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Conditional gradient method for stochastic submodular maximization: Closing the gap

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arXiv 2017年

作者： Mokhtari, Aryan Hassani, Hamed Karbasi, Amin Laboratory for Information and Decision Systems MIT Simons Institute for the Theory of Computing University of California Berkeley Department of Electrical and Systems Engineering University of Pennsylvania Department of Electrical Engineering and Computer Science Yale University

In this paper, we study the problem of constrained and stochastic continuous submodular maximization. Even though the objective function is not concave (nor convex) and is defined in terms of an expectation, we develop a variant of the conditional gradient method, called Stochastic Continuous Greedy, which achieves a tight approximation guarantee. More precisely, for a monotone and continuous DR-submodular function and subject to a general convex body constraint, we prove that Stochastic Continuous Greedy achieves a [(1 - 1/e)OPT - ϵ] guarantee (in expectation) with O(1/ϵ3) stochastic gradient computations. This guarantee matches the known hardness results and closes the gap between deterministic and stochastic continuous submodular maximization. By using stochastic continuous optimization as an interface, we also provide the first (1-1/e) tight approximation guarantee for maximizing a monotone but stochastic submodular set function subject to a general matroid constraint. Copyright © 2017, The Authors. All rights reserved.

关键词： Stochastic systems

Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

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arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

Robust Online Spectrum Prediction with Incomplete and Corrupted Historical Observations

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Robust Online Spectrum Prediction with Incomplete and Corrup...

作者： Ding, Guoru Wu, Fan Wu, Qihui Tang, Shaojie Song, Fei Vasilakos, Athanasios V. Tsiftsis, Theodoros A. National Mobile Communications Research Laboratory Southeast University Nanjing210018 China Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electronics and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing210007 China Department of Information Systems University of Texas at Dallas RichardsonTX75080 United States College of Communications Engineering PLA University of Science and Technology Nanjing210007 China Department of Computer Science Electrical and Space Engineering Lulea University of Technology Lulea97187 Sweden School of Engineering Nazarbayev University Astana010000 Kazakhstan

A range of emerging applications, from adaptive spectrum sensing to proactive spectrum mobility, depend on the ability to foresee spectrum state evolution. Despite a number of studies appearing about spectrum prediction, fundamental issues still remain unresolved: 1) The existing studies do not explicitly account for anomalies, which may incur serious performance degradation;2) they focus on the design of batch spectrum prediction algorithms, which limit the scalability to analyze massive spectrum data in real time;3) they assume the historical data are complete, which may not hold in reality. To address these issues, we develop a Robust Online Spectrum Prediction (ROSP) framework, with incomplete and corrupted observations, in this paper. We first present data analytics of real-world spectrum measurements to reveal the correlation structures of spectrum evolution and to analyze the impact of anomalies on the rank distribution of spectrum matrices. Then, from a spectral-temporal 2-D perspective, we formulate the ROSP as a joint optimization problem of matrix completion and recovery by effectively integrating the time series forecasting techniques and develop an alternating direction optimization method to efficiently solve it. We apply ROSP to a wide range of real-world spectrum matrices of popular wireless services. Experiment results show that ROSP outperforms state-of-the-art spectrum prediction schemes. © 1967-2012 IEEE.

关键词： Forecasting

Next generation cloud computing: New Trends and Research directions

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arXiv 2017年

作者： Varghese, Blesson Buyya, Rajkumar School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom Laboratory School of Computing and Information Systems University of Melbourne Australia

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems. Copyright © 2017, The Authors. All rights reserved.

