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Off-stoichiometry effect on magnetic damping in thin films of Heusler alloy Co2MnSi

Physical Review B 2020年第17期101卷 174410-174410页

作者： Ting Li Wei Yan Xinhui Zhang Bing Hu Kidist Moges Tetsuya Uemura Masafumi Yamamoto Masahito Tsujikawa Masafumi Shirai Yoshio Miura State Key Laboratory of Superlattices and Microstructures Institute of Semiconductors Chinese Academy of Sciences Beijing 100083 People's Republic of China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing 100049 People's Republic of China Division of Electronics for Informatics Graduate School of Information Science and Technology Hokkaido University Sapporo 060-0814 Japan Research Institute of Electrical Communication Tohoku University Sendai 980-8577 Japan Center for Spintronics Research Network Tohoku University Sendai 980-8577 Japan Research Center for Magnetic and Spintronic Materials National Institute for Materials Science (NIMS) Tsukuba 305-0047 Japan Center for Materials Research by Information Integration National Institute for Materials Science (NIMS) Tsukuba 305-0047 Japan Center for Spintronic Research Network Osaka University Toyonaka 560-8531 Japan

We investigated the effect of off-stoichiometry on the Gilbert magnetic damping constant (α) of Heusler alloy Co2MnSi (CMS) thin films by employing time-resolved magneto-optical Kerr effect (TR-MOKE) measurements along with first-principles calculations based on the linear-response theory for magnetic damping. Because of the contribution of extrinsic damping arising from two-magnon scattering, the effective α (αeff) extracted from the TR-MOKE responses showed dependences on the in-plane magnetic-field angle (θH) and the field strength (H). Then, we obtained the smallest αeff (α0) for each sample under the most reduced contribution from two-magnon scattering realized through varying θH and H values, which is the closest value to α. The thus obtained α0 values of epitaxially grown off-stoichiometric Co2MnβSiγ (γ=0.82) films with various (Mn + Si) compositions, (β+γ), decreased with increasing (β+γ) from α0=0.0057 for (Mn + Si)-deficient (β+γ)=1.44 to α0=0.0036 for (β+γ)=1.90 being close to the stoichiometric one of (β+γ)=2.0 at 300 K. It was also demonstrated that a half-metallic (Mn+Si)-rich CMS film with β=1.30 and γ=0.90 showed a low α0 of 0.0035. The dependence of α0 on (β+γ) in Co2MnβSiγ (γ=0.82) was well explained by the first-principles calculations. Through the systematic investigations of off-stoichiometric CMS with various values of (β+γ), it was clarified that the total density of states (DOS) at the Fermi level, D(EF), plays the key role for determining the damping constant of CMS. Furthermore, it was revealed that the reduced minority-spin DOS at EF, caused by decreasing harmful CoMn antisites, is essential for reducing the damping constant of CMS. These findings demonstrate that appropriately controlling off-stoichiometry and film composition is thus promising for achieving half-metallicity and a low α simultaneously for CMS thin films.

关键词： First-principles calculations Magneto-optical Kerr effect Ultrafast magnetization dynamics

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Consensus algorithms for biased labeling in crowdsourcing

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information Sciences 2017年 382-383卷 254-273页

作者： Jing Zhang Victor S. Sheng Qianmu Li Jian Wu Xindong Wu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 P. R. China Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing 210094 P. R. China Department of Computer Science University of Central Arkansas Conway AR 72035 USA Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 P. R. China Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 P. R. China School of Computing and Informatics University of Louisiana at Lafayette LA 70504 USA

Although it has become an accepted lay view that when labeling objects through crowdsourcing systems, non-expert annotators often exhibit biases, this argument lacks sufficient evidential observation and systematic empirical study. This paper initially analyzes eight real-world datasets from different domains whose class labels were collected from crowdsourcing systems. Our analyses show that biased labeling is a systematic tendency for binary categorization; in other words, for a large number of annotators, their labeling qualities on the negative class (supposed to be the majority) are significantly greater than are those on the positive class (minority). Therefore, the paper empirically studies the performance of four existing EM-based consensus algorithms , DS, GLAD, RY, and ZenCrowd, on these datasets. Our investigation shows that all of these state-of-the-art algorithms ignore the potential bias characteristics of datasets and perform badly although they model the complexity of the systems. To address the issue of handling biased labeling, the paper further proposes a novel consensus algorithm, namely adaptive weighted majority voting (AWMV), based on the statistical difference between the labeling qualities of the two classes. AWMV utilizes the frequency of positive labels in the multiple noisy label set of each example to obtain a bias rate and then assigns weights derived from the bias rate to negative and positive labels. Comparison results among the five consensus algorithms (AWMV and the four existing) show that the proposed AWMV algorithm has the best overall performance. Finally, this paper notes some potential related topics for future study.

