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Data augmentation in microscopic images for material data mining

npj Computational Materials 2020年第1期6卷 601-609页

作者： Boyuan Ma Xiaoyan Wei Chuni Liu Xiaojuan Ban Haiyou Huang Hao Wang Weihua Xue Stephen Wu Mingfei Gao Qing Shen Michele Mukeshimana Adnan Omer Abuassba Haokai Shen Yanjing Su Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijing 100083China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing 100083China Institute for Advanced Materials and Technology University of Science and Technology BeijingBeijing 100083China School of Materials Science and Engineering University of Science and Technology BeijingBeijing 100083China School of Materials Science and Technology Liaoning Technical UniversityLiaoning 114051China The Institute of Statistical Mathematics Research Organization of Information and SystemsTachikawaTokyo 190-8562Japan National Intellectual Property Administration Beijing 100088China Faculty of Engineering Sciences University of BurundiBujumburaBurundi Faculty of Engineering and Technology Palestine Technical University–KadoorieTulkaremPalestine College of Information Science and Engineering China University of PetroleumBeijingChina Key Lab of Petroleum Data Mining China University of PetroleumBeijingChina

Recent progress in material data mining has been driven by high-capacity models trained on large ***,collecting experimental data(real data)has been extremely costly owing to the amount of human effort and expertise ***,we develop a novel transfer learning strategy to address problems of small or insufficient *** strategy realizes the fusion of real and simulated data and the augmentation of training data in a data mining *** a specific task of grain instance image segmentation,this strategy aims to generate synthetic data by fusing the images obtained from simulating the physical mechanism of grain formation and the“image style”information in real *** results show that the model trained with the acquired synthetic data and only 35%of the real data can already achieve competitive segmentation performance of a model trained on all of the real *** the time required to perform grain simulation and to generate synthetic data are almost negligible as compared to the effort for obtaining real data,our proposed strategy is able to exploit the strong prediction power of deep learning without significantly increasing the experimental burden of training data preparation.

关键词： mining instance competitive

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Real-life experiments for walkability assessment in elderly people

Real-life experiments for walkability assessment in elderly ...

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2020 Italian Workshop on Artificial Intelligence for an Ageing Society, AIxAS 2020

作者： Gasparini, Francesca Toffanin, Gianluca Campioni, Rino Di Marla, Maria Borghi, Armando Bandini, Stefania Department of Computer Science Systems and Communications University of Milano Bicocca Italy RCAST - Research Center for Advanced Science and Technology The University of Tokyo Japan Sede di AUSER Milano Milano20100 Italy Sede di AUSER Cantù Via Ettore Brambilla 3 Cantù22063 Italy

Walkability assessment is a quantitative approach to investigate accessibility and confidence in the urban environment for the older people. Physiological responses could be effectively adopted to reveal in particular safety perception while walking in different outdoor scenarios and the definition of a proper experimental protocol is a mandatory step to collect useful data. This paper presents two different experimental protocols for acquisition in outdoor real urban scenarios involving senior citizens. The two experiments face different aspects of walkability in cities, considering both road crossing in stressful conditions, and long walks in urban pathways and parks. © 2020 Copyright for this paper by its authors.

关键词： Parks

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Recursive Weighted Null-Space Fitting Method for Identification of Multivariate systems ⁎

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IFAC-PapersOnLine 2021年第7期54卷 345-350页

作者： Mengyuan Fang Miguel Galrinho Håkan Hjalmarsson Zhejiang Engineering Laboratory of Industrial Internet Communication Technology Zhejiang Sci-Tech University Hangzhou China. State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China OHB Sweden AB Kista Sweden Centre for Advanced Bio Production and Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

Recursive identification of structured multivariate models is known to be difficult due to the general non-convexity of the likelihood function. In this work, we propose a recursive multivariate weighted null-space fitting method for identification of structured multivariate models. The proposed method first uses recursive least squares to estimate a high order non-parametric model, then a parametric model is obtained through weighted least squares from the non-parametric model. In this way, the method avoids directly optimizing a non-convex likelihood function and has guaranteed global convergency. Moreover, the proposed method is flexible in model structures and has the same finite sample performance as its off-line counterpart. We use simulation examples to illustrate the performance.

