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Machine learning analysis of tunnel magnetoresistance of magnetic tunnel junctions with disordered MgAl2O4

Physical Review Research 2020年第2期2卷 023187-023187页

作者： Shenghong Ju Yoshio Miura Kaoru Yamamoto Keisuke Masuda Ken-ichi Uchida Junichiro Shiomi China-UK Low Carbon College Shanghai Jiao Tong University No. 3 Yinlian Road Lingang Shanghai 201306 China Department of Mechanical Engineering The University of Tokyo 7-3-1 Hongo Tokyo 113-8656 Japan Center for Materials Research by Information Integration (CMI2) Research and Services Division of Materials Data and Integrated System (MaDIS) National Institute for Materials Science (NIMS) 1-2-1 Sengen Tsukuba 305-0047 Japan Research Center for Magnetic and Spintronic Materials National Institute for Materials Science (NIMS) 1-2-1 Sengen Tsukuba 305-0047 Japan Center for Spintronics Research Network (CSRN) Graduate School of Engineering Science Osaka University Machikaneyama 1-3 Toyonaka Osaka 560-8531 Japan Core Research for Evolutional Science and Technology (CREST) Japan Science and Technology Agency (JST) 4-1-8 Kawaguchi Saitama 332-0012 Japan Institute for Materials Research Tohoku University Katahira 2-1-1 Sendai 980-8577 Japan Center for Spintronics Research Network Tohoku University Katahira 2-1-1 Sendai 980-8577 Japan

Through Bayesian optimization and the least absolute shrinkage and selection operator (LASSO) technique combined with first-principles calculations, we investigated the tunnel magnetoresistance (TMR) effect of Fe/disordered−MgAl2O4(MAO)/Fe(001) magnetic tunnel junctions (MTJs) to determine the structures of disordered-MAO that give large TMR ratios. The optimal structure with the largest TMR ratio was obtained by Bayesian optimization with 1728 structural candidates, where the convergence was reached within 300 structure calculations. Characterization of the obtained structures suggested that the in-plane distance between two Al atoms plays an important role in determining the TMR ratio. Since the Al-Al distance of disordered MAO significantly affects the imaginary part of complex band structures, the majority-spin conductance of the Δ1 state in Fe/disordered-MAO/Fe MTJs increases with increasing in-plane Al-Al distance, leading to larger TMR ratios. Furthermore, we found that the TMR ratio tended to be large when the ratio of the number of Al, Mg, and vacancies in the [001] plane was 2:1:1, indicating that the control of Al atomic positions is essential to enhancing the TMR ratio in MTJs with disordered MAO. The present work reveals the effectiveness and advantage of materials informatics combined with first-principles transport calculations in designing high-performance spintronic devices based on MTJs.

关键词： Magnetotransport Spintronics Machine learning

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Spintronic superconductor in a bulk layered material with natural spin-valve structure

arXiv

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arXiv 2020年

作者： Sakuragi, Shunsuke Sasaki, S. Akashi, R. Sakagami, R. Kuroda, K. Bareille, C. Hashimoto, T. Nagashima, T. Kinoshita, Y. Hirata, Y. Shimozawa, M. Asai, S. Yajima, T. Doi, S. Tsujimoto, N. Kunisada, S. Noguchi, R. Kurokawa, K. Azuma, N. Hirata, K. Yamasaki, Y. Nakao, H. Kim, T.K. Cacho, C. Masuda, T. Tokunaga, M. Wadati, H. Okazaki, K. Shin, S. Kamihara, Y. Yamashita, Minoru Kondo, Takeshi Institute for Solid State Physics University of Tokyo Kashiwa Chiba Japan Department of Physics University of Tokyo Tokyo Japan Department of Applied Physics and Phisico-Informatics Keio University Yokohama Japan Department of Applied Physics National Defense Academy of Japan Yokosuka Japan Division of Materials Physics Graduate School of Engineering Science Osaka University Toyonaka Japan Research Center for Computational Design of Advanced Functional Materials National Institute of Advanced Industrial Science and Technology Tsukuba Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba Japan RIKEN Center for Emergent Matter Science Wako Japan PRESTO Japan Science and Technology Agency Kawaguchi Japan Condensed Matter Research Center and Photon Factory Institute of Materials Structure Science High Energy Accelerator Research Organization Tsukuba Japan Diamond Light Source Harwell Campus Didcot United Kingdom Graduate School of Material Science University of Hyogo Hyogo Japan TheUniversity of Tokyo Kashiwa Chiba277-8581 Japan Center for Spintronics Research Network Keio University Yokohama Japan

