Beihang Universityand Key Laboratory of MathematicsInformatics and Behavioral SemanticsMinistry of EducationBeijing100191China Peng Cheng Laboratory Shenzhen518055China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing100191China Aviation Industry Development Research Center of China BeijingChina

Blockchain is an emerging decentralized architecture and distributed computing paradigm underlying Bitcoin and other cryptocurrencies,and has recently attracted intensive attention from governments,financial institutions,high-tech enterprises,and the capital *** cryptographic security relies on asymmetric cryptography,such as ECC,***,with the surprising development of quantum technology,asymmetric cryptography schemes mentioned above would become ***,lattice-based cryptography scheme was proposed to be secure against attacks in the quantum *** 2018,with the aid of Bonsai Trees technology,Yin et al.[Yin,Wen,Li et al.(2018)]proposed a lattice-based authentication method which can extend a lattice space to multiple lattice spaces accompanied by the corresponding *** their scheme has theoretical significance,it is unpractical in actual situation due to extremely large key size and signature *** this paper,aiming at tackling the critical issue of transaction size,we propose a post quantum blockchain over *** using SampleMat and signature without trapdoor,we can reduce the key size and signature size of our transaction authentication approach by a significant *** of using a whole set of vectors as a basis,we can use only one vector and rotate it enough times to form a *** on the hardness assumption of Short Integer Solution(SIS),we demonstrate that the proposed anti-quantum transaction authentication scheme over lattice provides existential unforgeability against adaptive chosen-message attacks in the random *** compared to the Yin et al.[Yin,Wen,Li et al.(2018)]scheme,our scheme has better performance in terms of energy consumption,signature size and signing key *** the underlying lattice problem is intractable even for quantum computers,our scheme would work well in the quantum age.

关键词： Blockchain post quantum lattice random oracle

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All-optical Helicity-Independent Switching State Diagram in Gd-Fe-Co Alloys

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Physical Review Applied 2021年第5期15卷 054065-054065页

作者： Jiaqi Wei Boyu Zhang Michel Hehn Wei Zhang Gregory Malinowski Yong Xu Weisheng Zhao Stéphane Mangin Fert Beijing Institute Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) School of Integrated Circuit Science and Engineering Beihang University Beijing 100191 China Université de Lorraine CNRS IJL F-54000 Nancy France

Ultrafast magnetization switching induced by a single femtosecond laser pulse, under no applied magnetic field has attracted a lot of attention in the last 10 years because of its high potential for low-energy and ultrafast memory applications. Single-pulse helicity-independent switching has mostly been demonstrated for Gd-based materials. It is now necessary to optimize the pulse duration and the energy needed to switch a Gd-Fe-Co magnet depending on the alloy thickness and composition. Here we experimentally report state diagrams showing the magnetic state obtained after one single pulse depending on the laser pulse duration and fluence for various Gd-Fe-Co thin films with different compositions and thicknesses. We demonstrate that these state diagrams share similar characteristics: the fluence window for switching narrows for longer pulse duration and for the considered pulse-duration range the critical fluence for single-pulse switching increases linearly as a function of the pulse duration while the critical fluence required for creating a multidomain state remains almost constant. Calculations based on the atomistic spin model qualitatively reproduce the experimental state diagrams and their evolution. By studying the effect of the composition and the thickness on the state diagram, we demonstrate that the best energy efficiency and the longest pulse duration for switching are obtained for composition around the magnetic compensation.

关键词： Light-induced magnetic effects Magnetic domains Magnetization switching Spin dynamics Spintronics Ultrafast magnetic effects Ferrimagnets Transition-metal rare-earth alloys

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Strong Coupling of Antiferromagnetic Resonance with Subterahertz Cavity Fields

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Physical Review Applied 2021年第4期15卷 044018-044018页

作者： M. Białek J. Zhang H. Yu J.-Ph. Ansermet Institute of Physics École Polytechnique Fédérale de Lausanne (EPFL) Lausanne 1015 Switzerland Fert Beijing Institute School of Microelectronics Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China

Strong coupling of electromagnetic cavity fields with antiferromagnetic spin waves in hematite (α-Fe2O3) is achieved above room temperature. A cube of hematite is placed in a metallic tube and the transmission is measured, using a continuous-wave terahertz spectrometer. The spectra, collected as a function of the temperature, reveal the formation of magnetic polaritons.

关键词： Magnons Polaritons Spin waves Antiferromagnets Noncollinear magnets Transition metal oxides Cavity resonators Terahertz spectroscopy

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Synthesis of Decoherence-Free Modes in Linear Quantum Passive Systems via Robust Pole Placement

Synthesis of Decoherence-Free Modes in Linear Quantum Passiv...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Zibo Miao Yu Pan Qing Gao Guangdong Key Laboratory of Intelligent Morphing Mechanisms School of Mechanical Engineering and Automation Adaptive Robotics Harbin Institute of Technology Shenzhen China State Key Laboratory of Industrial Control Technology The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at preservation of quantum information. In particular, DF modes can be generated by placing the poles on the imaginary axis via a coherent feedback design scheme, and these modes can further be simultaneously made robust against perturbations to the system parameters by minimizing the condition number associated with imaginary poles. We develop explicit algebraic conditions for the existence of such a coherent quantum controller, with the corresponding deign procedure provided. Examples are given to illustrate the process of tuning the DF modes towards perfect robustness via the proposed pole placement technique.

