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Tunable magnetization relaxation of Fe2Cr1−xCoxSi half-metallic Heusler alloys by band structure engineering

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Physical Review Materials 2017年第6期1卷 064401-064401页

作者： Shikun He Yifan Liu Yuhong Zheng Qing Qin Zhenchao Wen Qingyun Wu Yi Yang Yupu Wang YuanPing Feng Kie Leong Teo Christos Panagopoulos Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Data Storage Institute Agency for Science Technology and Research (A*STAR) 2 Fusionopolis Way 08-01 Innovis Singapore 138634 Department of Electrical and Computer Engineering National University of Singapore Singapore 117582 Department of Materials Science and Engineering National University of Singapore Singapore 117574 Center for Spintronics Research Network (CSRN) Tohoku University Sendai 980-8577 Japan Department of Physics National University of Singapore Singapore 117542

We report a systematic investigation on the magnetization relaxation properties of iron-based half-metallic Heusler alloy Fe2Cr1−xCoxSi (FCCS) thin films using broadband angular-resolved ferromagnetic resonance. Band structure engineering through Co doping (x) demonstrated by first-principles calculations is shown to tune the intrinsic magnetic damping over an order of magnitude, namely 1×10−2–8×10−4. Notably, the intrinsic damping constants for samples with high Co concentration are among the lowest reported for Heusler alloys and even comparable to magnetic insulator yttrium iron garnet. Furthermore, a significant reduction of both isotropic and anisotropic contributions of extrinsic damping of the FCCS alloys was found in the FCCS films with x=0.5–0.75, which is of particular importance for applications. These results demonstrate a practical recipe to tailor functional magnetization for Heusler alloy-based spintronics at room temperature.

关键词： Ferromagnetism Spin relaxation Spin transfer torque

Coherent oscillations of driven rf SQUID metamaterials

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Physical Review E 2017年第5期95卷 050201(R)-050201(R)页

作者： Melissa Trepanier Daimeng Zhang Oleg Mukhanov V. P. Koshelets Philipp Jung Susanne Butz Edward Ott Thomas M. Antonsen Alexey V. Ustinov Steven M. Anlage Department of Physics CNAM University of Maryland College Park Maryland 20742 USA Department of Electrical and Computer Engineering University of Maryland College Park Maryland 20742 USA Hypres Inc. 175 Clearbrook Road Elmsford New York 10523 USA Laboratory of Superconducting Devices for Signal Detection and Processing Kotelnikov Institute of Radio Engineering and Electronics Moscow 125009 Russia Physikalisches Institut Karlsruhe Institute of Technology D-76049 Karlsruhe Germany Russian Quantum Center National University of Science and Technology MISIS Moscow 119049 Russia

Through experiments and numerical simulations we explore the behavior of rf SQUID (radio frequency superconducting quantum interference device) metamaterials, which show extreme tunability and nonlinearity. The emergent electromagnetic properties of this metamaterial are sensitive to the degree of coherent response of the driven interacting SQUIDs. Coherence suffers in the presence of disorder, which is experimentally found to be mainly due to a dc flux gradient. We demonstrate methods to recover the coherence, specifically by varying the coupling between the SQUID meta-atoms and increasing the temperature or the amplitude of the applied rf flux.

关键词： Collective dynamics Coupled oscillators Josephson effect Magnetic susceptibility Optical & microwave phenomena Synchronization Metamaterials

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

Graphene-based Josephson junction single photon detector

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arXiv 2017年

作者： Walsh, Evan D. Efetov, Dmitri K. Lee, Gil-Ho Heuck, Mikkel Crossno, Jesse Ohki, Thomas A. Kim, Philip Englund, Dirk Fong, Kin Chung Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States 08860 Spain Department of Physics Harvard University CambridgeMA02138 United States Raytheon BBN Technologies Quantum Information Processing Group CambridgeMA02138 United States

We propose to use graphene-based Josephson junctions (gJjs) to detect single photons in a wide electromagnetic spectrum from visible to radio frequencies. Our approach takes advantage of the exceptionally low electronic heat capacity of monolayer graphene and its constricted thermal conductance to its phonon degrees of freedom. Such a system could provide high sensitivity photon detection required for research areas including quantum information processing and radio-astronomy. As an example, we present our device concepts for gJj single photon detectors in both the microwave and infrared regimes. The dark count rate and intrinsic quantum efficiency are computed based on parameters from a measured gJj, demonstrating feasibility within existing technologies. Copyright © 2017, The Authors. All rights reserved.