关键词： Cloud security

An environment for sustainable research software in Germany and beyond: Current state, open challenges, and call for action

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arXiv 2020年

作者： Anzt, Hartwig Bach, Felix Druskat, Stephan Löffler, Frank Loewe, Axel Renard, Bernhard Y. Seemann, Gunnar Struck, Alexander Achhammer, Elke Aggarwal, Piush Appel, Franziska Bader, Michael Brusch, Lutz Busse, Christian Chourdakis, Gerasimos Dabrowski, Piotr W. Ebert, Peter Flemisch, Bernd Friedl, Sven Fritzsch, Bernadette Funk, Maximilian D. Gast, Volker Goth, Florian Grad, Jean-Noël Hermann, Sibylle Hohmann, Florian Janosch, Stephan Kutra, Dominik Linxweiler, Jan Muth, Thilo Peters-Kottig, Wolfgang Rack, Fabian Raters, Fabian H.C. Rave, Stephan Reina, Guido Reißig, Malte Ropinski, Timo Schaarschmidt, Joerg Seibold, Heidi Thiele, Jan P. Uekerman, Benjamin Unger, Stefan Weeber, Rudolf Germany Innovative Computing Lab University of Tennessee KnoxvilleTN United States Department of English Studies Friedrich Schiller University Jena Germany Germany Department of Computer Science Humboldt-Universität zu Berlin Germany Heinz Nixdorf Chair for Distributed Information Systems Friedrich Schiller University Jena Germany Michael Stifel Center Jena Germany Center for Computation and Technology Louisiana State University Baton Rouge United States Germany Robert Koch Institute Berlin Germany Hasso Plattner Institute Faculty of Digital Engineering University of Potsdam Germany Institute for Experimental Cardiovascular Medicine University Heart Centre Freiburg Bad Krozingen Germany Faculty of Medicine University of Freiburg Freiburg Germany Matters of Activity. Image Space Material. Cluster of Excellence at Humboldt-Universität zu Berlin Germany Technische Universität München Germany Language Technology Lab Universität Duisburg-Essen Germany Germany Department of Informatics Technical University of Munich Germany Technische Universität Dresden Germany Deutsches Krebsforschungszentrum Heidelberg Germany Scientific Computing in Computer Science Technical University Munich Germany School of Computing Communication and Business Hochschule für Technik und Wirtschaft Berlin Germany Center for Bioinformatics Saar Saarland Informatics Campus Germany Institute for Modelling Hydraulic and Environmental Systems University of Stuttgart Germany Berlin Institute of Health Germany Scientific Computing Alfred Wegener Institute Helmholtz Center for Polar and Marine Research Bremerhaven Germany Max-Planck-Gesellschaft e.V. Institut für Theoretische Physik und Astrophysik Universität Würzburg Germany Institut für Computerphysik University of Stuttgart Germany University Library University of Stuttgart Germany Universität Bremen Germany Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany European Molecular Biology Laboratory Heidelb

Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin. Copyright © 2020, The Authors. All rights reserved.

关键词： Software design

Self-Assembled Plasmonic Pyramids from Anisotropic Nanoparticles for High-Efficient SERS

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Journal of Analysis and Testing 2017年第4期1卷 335-343页

作者： Wenjuan Yang Kae Jye Si Pengzhen Guo Dashen Dong Debabrata Sikdar Malin Premaratne Wenlong Cheng Department of Chemical Engineering Faculty of EngineeringMonash UniversityClaytonVIC 3800Australia The Melbourne Centre for Nanofabrication 151 Wellington RoadClaytonVIC 3168Australia Advanced Computing and Simulation Laboratory(AvL) Department of Electrical and Computer Systems EngineeringFaculty of EngineeringMonash UniversityClaytonVIC 3800Australia Department of Chemistry Faculty of Natural SciencesImperial College LondonExhibition RoadSouth KensingtonLondon SW72AZUK