关键词： Perturbation methods Design methodology Quantum optics Robustness Feedback control Closed loop systems Tuning Cybernetics

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DAGAD: data Augmentation for Graph Anomaly Detection

DAGAD: Data Augmentation for Graph Anomaly Detection

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IEEE International Conference on data Mining (ICDM)

作者： Fanzhen Liu Xiaoxiao Ma Jia Wu Jian Yang Shan Xue Amin Beheshti Chuan Zhou Hao Peng Quan Z. Sheng Charu C. Aggarwal School of Computing Macquarie University Sydney Australia School of Computing and Information Technology University of Wollongong Wollongong Australia Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China IBM T. J. Watson Research Center Yorktown NY USA

Graph anomaly detection in this paper aims to distinguish abnormal nodes that behave differently from the benign ones accounting for the majority of graph-structured instances. Receiving increasing attention from both academia and industry, yet existing research on this task still suffers from two critical issues when learning informative anomalous behavior from graph data. For one thing, anomalies are usually hard to capture because of their subtle abnormal behavior and the shortage of background knowledge about them, which causes severe anomalous sample scarcity. Meanwhile, the overwhelming majority of objects in real-world graphs are normal, bringing the class imbalance problem as well. To bridge the gaps, this paper devises a novel data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs, equipped with three specially designed modules: 1) an information fusion module employing graph neural network encoders to learn representations, 2) a graph data augmentation module that fertilizes the training set with generated samples, and 3) an imbalance-tailored learning module to discriminate the distributions of the minority (anomalous) and majority (normal) classes. A series of experiments on three datasets prove that DAGAD outperforms ten state-of-the-art baseline detectors concerning various mostly-used metrics, together with an extensive ablation study validating the strength of our proposed modules.

关键词： Measurement Training Industries Sensitivity analysis Blogs Detectors Graph neural networks

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Neuromodulated Meta-Learning

arXiv

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arXiv 2024年

作者： Wang, Jingyao Guo, Huijie Qiang, Wenwen Li, Jiangmeng Zheng, Changwen Xiong, Hui Hua, Gang University of Chinese Academy of Sciences Beijing China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Thrust of Artificial Intelligence Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Wormpex AI Research LLC BellevueWA98004 United States

Humans excel at adapting perceptions and actions to diverse environments, enabling efficient interaction with the external world. This adaptive capability relies on the biological nervous system (BNS), which activates different brain regions for distinct tasks. Meta-learning similarly trains machines to handle multiple tasks but relies on a fixed network structure, not as flexible as BNS. To investigate the role of flexible network structure (FNS) in meta-learning, we conduct extensive empirical and theoretical analyses, finding that model performance is tied to structure, with no universally optimal pattern across tasks. This reveals the crucial role of FNS in meta-learning, ensuring meta-learning to generate the optimal structure for each task, thereby maximizing the performance and learning efficiency of meta-learning. Motivated by this insight, we propose to define, measure, and model FNS in meta-learning. First, we define that an effective FNS should possess frugality, plasticity, and sensitivity. Then, to quantify FNS in practice, we present three measurements for these properties, collectively forming the structure constraint with theoretical supports. Building on this, we finally propose Neuromodulated Meta-Learning (NeuronML) to model FNS in meta-learning. It utilizes bi-level optimization to update both weights and structure with the structure constraint. Extensive theoretical and empirical evaluations demonstrate the effectiveness of NeuronML on various tasks. Code is publicly available at https://***/WangJingyao07/NeuronML. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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DAGAD: data Augmentation for Graph Anomaly Detection

arXiv

引用

arXiv 2022年

作者： Liu, Fanzhen Ma, Xiaoxiao Wu, Jia Yang, Jian Xue, Shan Beheshti, Amin Zhou, Chuan Peng, Hao Sheng, Quan Z. Aggarwal, Charu C. School of Computing Macquarie University Sydney Australia School of Computing and Information Technology University of Wollongong Wollongong Australia Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Ibm T. J. Watson Research Center YorktownNY United States

关键词： Graph neural networks

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Adaptive Leaderless Consensus for a Class of Uncertain Nonlinear Systems with Intermittent Actuator Faults* 15

Adaptive Leaderless Consensus for a Class of Uncertain Nonli...

引用

15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020

作者： Mahdi Al-Adhami, Marwah Saad Long, Jiang Wang, Wei Wang, Lei Beihang University School of Automation Science and Electrical Engineering Beijing China Beihang University School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China

ISBN: (纸本)9781728151694

This paper investigates the leaderless consensus problem for high-order nonlinear systems with unmatched unknown parameters and intermittent actuator faults. Under directed topology condition, a novel adaptive fault tolerant leaderless consensus control scheme is proposed. In order to achieve the leaderless consensus under directed topology condition, an auxiliary system, which is related to the individual states and neighboring states, is introduced in each agent. On the other hand, to compensate the effect of intermittent actuator faults on the system stability of each agent, a local parameter estimator is introduced to estimate a lumped unknown parameter related to the lower bound of the effectiveness of actuator. It is shown that with the proposed adaptive fault tolerant leaderless consensus control scheme, all the closed-loop signals are globally uniformly bounded and all the agents' outputs can reach a consensus. © 2020 IEEE.

关键词： System stability

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