关键词： Graphene

Visual Reasoning Indexing and Retrieval Using In-Memory Computing

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Visual Reasoning Indexing and Retrieval Using In-Memory Comp...

作者： Cao, Hongfei Li, Yu Allen, Carla M. Phinney, Michael A. Shyu, Chi-Ren Computer Science Interdisciplinary Data Analytics and Search Lab University of Missouri-Columbia ColumbiaMO65211 United States Clinical and Diagnostic Science Department University of Missouri-Columbia ColumbiaMO65211 United States Computer Science Electrical and Computer Engineering Informatics Institute Interdisciplinary Data Analytics and Search Lab University of Missouri-Columbia ColumbiaMO65211 United States

Research has shown that visual information of multimedia is critical in highly-skilled applications, such as biomedicine and life sciences, and a certain visual reasoning process is essential for meaningful search in a timely manner. Relevant image characteristics are learned and verified with accumulated experiences during the reasoning processes. However, such processes are highly dynamic and elusive to computationally quantify and therefore challenging to analyze, let alone to make the knowledge sharable across users. In this paper we study real-time human visual reasoning processes with the aid of gaze tracking devices. Temporal and spatial representations are proposed for gaze modeling, and a visual reasoning retrieval system utilizing in-memory computing under Big data ecosystems is designed for real-time search of similar reasoning models. Simulated data derived from human subject experiments show that the system has a reasonably high accuracy and provides predictive estimations for hardware requirements versus data sizes for exhaustive searches. Comparison between various visual action classifiers show challenges in modeling visual actions. The proposed system provides a theoretical framework and computing platform for advancement in visual semantic computing, as well as potential applications in medicine, social science, and arts. © 2016 World Scientific Publishing Company.

关键词： Eye tracking

Network slicing for service-oriented networks under resource constraints

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arXiv 2017年

作者： Zhang, Nan Liu, Ya-Feng Farmanbar, Hamid Chang, Tsung-Hui Hong, Mingyi Luo, Zhi-Quan School of Mathematical Sciences Peking University State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Huawei Canada Research Center Ottawa Canada Department of Electrical and Computer Engineering University of Minnesota United States Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen China

To support multiple on-demand services over fixed communication networks, network operators must allow flexible customization and fast provision of their network resources. One effective approach to this end is network virtualization, whereby each service is mapped to a virtual subnetwork providing dedicated on-demand support to network users. In practice, each service consists of a prespecified sequence of functions, called a service function chain (SFC), while each service function in a SFC can only be provided by some given network nodes. Thus, to support a given service, we must select network function nodes according to the SFC and determine the routing strategy through the function nodes in a specified order. A crucial network slicing problem that needs to be addressed is how to optimally localize the service functions in a physical network as specified by the SFCs, subject to link and node capacity constraints. In this paper, we formulate the network slicing problem as a mixed binary linear program and establish its strong NP-hardness. Furthermore, we propose efficient penalty successive upper bound minimization (PSUM) and PSUM-R(ounding) algorithms, and two heuristic algorithms to solve the problem. Simulation results are shown to demonstrate the effectiveness of the proposed algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Network function virtualization

Low dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss

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arXiv 2017年

作者： Yang, Qingsong Yan, Pingkun Zhang, Yanbo Yu, Hengyong Shi, Yongyi Mou, Xuanqin Kalra, Mannudeep K. Zhang, Yi Sun, Ling Wang, Ge Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Electrical and Computer Engineering University of Massachusetts Lowell LowellMA01854 United States Institute of Image Processing and Pattern Recognition Xian Jiaotong University Xian Shaanxi710049 China Department of Radiology Massachusetts General Hospital Harvard Medical School BostonMA United States College of Computer Science Sichuan Universiyt Chengdu610065 China Department of Radiology West China Hospital Sichuan University Chengdu610041 China