关键词： Recursive system identification multivariate systems weighted null-space fitting

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Krasovskii and Shifted Passivity Based Output Consensus

arXiv

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arXiv 2022年

作者： Kawano, Yu Cucuzzella, Michele Feng, Shuai Scherpen, Jacquelien M.A. Graduate School of Advanced Science and Engineering Hiroshima University Higashi-Hiroshima739-8527 Japan Department of Electrical Computer and Biomedical Engineering University of Pavia PV Pavia27100 Italy School of Automation Nanjing University of Science and Technology Nanjing210094 China Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands

Motivated by current sharing in power networks, we consider a class of output consensus (also called agreement) problems for nonlinear systems, where the consensus value is determined by external disturbances, e.g., power demand. This output consensus problem is solved by a simple distributed output feedback controller if a system is either Krasovskii or shifted passive, which is the only essential requirement. The effectiveness of the proposed controller is shown in simulation on an islanded DC power network. Copyright © 2022, The Authors. All rights reserved.

关键词： Nonlinear systems

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technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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An Ultra-compact Broadband TE-pass Nanofocusing Structure

An Ultra-compact Broadband TE-pass Nanofocusing Structure

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2020 Asia Communications and Photonics Conference, ACP 2020 and International Conference on Information Photonics and Optical Communications, IPOC 2020

作者： Zhu, Kejian Pengfei, Xu Sun, Pengfei Liu, Xingpeng Li, Haiou Zhou, Zhiping Nano-optoelectronics Frontier Center of Ministry of Education Peking University Beijing100871 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Department of Electronics Beijing100871 China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin China

We propose a broadband structure which integrates polarization and nanofocusing functions into a single device with 3.8x4 μm footprint. The bandwidth is over 200 nm with insert loss (IL) smaller than 1.68 dB. © ...

ISBN: (纸本)9781943580828

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Double-free-layer stochastic magnetic tunnel junctions with synthetic antiferromagnets

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Physical Review Applied 2024年第5期21卷 054002-054002页

作者： Kemal Selcuk Shun Kanai Rikuto Ota Hideo Ohno Shunsuke Fukami Kerem Y. Camsari Department of Electrical and Computer Engineering University of California Santa Barbara California 93106 USA Research Institute of Electrical Communication Tohoku University 2-1-1 Katahira Aoba-ku Sendai 980-8577 Japan Graduate School of Engineering Tohoku University 6-6 Aramaki Aza Aoba Aoba-ku Sendai 980-8579 Japan WPI Advanced Institute for Materials Research (WPI-AIMR) Tohoku University 2-1-1 Katahira Aoba-ku Sendai 980-8577 Japan Center for Science and Innovation in Spintronics (CSIS) Tohoku University 2-1-1 Katahira Aoba-ku Sendai 980-8577 Japan PRESTO Japan Science and Technology Agency (JST) Kawaguchi 332-0012 Japan Division for the Establishment of Frontier Sciences of Organization for Advanced Studies at Tohoku University Tohoku University Sendai 980-8577 Japan National Institutes for Quantum Science and Technology Takasaki 370-1207 Japan Center for Innovative Integrated Electronic Systems (CIES) Tohoku University 468-1 Aramaki Aza Aoba Aoba-ku Sendai 980-0845 Japan Inamori Research Institute of Science (InaRIS) Kyoto 600-8411 Japan

Stochastic magnetic tunnel junctions (SMTJs) using low-barrier nanomagnets have shown promise as fast, energy-efficient, and scalable building blocks for probabilistic computing. Despite recent experimental and theoretical progress, SMTJs exhibiting the ideal characteristics necessary for probabilistic bits (p-bits) are still lacking. Ideally, the SMTJs should have (a) voltage bias independence, preventing read disturbance; (b) uniform randomness in the magnetization angle between the two magnetic layers; and (c) fast fluctuations without requiring external magnetic fields, while being robust to magnetic field perturbations. Here, we propose a design that satisfies all of these requirements, using double-free-layer SMTJs with synthetic antiferromagnets (SAFs). We evaluate the proposed SMTJ design with experimentally benchmarked spin-circuit models, accounting for transport physics, coupled with the stochastic Landau-Lifshitz-Gilbert equation for magnetization dynamics. We find that the use of low-barrier SAF layers reduces dipolar coupling, achieving uncorrelated fluctuations at zero-magnetic field, surviving up to diameters exceeding D≈100nm if the nanomagnets can be made thin enough (≈1–2nm). The double-free-layer structure retains bias independence and the circular nature of the nanomagnets provides near-uniform randomness with fast fluctuations. Combining our full SMTJ model with advanced transistor models, we estimate the energy to generate a random bit to be about 3.6fJ, with fluctuation rates of about 3.3GHz per p-bit. Our results will guide the experimental development of superior stochastic magnetic tunnel junctions for large-scale and energy-efficient probabilistic computation for problems relevant to machine learning and artificial intelligence.