Multi-layered materials provide fascinating platforms to realize various functional properties, possibly leading to future electronic devices controlled by external fields. In particular, layered magnets coupled with conducting layers have been extensively studied recently for possible control of their transport properties via the spin structure. Successful control of quantum-transport properties in the materials with antiferromagnetic (AFM) layers, so-called natural spin-valve structure, has been reported for the Dirac Fermion and topological/axion materials [1-11]. However, a bulk crystal in which magnetic and superconducting layers are alternately stacked has not been realized until now, and the search for functional properties in it is an interesting yet unexplored field in material science. Here, we discover superconductivity providing such an ideal platform in EuSn2As2 with the van der Waals stacking of magnetic Eu layers and superconducting Sn-As layers, and present the first demonstration of a natural spin-valve effect on the superconducting current. Below the superconducting transition temperature (Tc), the electrical resistivity becomes zero in the in-plane direction. In contrast, it, surprisingly, remains finite down to the lowest temperature in the out-of-plane direction, mostly due to the structure of intrinsic magnetic Josephson junctions in EuSn2As2. The magnetic order of the Eu layers (or natural spin-valve) is observed to be extremely soft, allowing one to easy control of the out-of-plane to in-plane resistivities ratio from 1 to ∞ by weak external magnetic fields. The concept of multi-functional materials with stacked magnetic-superconducting layers will open a new pathway to develop novel spintronic devices with magnetically controllable superconductivity. Copyright © 2020, The Authors. All rights reserved.

关键词： Europium compounds

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Bridging the Gap between Structurally Distinct 2D Lamellar Zeolitic Precursors through a 3D Germanosilicate Intermediate

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Angewandte Chemie 2019年第41期131卷

作者： Le Xu Madhuresh K. Choudhary Koki Muraoka Watcharop Chaikittisilp Toru Wakihara Jeffrey D. Rimer Tatsuya Okubo Department of Chemical System Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Department of Chemical and Biomolecular Engineering University of Houston Houston TX 77204 USA Research and Services Division of Materials Data and Integrated System (MaDIS) National Institute for Materials Sciences (NIMS) 1-1 Namiki Tsukuba Ibaraki 305-0044 Japan

There is broad scientific interest in lamellar zeolitic materials for a large variety of technological applications. The traditional synthetic methods towards two‐dimensional (2D) zeolitic precursors have made a great impact in the construction of families of related zeolites; however, the connection between structurally distinct 2D zeolitic precursors is much less investigated in comparison, thereby resulting in a synthetic obstacle that theoretically limits the types of zeolites that can be constructed from each layer. Herein, we report a Ge‐recycling strategy for the topotactic conversion between different 2D zeolitic precursors through a three‐dimensional (3D) germanosilicate. Specifically, the intermediate germanosilicate can be constructed within 150 min by taking advantage of its structural similarity with the parent lamellar precursor. This process enables the conversion of one 2D zeolite structure into another distinct structure, thus overcoming the synthetic obstacle between two families of zeolitic materials.

关键词： Kristallwachstum Strukturtransformation Schichtverbindungen Zeolithe

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Progressive Refinement Bilateral Filter

Progressive Refinement Bilateral Filter

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IEEE International Conference on Automation, Electronics and Electrical engineering (AUTEEE)

作者： Chanyi Lu Yong Zhao Lin Wang Guiying Zhang Fujian Feng Li Zhang College of Data Science and Information Engineering Guizhou Minzu University Guiyang China School of Electronic and Computer Engineering Shenzhen Graduate School of Peking University Shenzhen China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China Department of Medical Information Engineering Zunyi Medical University Zunyi China

ISBN: (纸本)9781538678626;9781538678619

In this paper, a coarse-to-fine framework for image noise removal is proposed. The bilateral filter is redefined by the manner of progressive refining to effectively eliminate noise, thus forming progressive refinement bilateral filter (PRBF), which estimates the pixel values of a noisy image through the method of progressive refinement with gradual recursion and its result retains more details, infinite close to the original image. PRBF is further integrated into the denoising model for the details processing again after denoising of the major part of the image. Our algorithm is evaluated and compared on two large standardized datasets and a number of selected images. The experimental results show that our algorithm consistently outperforms other approaches for image denoising and demonstrates the effectiveness of our framework for stereo matching.