Surface-enhanced Raman scattering(SERS)substrates play important roles for the enhancement of inelastic scattering *** substrates such as roughened electrodes and colloidal aggregates suffer from well-known signal reproducibility issues,whereas for current dominant two-dimensional planar systems,the hot spot distributions are limited by the zero-,one-or two-dimensional *** introduction of a three-dimensional(3D)system such as a pyramid geometry breaks the limitation of a single Cartesian SERS-active area and extends it into the z-direction,with the tip potentially offering additional benefits of strong field enhancement and high ***,current 3D pyramidal designs are restricted to film deposition on prepared pyramid templates or self-assembly in pyramidal molds with spherical building blocks,hence limiting their SERS ***,we report on the fabrication of a new class of low cost and well-defined plasmonic nanoparticle pyramid arrays from different anisotropic shaped nanoparticles using combined top-down lithography and bottom-up self-assembly *** pyramids exhibit novel optical scattering properties that can be exploited for the design of reproducible and sensitive SERS *** SERS intensity was found to decrease drastically in accordance with a power law function as the focal planes move from the apex of the pyramid structure towards the *** comparison to sphere-based building blocks,pyramids assembled from anisotropic rhombic dodecahedral gold nanocrystals with numerous sharp tips exhibited the strongest SERS *** Abstract Macroscale pyramidal array films with plasmonic tunability as a new class of SERS substrate for sensitive detection of chemicals.

关键词： Self-assembly Pyramid Plasmonics Shape anisotropy Surface-enhanced Raman scattering

Regularized Training of the Extreme Learning Machine using the Conjugate Gradient Method

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Regularized Training of the Extreme Learning Machine using t...

International Joint Conference on Neural Networks

作者： Philip de Chazal Mark D. McDonnell Charles Perkins Centre Faculty of Engineering and Information Technology School of Electrical and Information Engineering The University of Sydney Computational Learning Systems Laboratory Advanced Computing Research Centre School of Information Technology and Mathematical Sciences University of South Australia

ISBN: (纸本)9781509061839

We describe a new algorithm providing regularized training of the extreme learning machine (ELM) that uses a modified conjugate gradient (CG) method to determine the network hidden to output weights. The CG method is modified to include a validation set performance calculation at each iteration step. The solution is initialized to zero and during the CG iterations, we monitor the validation set error. When the error begins to rise we terminate the CG algorithm. The operations per iteration is O(P~2), where P is the number of output weights, which is significantly faster than the O(P~3) operations per iteration required by ridge regression regularization methods. We demonstrate the effectiveness of our method by classifying the MNIST database and achieve an accuracy of 99.2% using an ELM classifier processing the unmodified pixel values.

关键词： Cholesky Conjugate gradient method Early stopping Extreme Learning Machine Regularization QR SVD conjugate gradient methods Learning machines Cholesky iterative methods quick release Swine vesicular disease Singular value decomposition BMP8B protein, human performance calculation gradient methods Ecosystem models Elm TEST SETS