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Robust Imagined Speech Production from Electrocorticography with Adaptive Frequency Enhancement

Robust Imagined Speech Production from Electrocorticography ...

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International Joint Conference on Neural networks (IJCNN)

作者： Xu Xu Chong Fu Junxin Chen Gwanggil Jeon David Camacho School of Computer Science and Engineering Northeastern University Shenyang China School of Software Dalian University of Technology Dalian China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China College of Information Technology Incheon National University Incheon Korea Department of Computer Systems Engineering Universidad Politécnica de Madrid Madrid Spain

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Imagined speech production with electrocorticography (ECoG) plays a crucial role in brain-computer interface system. A challenging issue is the great variation underlying the frequency bands of the ECoG signals’ encode information, which makes current methods difficult to generate imagined speech with stable quality among different persons. To this end, we propose a robust model to generate high-quality imagined speech from ECoG. A frequency enhancement branch is first designed to adaptively modulate the frequency information, whose product is fed into the following multi-scale channel attention module for robust feature extraction and fusion. By incorporating both the mel-spectrum and audio as training constraints, a multi-constraint decoder branch is finally constructed for imagined speech production. The performance of our model is evaluated on a high-quality dateset, i.e, Single Word Production Dutch-iBIDS. It yields Pearson correlation scores that are all above 0.8, and the standard deviationsare are all below 0.2 in different volunteers. Experimental results demonstrate that our model is effective and robust for ECoG based imagined speech production, and has advantages over peer methods.

关键词： Training Adaptation models Frequency modulation Correlation Neuroscience Production Speech enhancement

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Unsupervised Community Discovery Algorithm via Reconstructed Graph Neural network

Unsupervised Community Discovery Algorithm via Reconstructed...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jinghong Wang Hui Wang Jiateng Yang Xizhao Wang College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Hebei Key Laboratory of Network and Information Security Hebei Normal University Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security (Hebei Normal University) Shijiazhuang China College of Artificial Intelligence and Big Data Hebei Polytechnic Institute Shijiazhuang China College of Computer Science and Software Engineering ShenZhen University Shenzhen China

Community discovery can help discover potential community structures in the network, which is a fundamental and important issue in network science. Graph neural network-based algorithms are receiving increasing attention. However, there is relatively little work on community discovery directly using graph neural networks in reconstruction design. This paper presents an Unsupervised Community Discovery Algorithm based on reconstructed graph neural network (UCDA) to resolve the above issues. To be specific, this paper proposes a framework that learns the representation based on network structure and attribute information. By designing input network features, encoders, decoders, this algorithm uses an unsupervised loss function to train neural networks to extract communities in an integrated way. These designs integrate higher-order modularity information with network features and use variational graph auto-encoders to train data end-to-end under the graph neural network framework. Based on a series of experiments with a wide range of datasets and advanced algorithms, UCDA has achieved superior performance and shows strong competitiveness. Finally, the last section reports the results analysis, t-SNE visualization and hyperparameter analysis, which depicts stable performance and powerful network modularity ability.

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An improved DV-Hop positioning algorithm for wireless sensor network

An improved DV-Hop positioning algorithm for wireless sensor...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Jiuhu Zheng Huanyan Qian Lie Wang School of Information Technology Jiangsu Institute of Commerce Nanjing Jiangsu China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing Jiangsu China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing Nanjing Jiangsu China National Mobile Communications esearch Laboratory Southeast University Nanjing Jiangsu China Ministry of Education Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications) Nanjing Jiangsu China

In the view of the DV-Hop positioning algorithm which relies on the distance vector and beacon nodes, several aspects are improved: the average hop distance and hop count are corrected; a method to detect the abnormal distribution of beacon nodes is used to avoid the influence of the abnormal distribution on the positioning performance. The proposed algorithm is simple and effective. Simulation results show that compared with the traditional DV-Hop algorithm, the performance of the proposed algorithm is greatly improved. The performance is even even superior to that of the algorithm mentioned in literature [10].