关键词： Noise reduction Image denoising Mathematical model Filtering Filtering algorithms Kernel Biomedical imaging

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A transcriptome-based human universal senescence index (hUSI) robustly predicts cellular senescence under various conditions

Nature aging

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Nature aging 2025年 2025 May 29页

作者： Jing Wang Xiaolan Zhou Peng Yu Jun Yao Pengfei Guo Qiushi Xu Yuqi Zhao Guanlong Wang Qianru Li Xiaofeng Zhu Gang Wei Weixu Wang Ting Ni State Key Laboratory of Genetics and Development of Complex Phenotypes National Clinical Research Center for Aging and Medicine Huashan Hospital Collaborative Innovation Center of Genetics and Development Human Phenome Institute Center for Evolutionary Biology Shanghai Engineering Research Center of Industrial Microorganisms School of Life Sciences Fudan University Shanghai China. Department of Cardiology Zhongshan Hospital Fudan University Shanghai Institute of Cardiovascular Diseases Shanghai China. Department of Data System 3D Medicines Inc. Shanghai China. State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock Institutes of Biomedical Sciences School of Life Sciences Inner Mongolia University Hohhot China. State Key Laboratory of Genetics and Development of Complex Phenotypes National Clinical Research Center for Aging and Medicine Huashan Hospital Collaborative Innovation Center of Genetics and Development Human Phenome Institute Center for Evolutionary Biology Shanghai Engineering Research Center of Industrial Microorganisms School of Life Sciences Fudan University Shanghai China. gwei@***. State Key Laboratory of Genetics and Development of Complex Phenotypes National Clinical Research Center for Aging and Medicine Huashan Hospital Collaborative Innovation Center of Genetics and Development Human Phenome Institute Center for Evolutionary Biology Shanghai Engineering Research Center of Industrial Microorganisms School of Life Sciences Fudan University Shanghai China. weixu.wang@helmholtz-munich.de. Institute of Computational Biology Helmholtz Center Munich Munich Germany. weixu.wang@helmholtz-munich.de. TUM School of Life Sciences Weihenstephan Technical University of Munich Munich Germany. weixu.wang@helmholtz-munich.de. State Key Laboratory of Genetics and Development of Complex Phenotypes National Clinical Research Center for Aging and Medicine Huashan Hospital Collaborative Innovation Center of Genetics and Development Hu

Despite the manifestation and contribution of cellular senescence to aging and various diseases, accurate identification of heterogeneous senescent cells remains challenging. Current senescence evaluation methods rely mainly on limited markers or homogeneous samples, which might fail to capture universal senescence features, limiting their generalizability. Here we developed the human universal senescence index (hUSI), an accurate and robust senescence evaluation method for diverse cells and samples. Based on features learned from the most comprehensive cellular senescence-associated transcriptome data so far, hUSI demonstrated its convincing connections with senescence phenotypes and superior robustness in predicting senescence state. Using hUSI, we discovered potential senescence regulators and mapped senescent cell accumulation across cell types in coronavirus disease 2019 (COVID-19). The method also facilitates decoding heterogeneous senescence states in melanoma tumors, identifying prognosis-associated signaling pathways. Overall, hUSI demonstrates its utility in characterizing cellular senescence across biological contexts, with broad applications in aging research and clinical practice.

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Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

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Sunspot Observations by Hisako Koyama: 1945 – 1996

arXiv

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arXiv 2019年

作者： Hayakawa, Hisashi Clette, Frédéric Horaguchi, Toshihiro Iju, Tomoya Knipp, Delores J. Liu, Huixin Nakajima, Takashi Graduate School of Letters Osaka University Toyonaka5600043 Japan UK Solar System Data Centre Space Physics and Operations Division RAL Space Science and Technology Facilities Council Rutherford Appleton Laboratory Harwell Oxford Didcot OxfordshireOX11 0QX United Kingdom World Data Center SILSO Royal Observatory of Belgium 3 avenue Circulaire Brussels1180 Belgium National Museum of Nature and Science Tsukuba3050005 Japan National Astronomical Observatory of Japan Mitaka1818588 Japan High Altitude Observatory National Center for Atmospheric Research BoulderCO80307 United States Smead Aerospace Engineering Sciences Department University of Colorado Boulder BoulderCO80309 United States FFaculty of Science Department of Earth and Planetary Science Kyushu University Fukuoka8190395 Japan

The sunspot record is the only observational tracer of solar activity that provides a fundamental, multi-century reference. Its homogeneity has been largely maintained with a succession of long-duration visual observers. In this paper, we examine observations of one of the primary reference sunspot observers, Hisako Koyama. By consulting original archives of the National Museum of Nature and Science of Japan (hereafter, NMNS), we retrace the main steps of her solar-observing career, from 1945 to 1996. We also present the reconstruction of a full digital database of her sunspot observations at the NMNS, with her original drawings and logbooks. Here, we extend the availability of her observational data from 1947-1984 to 1945-1996. Comparisons with the international sunspot number (version 2) and with the group sunspot number series show a good global stability of Koyama’s observations, with only temporary fluctuations over the main interval 1947-1982. Identifying drawings made by alternate observers throughout the series, we find that a single downward baseline shift in the record coincides with the partial contribution of replacement observers mostly after 1983. We determine the correction factor to bring the second part (1983-1996) to the same scale with Koyama’s main interval (1947-1982). We find a downward jump by 9% after 1983, which then remains stable until 1996. Overall, the high quality of Koyama’s observations with her life-long dedication leaves a lasting legacy of this exceptional personal achievement. With this comprehensive recovery, we now make the totality of this legacy directly accessible and exploitable for future research. Copyright © 2019, The Authors. All rights reserved.