The continuous development and extensive use of CT in medical practice has raised a public concern over the associated radiation dose to the patient. Reducing the radiation dose may lead to increased noise and artifacts, which can adversely affect the radiologists judgement and confidence. Hence, advanced image reconstruction from low-dose CT data is needed to improve the diagnostic performance, which is a challenging problem due to its ill-posed nature. Over the past years, various low-dose CT methods have produced impressive results. However, most of the algorithms developed for this application, including the recently popularized deep learning techniques, aim for minimizing the mean-squared-error (MSE) between a denoised CT image and the ground truth under generic penalties. Although the peak signal-to-noise ratio (PSNR) is improved, MSE-or weighted-MSE-based methods can compromise the visibility of important structural details after aggressive denoising. This paper introduces a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. The Wasserstein distance is a key concept of the optimal transport theory, and promises to improve the performance of GAN. The perceptual loss suppresses noise by comparing the perceptual features of a denoised output against those of the ground truth in an established feature space, while the GAN focuses more on migrating the data noise distribution from strong to weak statistically. Therefore, our proposed method transfers our knowledge of visual perception to the image denoising task and is capable of not only reducing the image noise level but also trying to keep the critical information at the same time. Promising results have been obtained in our experiments with clinical CT images. Copyright © 2017, The Authors. All rights reserved.

关键词： Image denoising

Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

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Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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Deep learning for multi-messenger astrophysics a gateway for discovery in the big data era

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arXiv 2019年

作者： Allen, Gabrielle Andreoni, Igor Bachelet, Etienne Bruce Berriman, G. Bianco, Federica B. Biswas, Rahul Kind, Matias Carrasco Chard, Kyle Cho, Minsik Cowperthwaite, Philip S. Etienne, Zachariah B. George, Daniel Gibbs, Tom Graham, Matthew Gropp, William Gupta, Anushri Haas, Roland Huerta, E.A. Jennings, Elise Katz, Daniel S. Khan, Asad Kindratenko, Volodymyr Kramer, William T.C. Liu, Xin Mahabal, Ashish McHenry, Kenton Miller, J.M. Neubauer, M.S. Oberlin, Steve Olivas, Alexander R. Rosofsky, Shawn Ruiz, Milton Saxton, Aaron Schutz, Bernard Schwing, Alex Seidel, Ed Shapiro, Stuart L. Shen, Hongyu Shen, Yue Sipocz, Brigitta M. Sun, Lunan Towns, John Tsokaros, Antonios Wei, Wei Wells, Jack Williams, Timothy J. Xiong, Jinjun Zhao, Zhizhen NCSA University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States California Institute of Technology 1200 E California Blvd PasadenaCA91125 United States Las Cumbres Observatory 6740 Cortona Drive Suite 102 GoletaCA93117 United States Caltech/IPAC-NExScI 1200 E California Blvd PasadenaCA91125 United States Department of Physics and Astronomy University of Delaware NewarkDE19716 United States Joseph R. Biden Jr . School of Public Policy and Administration University of Delaware NewarkDE19716 United States Data Science Institute University of Delaware NewarkDE19716 United States Center for Urban Science and Progress New York University 370 Jay St BrooklynNY11201 United States The Oskar Klein Centre for Cosmoparticle Physics Stockholm University AlbaNova StockholmSE-106 91 Sweden University of Chicago ChicagoIL60605 United States IBM Systems AustinTX78717 United States Observatories of the Carnegie Institution for Science 813 Santa Barbara St. PasadenaCA91101 United States Department of Mathematics West Virginia University MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States NVIDIA 2788 San Tomas Expressway Santa ClaraCA95050 United States Center for Data Driven Discovery Caltech PasadenaCA91125 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Argonne National Laboratory Leadership Computing Facility LemontIL60439 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 Unit

This report provides an overview of recent work that harnesses the Big data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at the National Center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics;(ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data;(iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups;(iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big data Era;(v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics. Copyright © 2019, The Authors. All rights reserved.

关键词： Big data