关键词： Wireless sensor networks Mobile communication Hardware Maximum likelihood estimation Mathematical model computer science Broadband communication

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Investigating Bi-level optimization for learning and vision from a unified perspective: A survey and beyond

arXiv

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arXiv 2021年

作者： Liu, Risheng Gao, Jiaxin Zhang, Jin Meng, Deyu Lin, Zhouchen DUT-RU International School of Information Science & Engineering Dalian University of Technology Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian116024 China Department of Mathematics Southern University of Science and Technology National Center for Applied Mathematics Shenzhen China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an Shaanxi China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai200240 China

Bi-Level Optimization (BLO) is originated from the area of economic game theory and then introduced into the optimization community. BLO is able to handle problems with a hierarchical structure, involving two levels of optimization tasks, where one task is nested inside the other. In machine learning and computer vision fields, despite the different motivations and mechanisms, a lot of complex problems, such as hyper-parameter optimization, multi-task and meta learning, neural architecture search, adversarial learning and deep reinforcement learning, actually all contain a series of closely related subproblms. In this paper, we first uniformly express these complex learning and vision problems from the perspective of BLO. Then we construct a best-response-based single-level reformulation and establish a unified algorithmic framework to understand and formulate mainstream gradient-based BLO methodologies, covering aspects ranging from fundamental automatic differentiation schemes to various accelerations, simplifications, extensions and their convergence and complexity properties. Last but not least, we discuss the potentials of our unified BLO framework for designing new algorithms and point out some promising directions for future research. © 2021, CC BY.

关键词： Deep learning

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Mind the Gap: Towards Generalizable Autonomous Penetration Testing via Domain Randomization and Meta-Reinforcement Learning

arXiv

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arXiv 2024年

作者： Zhou, Shicheng Liu, Jingju Lu, Yuliang Yang, Jiahai Zhang, Yue Chen, Jie College of Electronic Engineering National University of Defense Technology Hefei230037 China Key Laboratory of Cyberspace Security Situation Awareness and Evaluation Anhui Province Hefei230037 China Institute for Network Sciences and Cyberspace The Beijing National Research Center for Information Science and Technology Tsinghua University Beijing10084 China College of Computer Science and Technology National University of Defense Technology Changsha410073 China

With increasing numbers of vulnerabilities exposed on the internet, autonomous penetration testing (pentesting) has emerged as a promising research area. Reinforcement learning (RL) is a natural fit for studying this topic. However, two key challenges limit the applicability of RL-based autonomous pentesting in real-world scenarios: (a) training environment dilemma – training agents in simulated environments is sample-efficient while ensuring their realism remains challenging;(b) poor generalization ability – agents’ policies often perform poorly when transferred to unseen scenarios, with even slight changes potentially causing significant generalization gap. To this end, we propose GAP, a generalizable autonomous pentesting framework that aims to realizes efficient policy training in realistic environments and train generalizable agents capable of drawing inferences about other cases from one instance. GAP introduces a Real-to-Simto-Real pipeline that (a) enables end-to-end policy learning in unknown real environments while constructing realistic simulations;(b) improves agents’ generalization ability by leveraging domain randomization and meta-RL learning. Specially, we are among the first to apply domain randomization in autonomous pentesting and propose a large language model-powered domain randomization method for synthetic environment generation. We further apply meta-RL to improve agents’ generalization ability in unseen environments by leveraging synthetic environments. The combination of two methods effectively bridges the generalization gap and improves agents’ policy adaptation performance. Experiments are conducted on various vulnerable virtual machines, with results showing that GAP can enable policy learning in various realistic environments, achieve zero-shot policy transfer in similar environments, and realize rapid policy adaptation in dissimilar environments. The source code is available at https://***/Joe-zsc/GAP. Copyright © 2024, The Auth

关键词： Reinforcement learning

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Supervised discriminative sparse PCA for com-characteristic gene selection and tumor classification on multiview biological data

arXiv

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arXiv 2019年

作者： Feng, Chun-Mei Xu, Yong Liu, Jin-Xing Gao, Ying-Lian Zheng, Chun-Hou Bio-Computing Research Center Harbin Institute of Technology Shenzhen518055 China Key Laboratory of Network Oriented Intelligent Computation Shenzhen518055 China School of Information Science and Engineering Qufu Normal University Rizhao276826 China Library of Qufu Normal University Qufu Normal University Rizhao276826 China School of Computer Science and Technology Anhui University Hefei230000 China