Roadmap on spin-wave computing

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arXiv 2021年

作者： Chumak, Andrii V. Kabos, P. Wu, M. Abert, C. Adelmann, C. Adeyeye, A. Åkerman, J. Aliev, F.G. Anane, A. Awad, A. Back, C.H. Barman, A. Bauer, G.E.W. Becherer, M. Beginin, E.N. Bittencourt, V.A.S.V. Blanter, Y.M. Bortolotti, P. Boventer, I. Bozhko, D.A. Bunyaev, S.A. Carmiggelt, J.J. Cheenikundil, R.R. Ciubotaru, F. Cotofana, S. Csaba, G. Dobrovolskiy, O.V. Dubs, C. Elyasi, M. Fripp, K.G. Fulara, H. Golovchanskiy, I.A. Gonzalez-Ballestero, C. Graczyk, P. Grundler, D. Gruszecki, P. Gubbiotti, G. Guslienko, K. Haldar, A. Hamdioui, S. Hertel, R. Hillebrands, B. Hioki, T. Houshang, A. Hu, C.-M. Huebl, H. Huth, M. Iacocca, E. Jungfleisch, M.B. Kakazei, G.N. Khitun, A. Khymyn, R. Kikkawa, T. Kläui, M. Klein, O. Klos, J.W. Knauer, S. Koraltan, S. Kostylev, M. Krawczyk, M. Krivorotov, I.N. Kruglyak, V.V. Lachance-Quirion, D. Ladak, S. Lebrun, R. Li, Y. Lindner, M. Macêdo, R. Mayr, S. Melkov, G.A. Mieszczak, S. Nakamura, Y. Nembach, H.T. Nikitin, A.A. Nikitov, S.A. Novosad, V. Otalora, J.A. Otani, Y. Papp, A. Pigeau, B. Pirro, P. Porod, W. Porrati, F. Qin, H. Rana, B. Reimann, T. Riente, F. Romero-Isart, O. Ross, A. Sadovnikov, A.V. Safin, A.R. Saitoh, E. Schmidt, G. Schultheiss, H. Schultheiss, K. Serga, A.A. Sharma, S. Shaw, J.M. Suess, D. Surzhenko, O. Szulc, K. Taniguchi, T. Urbánek, M. Usami, K. Ustinov, A.B. van der Sar, T. van Dijken, S. Vasyuchka, V.I. Verba, R. Viola Kusminskiy, S. Wang, Q. Weides, M. Weiler, M. Wintz, S. Wolski, S.P. Zhang, X. Faculty of Physics University of Vienna Boltzmanngasse 5 ViennaA-1090 Austria National Institute of Standards and Technology BoulderCO80305 United States Department of Physics Colorado State University Fort CollinsCO80523 United States Research Platform MMM Mathematics-Magnetism-Materials University of Vienna Oskar-Morgenstern-Platz 1 Vienna1090 Austria Imec Leuven3001 Belgium Department of Physics Durham University South Road DurhamDH1 3LE United Kingdom Department of Physics University of Gothenburg Gothenburg412 96 Sweden Universidad Autónoma de Madrid Madrid28049 Spain Unité Mixte de Physique CNRS Thales Université Paris Saclay Palaiseau91767 France Department of Physics Technical University of Munich Garching85748 Germany Department of Condensed Matter Physics and Material Sciences S N Bose National Centre for Basic Sciences Salt Lake Kolkata700106 India Advanced Institute for Materials Research Tohoku University Sendai980-8577 Japan Zernike Institute for Advanced Materials University of Groningen Netherlands Department of Electrical and Computer Engineering Technical University of Munich Munich80333 Germany Laboratory of Magnetic Metamaterials Saratov State University Saratov410012 Russia Max Planck Institute for the Science of Light Erlangen91058 Germany Department of Quantum Nanoscience Kavli Institute of Nanoscience Delft University of Technology Delft2628 CJ Netherlands Department of Physics and Energy Science University of Colorado Colorado Springs Colorado SpringsCO80918 United States Departamento de Física e Astronomia Universidade do Porto Porto4169-007 Portugal Institut de Physique et Chimie des Matériaux de Strasbourg CNRS Université de Strasbourg StrasbourgF-67000 France Department of Quantum and Computer Engineering Delft University of Technology Delft2628 CD Netherlands Faculty for Information Technology and Bionics Pazmany Peter Catholic University Prater u. 50/A BudapestH-1083 Hungary INNOVENT e.V.

Magnonics is a field of science that addresses the physical properties of spin waves and utilizes them for data processing. Scalability down to atomic dimensions, operations in the GHz-to-THz frequency range, utilization of nonlinear and nonreciprocal phenomena, and compatibility with CMOS are just a few of many advantages offered by magnons. Although magnonics is still primarily positioned in the academic domain, the scientific and technological challenges of the field are being extensively investigated, and many proof-of-concept prototypes have already been realized in laboratories. This roadmap is a product of the collective work of many authors that covers versatile spin-wave computing approaches, conceptual building blocks, and underlying physical phenomena. In particular, the roadmap discusses the computation operations with Boolean digital data, unconventional approaches like neuromorphic computing, and the progress towards magnon-based quantum computing. The article is organized as a collection of sub-sections grouped into seven large thematic sections. Each sub-section is prepared by one or a group of authors and concludes with a brief description of the current challenges and the outlook of the further development of the research directions. © 2021, CC BY.

关键词： Spin waves