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Forecasting transportation network speed using deep capsule networks with nested LSTM models

arXiv

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arXiv 2018年

作者： Ma, Xiaolei Li, Yi Cui, Zhiyong Wang, Yinhai School of Transportation Science and Engineering Beijing Key Laboratory for Cooperative Vehicle Infrastructure System and Safety Control Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Department of Civil and Environmental Engineering University of Washington Seattle98195 United States

Accurate and reliable traffic forecasting for complicated transportation networks is of vital importance to modern transportation management. The complicated spatial dependencies of roadway links and the dynamic temporal patterns of traffic states make it particularly challenging. To address these challenges, we propose a new capsule network (CapsNet) to extract the spatial features of traffic networks and utilize a nested LSTM (NLSTM) structure to capture the hierarchical temporal dependencies in traffic sequence data. A framework for network-level traffic forecasting is also proposed by sequentially connecting CapsNet and NLSTM. On the basis of literature review, our study is the first to adopt CapsNet and NLSTM in the field of traffic forecasting. An experiment on a Beijing transportation network with 278 links shows that the proposed framework with the capability of capturing complicated spatiotemporal traffic patterns outperforms multiple state-of-the-art traffic forecasting baseline models. The superiority and feasibility of CapsNet and NLSTM are also demonstrated, respectively, by visualizing and quantitatively evaluating the experimental results. Copyright © 2018, The Authors. All rights reserved.

关键词： Long short-term memory

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Adaptive Clutter Suppression Algorithm for Detection and Positioning using IR-UWB Radar

Adaptive Clutter Suppression Algorithm for Detection and Pos...

引用

International Conference on Ultrawideband and Ultrashort Impulse Signals

作者： Sungwon Yoo Sungmoon Chung Dong-Min Seol Sung Ho Cho Department of Electronics and Computer Engineering Hanyang University Seoul Republic of Korea Agency of Defense Development Defense Industry Technology Industry Center Seoul Republic of Korea Data Link & Future Soldier System LIGNex1 Co. Ltd. Seongnam Republic of Korea

In this paper, we propose a novel clutter suppression method based on impulse radio ultra-wideband (IR-UWB) radar system. Unlike conventional moving target indicator (MTI) algorithms, our proposed algorithm can effectively remove clutter signal not only from static objects but also from multipath effects between humans and objects. It can be accomplished by changing the application ratio of clutter suppression algorithm according to the circumstance of radar coverage. It results higher peak to average power ratio (PAPR) and signal to clutter ratio (SCR), so other post-processing likes detection or positioning operates more stable. We evaluate this algorithm's performance by comparing conventional algorithm through some experimental results.

关键词： Clutter Moving target indicator Detection Positioning IR-UWB radar

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Bayesian spectroscopy in solid-state photo-physics

Bayesian spectroscopy in solid-state photo-physics

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International Meeting on High-Dimensional data-Driven Science, HD3 2017

作者： Akai, Ichiro Iwamitsu, Kazunori Okada, Masato Institute of Pulsed Power Science Kumamoto University 2-39-1 Kurokami Chuo-ku Kumamoto860-8555 Japan Faculty of Science Kumamoto University 2-39-1 Kurokami Chuo-ku Kumamoto860-8555 Japan Department of Complexity Science and Engineering University of Tokyo 5-1-5 Kashiwanoha Kashiwa Chiba277-8561 Japan Research and Service Division of Materials Data and Integrated System National Institute for Material Science 1-2-1 Sengen Ibaraki Tsukuba305-0047 Japan

We propose Bayesian spectroscopy (BS) both for the spectral analyses of optical spectra and for normal mode analyses of coherent phonon (CP) signals in solid-state materials. In optical spectra due to elementary electronic excitations, multiple excitonic absorption peaks appear and are overlapped with other background optical transitions, which makes their spectral decomposition difficult. We demonstrate that BS can be used to estimate the band gap and the excitonic binding energies with high accuracies by the introduction of physical laws related with the excitonic transitions. For normal mode analyses of CP signals, we show that BS can estimate the frequency and initial vibrating phase of the normal mode with high accuracies. In contrast to the Fourier transform spectrum which has a broad spectral width on account of the damping behaviors of the CP signals, BS with a physical model of a damped oscillation improves the estimation accuracy of the normal mode frequency by two orders of magnitude compared to the Fourier transform method. These results indicate the significant advantages of BS over conventional methodologies such as the least-squares and Fourier transform methods. © Published under licence by IOP Publishing Ltd.

关键词： Energy gap

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