Principal component analysis (PCA) has been used to study the pathogenesis of diseases. To enhance the interpretability of classical PCA, various improved PCA methods have been proposed to date. Among these, a typical method is the so-called sparse PCA, which focuses on seeking sparse loadings. However, the performance of these methods is still far from satisfactory due to their limitation of using unsupervised learning methods;moreover, the class ambiguity within the sample is high. To overcome this problem, this paper developed a new PCA method, which is named the supervised discriminative sparse PCA (SDSPCA). The main innovation of this method is the incorporation of discriminative information and sparsity into the PCA model. Specifically, in contrast to the traditional sparse PCA, which imposes sparsity on the loadings, here, sparse components are obtained to represent the data. Furthermore, via the linear transformation, the sparse components approximate the given label information. On the one hand, sparse components improve interpretability over the traditional PCA, while on the other hand, they are have discriminative abilities suitable for classification purposes. A simple algorithm is developed, and its convergence proof is provided. SDSPCA has been applied to the common-characteristic gene selection and tumor classification on multiview biological data. The sparsity and classification performance of SDSPCA are empirically verified via abundant, reasonable, and effective experiments, and the obtained results demonstrate that SDSPCA outperforms other state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Principal component analysis

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Reduce the rank calculation of a high-dimensional sparse matrix based on network controllability theory

arXiv

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arXiv 2021年

作者： Zhao, Chen Liu, Yuqing Hu, Li Yuan, Zhengzhong College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang050024 China Hebei Key Laboratory of Network and Information Security Shijiazhuang050024 China School of Mathematics and Statistics Minnan Normal University Zhangzhou363000 China Fujian Key Laboratory of Data Science and Statistics Minnan Normal University Zhangzhou363000 China Institute of Meteorological Big Data-Digital Fujian Minnan Normal University Zhangzhou363000 China

Numerical computing of the rank of a matrix is a fundamental problem in scientific computation. The datasets generated by the internet often correspond to the analysis of high-dimensional sparse matrices. Notwithstanding recent advances in the promotion of traditional singular value decomposition (SVD), an efficient estimation algorithm for the rank of a high-dimensional sparse matrix is still lacking. Inspired by the controllability theory of complex networks, we converted the rank of a matrix into maximum matching computing. Then, we established a fast rank estimation algorithm by using the cavity method, a powerful approximate technique for computing the maximum matching, to estimate the rank of a sparse matrix. In the merit of the natural low complexity of the cavity method, we showed that the rank of a high-dimensional sparse matrix can be estimated in a much faster way than SVD with high accuracy. Our method offers an efficient pathway to quickly estimate the rank of the high-dimensional sparse matrix when the time cost of computing the rank by SVD is unacceptable. Copyright © 2021, The Authors. All rights reserved.

关键词： Singular value decomposition

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Robust Beamforming for IRS-assisted Wireless Communications under Channel Uncertainty

Robust Beamforming for IRS-assisted Wireless Communications ...

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IEEE Conference on Wireless Communications and networking

作者： Yongchang Deng Yuze Zou Shimin Gong Bin Lyu Dinh Thai Hoang Dusit Niyato School of Intelligent Systems Engineering Sun Yat-sen University China School of Electronic Information and Communications Huazhong University of Science and Technology China Key Laboratory of Ministry of Education in Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications China School of Electrical and Data Engineering University of Technology Sydney Australia School of Computer Science and Engineering Nanyang Technological University Singapore

In this paper, we consider IRS-assisted transmissions from a multi-antenna access point (AP) to a receiver with uncertain channel information. By adjusting the magnitude of reflecting coefficients, the IRS can sustain its operations by harvesting energy from the AP's signal beamforming. Considering channel estimation errors, we model both the AP-IRS channel and the AP-IRS-receiver as a cascaded channel by norm-based uncertainty sets. This allows us to formulate a robust optimization problem to minimize the AP's transmit power, subject to the receiver's worst-case data rate requirement and the IRS's worst-case power budget constraint. Instead of using the alternating optimization (AO) method, we firstly propose a heuristic scheme to decompose the IRS's phase shift optimization and the AP's active beamforming. Based on semidefinite relaxations of the worst-case constraints, we further devise an iterative algorithm to optimize the AP's transmit beamforming and the magnitude of the IRS's reflecting coefficients efficiently by solving a set of semidefinite programs. Simulation results reveal that the AP requires a higher transmit power to deal with the channel uncertainty. Moreover, the negative effect of channel uncertainty can be alleviated by using a larger-size IRS.

关键词： Wireless communication Uncertainty Array signal processing Simulation Transmitting antennas Channel estimation Receivers

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