关键词： Machine learning Neuromorphic computing Probabilistic computing Spintronics Stochastic processes Devices for digital logic, storage & processing Magnetic tunnel junctions Synthetic antiferromagnetic multilayers

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Learning generalized causal structure in time-series

arXiv

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arXiv 2021年

作者： Kathpalia, Aditi Charantimath, Keerti Panchakshari Nagaraj, Nithin Department of Complex Systems Institute of Computer Science of the Czech Academy of Sciences Prague Czech Republic Department of Mathematics Indian Institute of Technology West Bengal Kharagpur India Consciousness Studies Programme National Institute of Advanced Studies Indian Institute of Science Campus Bengaluru India

The science of causality explains/determines 'cause-effect' relationship between the entities of a system by providing mathematical tools for the purpose. In spite of all the success and widespread applications of machine-learning (ML) algorithms, these algorithms are based on statistical learning alone. Currently, they are nowhere close to'human-like' intelligence as they fail to answer and learn based on the important "Why?" questions. Hence, researchers are attempting to integrate ML with the science of causality. Among the many causal learning issues encountered by ML, one is that these algorithms are dumb to the temporal order or structure in data. In this work we develop a machine learning pipeline based on a recently proposed 'neurochaos' feature learning technique (ChaosFEX feature extractor), that helps us to learn generalized causal-structure in given time-series data. © 2021, CC BY.

关键词： Time series

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Weapon Engagement Zone Maximum Launch Range Estimation Using a Deep Neural Network

arXiv

引用

arXiv 2021年

作者： Dantas, Joao P.A. Costa, Andre N. Geraldo, Diego Maximo, Marcos R.O.A. Yoneyama, Takashi Decision Support Systems Subdivision Institute for Advanced Studies Sao Jose dos Campos12.288-001 Brazil Computer Science Division Aeronautics Institute of Technology Sao Jose dos Campos12228-900 Brazil Electronic Engineering Division Aeronautics Institute of Technology Sao Jose dos Campos12228-900 Brazil

This work investigates the use of a Deep Neural Network (DNN) to perform an estimation of the Weapon Engagement Zone (WEZ) maximum launch range. The WEZ allows the pilot to identify an airspace in which the available missile has a more significant probability of successfully engaging a particular target, i.e., a hypothetical area surrounding an aircraft in which an adversary is vulnerable to a shot. We propose an approach to determine the WEZ of a given missile using 50,000 simulated launches in variate conditions. These simulations are used to train a DNN that can predict the WEZ when the aircraft finds itself on different firing conditions, with a coefficient of determination of 0.99. It provides another procedure concerning preceding research since it employs a non-discretized model, i.e., it considers all directions of the WEZ at once, which has not been done previously. Additionally, the proposed method uses an experimental design that allows for fewer simulation runs, providing faster model training. © 2021, CC BY.

关键词： Deep neural networks

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Inter-domain alignment for predicting high-resolution brain networks using teacher-student learning

arXiv

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arXiv 2021年

作者： Demir, Başar Bessadok, Alaa Rekik, Islem BASIRA Lab Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Higher Institute of Informatics and Communication Technologies University of Sousse 4011 Tunisia National Engineering School of Sousse University of Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia

Accurate and automated super-resolution image synthesis is highly desired since it has the great potential to circumvent the need for acquiring high-cost medical scans and a time-consuming preprocessing pipeline of neuroimaging data. However, existing deep learning frameworks are solely designed to predict high-resolution (HR) image from a low-resolution (LR) one, which limits their generalization ability to brain graphs (i.e., connectomes). A small body of works has focused on superresolving brain graphs where the goal is to predict a HR graph from a single LR graph. Although promising, existing works mainly focus on superresolving graphs belonging to the same domain (e.g., functional), overlooking the domain fracture existing between multimodal brain data distributions (e.g., morphological and structural). To this aim, we propose a novel inter-domain adaptation framework namely, Learn to SuperResolve Brain Graphs with Knowledge Distillation Network (L2S-KDnet), which adopts a teacher-student paradigm to superresolve brain graphs. Our teacher network is a graph encoder-decoder that firstly learns the LR brain graph embeddings, and secondly learns how to align the resulting latent representations to the HR ground truth data distribution using an adversarial regularization. Ultimately, it decodes the HR graphs from the aligned embeddings. Next, our student network learns the knowledge of the aligned brain graphs as well as the topological structure of the predicted HR graphs transferred from the teacher. We further leverage the decoder of the teacher to optimize the student network. In such a way, we are not only bringing the learned embeddings from both networks closer to each other but also their predicted HR graphs. L2S-KDnet presents the first TS architecture tailored for brain graph super-resolution synthesis that is based on inter-domain alignment. Our experimental results demonstrate substantial performance gains over benchmark methods. Our code is available a

关键词： Graphic methods

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