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Aggregation of flexible domestic heat pumps for the provision of reserve in power systems 30

Aggregation of flexible domestic heat pumps for the provisio...

30th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy systems, ECOS 2017

作者： Georges, E. Quoilin, S. Mathieu, S. Lemort, V. Energy Systems Research Unit Thermodynamics Laboratory University of Liège Liège Belgium Systems and Modeling Research Unit Department of Electrical Engineering and Computer Science University of Liège Liège Belgium

The integration of renewable energy sources in the electricity production mix has an important impact on the management of the electricity grid, due to their intermittency. In particular, there is a rising need for flexibility, both on the supply and demand sides. This paper assesses the amount of flexibility that could be reserved from a set of flexible residential heat pumps in a given geographical area. It addresses the problem of a load aggregator controlling a set of heat pumps used to provide both space-heating and domestic hot water. The flexibility of the heat pumps is unlocked in order to reduce electricity procurement costs in the day-ahead electricity market, while ensuring the provision of a predefined amount of reserve for real-time grid management. The objective of the paper is two-fold. On the one hand, an aggregation method of large sets of heat pumps based on physics-based models and random sampling techniques is proposed. On the other hand, a combined optimization problem is formulated to determine both the optimal electricity demand profile to be bought on the day-ahead market and the cost associated to the reservation of a defined amount of power. The method is applied to a set of 40000 residential heat pumps in Belgian houses. Results show that these houses can provide up to 100MW of upward reserve for 50% of the current costs. The provision of downward reserve at competitive cost is hampered by significant overconsumption. © 2017 IMEKO

关键词： Housing

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Cribriform pattern detection in prostate histopathological images using deep learning models

arXiv

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arXiv 2019年

作者： Singh, Malay Kalaw, Emarene Mationg Jie, Wang Al-Shabi, Mundher Wong, Chin Fong Giron, Danilo Medina Chong, Kian-Tai Tan, Maxine Zeng, Zeng Lee, Hwee Kuan Unit Institute of Molecular and Cell Biology Singapore Imaging Informatics Division Bioinformatics Institute Singapore Department of Computer Science School of Computing National University of Singapore Singapore UQ Centre for Clinical Research University of Queensland Brisbane Australia Distributed Analytics Lab Institute for Infocomm Research Singapore School of Engineering Monash University Malaysia Selangor Darul Ehsan Malaysia Department of Pathology Tan Tock Seng Hospital Singapore PanAsia Surgery Pte Ltd Mount Elizabeth Novena Hospital Singapore Surgi-TEN Specialists Pte Ltd Farrer Park Hospital Singapore Singapore Singapore Eye Research Institute Singapore

Architecture, size, and shape of glands are most important patterns used by pathologists for assessment of cancer malignancy in prostate histopathological tissue slides. Varying structures of glands along with cumbersome manual observations may result in subjective and inconsistent assessment. Cribriform gland with irregular border is an important feature in Gleason pattern 4. We propose using deep neural networks for cribriform pattern classification in prostate histopathological images. 163708 Hematoxylin and Eosin (H&E) stained images were extracted from histopathologic tissue slides of 19 patients with prostate cancer and annotated for cribriform patterns. Our automated image classification system analyses the H&E images to classify them as either 'Cribriform' or 'Non-cribriform'. Our system uses various deep learning approaches and hand-crafted image pixel intensity-based features. We present our results for cribriform pattern detection across various parameters and configuration allowed by our system. The combination of fine-tuned deep learning models outperformed the state-of-art nuclei feature based methods. Our image classification system achieved the testing accuracy of 85.93 ± 7.54 (cross-validated) and 88.04 ± 5.63 (additional unseen test set) across three folds. In this paper, we present an annotated cribriform dataset along with analysis of deep learning models and hand-crafted features for cribriform pattern detection in prostate histopathological images. Copyright © 2019, The Authors. All rights reserved.

关键词： Urology

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LifeTime and improving European healthcare through cell-based interceptive medicine (vol 587, pg 377, 2020)

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NATURE 2021年第7852期592卷 E8-E8页

作者： Rajewsky, Nikolaus Almouzni, Genevieve Gorski, Stanislaw A. Aerts, Stein Amit, Ido Bertero, Michela G. Bock, Christoph Bredenoord, Annelien L. Cavalli, Giacomo Chiocca, Susanna Clevers, Hans De Strooper, Bart Eggert, Angelika Ellenberg, Jan Fernandez, Xose M. Figlerowicz, Marek Gasser, Susan M. Hubner, Norbert Kjems, Jorgen Knoblich, Jurgen A. Krabbe, Grietje Lichter, Peter Linnarsson, Sten Marine, Jean-Christophe Marioni, John C. Marti-Renom, Marc A. Netea, Mihai G. Nickel, Dorthe Nollmann, Marcelo Novak, Halina R. Parkinson, Helen Piccolo, Stefano Pinheiro, Ines Pombo, Ana Popp, Christian Reik, Wolf Roman-Roman, Sergio Rosenstiel, Philip Schultze, Joachim L. Stegle, Oliver Tanay, Amos Testa, Giuseppe Thanos, Dimitris Theis, Fabian J. Torres-Padilla, Maria-Elena Valencia, Alfonso Vallot, Celine van Oudenaarden, Alexander Vidal, Marie Voet, Thierry Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine in the Helmholtz Association Berlin Germany Charité-Universitätsmedizin Berlin Germany Berlin Institute of Health (BIH) Berlin Germany German Center for Cardiovascular Research (DZHK) Partner Site Berlin Berlin Germany Institute for Biology Humboldt University of Berlin Berlin Germany Cardiovascular and Metabolic Sciences Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin Germany Department of Pediatric Oncology/Hematology Charité-Universitätsmedizin Berlin Berlin Germany Institut Curie CNRS PSL Research University Sorbonne Université Nuclear Dynamics Unit Equipe Labellisée Ligue contre le cancer Paris France VIB Center for Brain and Disease Research Leuven Belgium Department of Human Genetics KU Leuven Leuven Belgium Department of Neurosciences KU Leuven Leuven Belgium UK Dementia Research Institute at UCL University College London London UK Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK Department of Immunology Weizmann Institute of Science Rehovot Israel Centre for Genomic Regulation (CRG) Barcelona Institute of Science and Technology Barcelona Spain CNAG-CRG Centre for Genomic Regulation Barcelona Institute of Science and Technology Barcelona Spain Universitat Pompeu Fabra Barcelona Spain ICREA Barcelona Spain CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences Vienna Austria Department of Laboratory Medicine Medical University of Vienna Vienna Austria Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases Vienna Austria Department of Medical Humanities Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht The Netherlands Institute of Human Genetics UMR 9002 CNRS and University of Montpellier Montpellier France Department of Experimental Oncology IEO European Institute of Oncology IRCCS Milan Italy Department of Oncology and Hemato

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Music, Computing, and Health: A Roadmap for the Current and Future Roles of Music Technology for Health Care and Well-Being

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Music and Science 2021年 4卷

作者： Agres, Kat R. Schaefer, Rebecca S. Volk, Anja van Hooren, Susan Holzapfel, Andre Dalla Bella, Simone Müller, Meinard de Witte, Martina Herremans, Dorien Ramirez Melendez, Rafael Neerincx, Mark Ruiz, Sebastian Meredith, David Dimitriadis, Theo Magee, Wendy L. Yong Siew Toh Conservatory of Music National University of Singapore Singapore Social and Cognitive Computing Department Institute of High Performance Computing A*STAR Singapore Institute for Psychology Health Medical Neuropsychology Unit Leiden University Leiden Netherlands Leiden Institute for Brain and Cognition Leiden University Leiden Netherlands Academy of Creative and Performing Arts Leiden University Leiden Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Faculty of Health care Department of Arts Therapies Zuyd University of Applied Sciences Heerlen Netherlands KenVaK Research Centre for the Arts Therapies and Psychomotricity Heerlen Netherlands Faculty of Psychology Open University of The Netherlands Heerlen Netherlands Division of Media Technology and Interaction Design KTH Royal Institute of Technology Stockholm Sweden International Laboratory for Brain Music and Sound Research (BRAMS) Outremont QC Canada Department of Psychology University of Montreal Montreal QC Canada Centre for Research on Brain Language and Music (CRBLM) Montreal QC Canada University of Economics and Human Sciences in Warsaw Warsaw Poland International Audio Laboratories Erlangen Friedrich-Alexander Universität Erlangen-Nürnberg Erlangen Germany HAN University of Applied Sciences Department of Arts Therapies and Psychological Studies Nijmegen Netherlands University of Amsterdam Research Institute of Child Development and Education Amsterdam Netherlands Treatment Centre for People with Mild Intellectual Disabilities and Psychiatric and Behavioral Disorders Gennep Netherlands Information Systems Technology and Design Singapore University of Technology and Design Singapore Music and Machine Learning Lab Music Technology Group Universitat Pompeu Fabra Barcelona Spain Faculty of EEMCS Interactive Intelligence Group Delft University of Technology Delft Netherlands Centre for Digital Music School of Electronic Engineerin

The fields of music, health, and technology have seen significant interactions in recent years in developing music technology for health care and well-being. In an effort to strengthen the collaboration between the involved disciplines, the workshop “Music, Computing, and Health” was held to discuss best practices and state-of-the-art at the intersection of these areas with researchers from music psychology and neuroscience, music therapy, music information retrieval, music technology, medical technology (medtech), and robotics. Following the discussions at the workshop, this article provides an overview of the different methods of the involved disciplines and their potential contributions to developing music technology for health and well-being. Furthermore, the article summarizes the state of the art in music technology that can be applied in various health scenarios and provides a perspective on challenges and opportunities for developing music technology that (1) supports person-centered care and evidence-based treatments, and (2) contributes to developing standardized, large-scale research on music-based interventions in an interdisciplinary manner. The article provides a resource for those seeking to engage in interdisciplinary research using music-based computational methods to develop technology for health care, and aims to inspire future research directions by evaluating the state of the art with respect to the challenges facing each field. © The Author(s) 2021.

关键词： health care interdisciplinarity MedTech music information retrieval (MIR) music neuroscience Music psychology music technology music therapy well-being

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Author Correction: Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition

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Nature genetics 2023年第6期55卷 1080页

作者： Bernardo Rodriguez-Martin Eva G Alvarez Adrian Baez-Ortega Jorge Zamora Fran Supek Jonas Demeulemeester Martin Santamarina Young Seok Ju Javier Temes Daniel Garcia-Souto Harald Detering Yilong Li Jorge Rodriguez-Castro Ana Dueso-Barroso Alicia L Bruzos Stefan C Dentro Miguel G Blanco Gianmarco Contino Daniel Ardeljan Marta Tojo Nicola D Roberts Sonia Zumalave Paul A Edwards Joachim Weischenfeldt Montserrat Puiggròs Zechen Chong Ken Chen Eunjung Alice Lee Jeremiah A Wala Keiran M Raine Adam Butler Sebastian M Waszak Fabio C P Navarro Steven E Schumacher Jean Monlong Francesco Maura Niccolo Bolli Guillaume Bourque Mark Gerstein Peter J Park David C Wedge Rameen Beroukhim David Torrents Jan O Korbel Iñigo Martincorena Rebecca C Fitzgerald Peter Van Loo Haig H Kazazian Kathleen H Burns Peter J Campbell Jose M C Tubio Department of Zoology Genetics and Physical Anthropology Universidade de Santiago de Compostela Santiago de Compostela Spain. Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain. Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain. Transmissible Cancer Group Department of Veterinary Medicine University of Cambridge Cambridge UK. The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain. Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK. Genome Data Science Institute for Research in Biomedicine (IRB Barcelona) The Barcelona Institute of Science and Technology (BIST) Barcelona Spain. Institucio Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain. The Francis Crick Institute London UK. Department of Human Genetics University of Leuven Leuven Belgium. Genomes and Disease Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain. Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea. Cancer Ageing and Somatic Mutation Programme Wellcome Sanger Institute Cambridge UK. Department of Biochemistry Genetics and Immunology University of Vigo Vigo Spain. Galicia Sur Health Research Institute Vigo Spain. Faculty of Science and Technology University of Vic-Central University of Catalonia (UVic-UCC) Vic Spain. Barcelona Supercomputing Center (BSC) Barcelona Spain. Experimental Cancer Genetics Wellcome Sanger Institute Cambridge UK. Oxford Big Data Institute University of Oxford Oxford UK. DNA Repair and Genome Integrity Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain. Department of Biochemistry and Molecular Biology Universidade de Santiago de Compostela Santiago de Compostela Spain. Medical R

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A decision support system based on rapid progression rules to enhance baseline evaluation of Parkinson's disease patients

A decision support system based on rapid progression rules t...

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2018 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2018

作者： Tsiouris, Kostas M. Gatsios, Dimitrios Rigas, Georgios Konitsiotis, Spiros Antonini, Angelo Fotiadis, Dimitrios I. Biomedical Engineering Laboratory School of Electrical and Computer Engineering National Technical University of Athens AthensGR15773 Greece Department of Neurology Medical School University of Ioannina IoanninaGR45110 Greece Department for Parkinson's Disease IRCCS San Camillo Via Alberoni 70 VeniceIT30126 Italy Unit of Medical Technology and Intelligent Information Systems Department of Material Science and Engineering University of Ioannina IoanninaGR45110 Greece Department of Biomedical Research Institute of Molecular Biology and Biotechnology FORTH University of Ioannina IoanninaGR45110 Greece

ISBN: (纸本)9781538624050

Parkinson's Disease (PD) can express different rates of symptoms progression which in some patients has been found to be extremely aggressive. In this work, a set of rules are extracted using the Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm to enable the prediction of such rapid disease progression cases from the baseline evaluation. After selecting the most effective of the initial 139 baseline features using the Wrapper algorithm, the best RIPPER rules are isolated based on the reported accuracy in assigning PD patients into rapid or normal progression groups. In total, about 120 unique rules that signal faster worsening of PD symptoms at baseline evaluation were extracted, assigning patients into the rapid progression group with a mean classification accuracy of 86.3%. The most informative of these rules are presented in this work, while all of them are used to develop a decision support system (DSS) to facilitate rapid progression risk assessment at the baseline evaluation. The rules were extracted using baseline evaluation data from the Parkinson's Progression Markers Initiative (PPMI) dataset. The DSS for prediction of rapid progression is included in the PD-Manager mHealth platform to offer better management of PD patients when rapid progression is expected. © 2018 IEEE.

关键词： Decision support systems

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Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals (vol 52, pg 1314, 2020)

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NATURE GENETICS 2021年第5期53卷 762-762页

作者： Surendran, Praveen Feofanova, Elena V. Lahrouchi, Najim Ntalla, Ioanna Karthikeyan, Savita Cook, James Chen, Lingyan Mifsud, Borbala Yao, Chen Kraja, Aldi T. Cartwright, James H. Hellwege, Jacklyn N. Giri, Ayush Tragante, Vinicius Thorleifsson, Gudmar Liu, Dajiang J. Prins, Bram P. Stewart, Isobel D. Cabrera, Claudia P. Eales, James M. Akbarov, Artur Auer, Paul L. Bielak, Lawrence F. Bis, Joshua C. Braithwaite, Vickie S. Brody, Jennifer A. Daw, E. Warwick Warren, Helen R. Drenos, Fotios Nielsen, Sune Fallgaard Faul, Jessica D. Fauman, Eric B. Fava, Cristiano Ferreira, Teresa Foley, Christopher N. Franceschini, Nora Gao, He Giannakopoulou, Olga Giulianini, Franco Gudbjartsson, Daniel F. Guo, Xiuqing Harris, Sarah E. Havulinna, Aki S. Helgadottir, Anna Huffman, Jennifer E. Hwang, Shih-Jen Kanoni, Stavroula Kontto, Jukka Larson, Martin G. Li-Gao, Ruifang Lindstrom, Jaana Lotta, Luca A. Lu, Yingchang Luan, Jian'an Mahajan, Anubha Malerba, Giovanni Masca, Nicholas G. D. Mei, Hao Menni, Cristina Mook-Kanamori, Dennis O. Mosen-Ansorena, David Muller-Nurasyid, Martina Pare, Guillaume Paul, Dirk S. Perola, Markus Poveda, Alaitz Rauramaa, Rainer Richard, Melissa Richardson, Tom G. Sepulveda, Nuno Sim, Xueling Smith, Albert V. Smith, Jennifer A. Staley, James R. Stanakova, Alena Sulem, Patrick Theriault, Sebastien Thorsteinsdottir, Unnur Trompet, Stella Varga, Tibor V. Velez Edwards, Digna R. Veronesi, Giovanni Weiss, Stefan Willems, Sara M. Yao, Jie Young, Robin Yu, Bing Zhang, Weihua Zhao, Jing-Hua Zhao, Wei Zhao, Wei Evangelou, Evangelos Aeschbacher, Stefanie Asllanaj, Eralda Blankenberg, Stefan Bonnycastle, Lori L. Bork-Jensen, Jette Brandslund, Ivan Braund, Peter S. Burgess, Stephen Cho, Kelly Christensen, Cramer Connell, John de Mutsert, Renee Dominiczak, Anna F. Dorr, Marcus Eiriksdottir, Gudny Farmaki, Aliki-Eleni Gaziano, J. Michael Grarup, Niels Grove, Megan L. Hallmans, Goran Hansen, Torben Have, Christian T. Heiss, Gerardo Jorgensen, Marit E. Jousilahti, Pekka Kajantie, Eero Kamat, Mihir Karajamaki, AnneMari Kar British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK British Heart Foundation Centre of Research Excellence University of Cambridge Cambridge UK Health Data Research UK Cambridge Wellcome Genome Campus and University of Cambridge Cambridge UK Rutherford Fund Fellow Department of Public Health and Primary Care University of Cambridge Cambridge UK MRC Biostatistics Unit University of Cambridge Cambridge UK National Institute for Health Research Cambridge Biomedical Research Centre University of Cambridge and Cambridge University Hospitals Cambridge UK MRC Integrative Epidemiology Unit (IEU) Population Health Sciences Bristol Medical School University of Bristol Bristol UK Robertson Centre for Biostatistics University of Glasgow Glasgow UK MRC Epidemiology Unit University of Cambridge School of Clinical Medicine Cambridge UK Department of Immunology and Inflammation Imperial College London London UK Centre for Non-communicable Disease Research (CNCR) Dhaka Bangladesh National Institute for Health Research (NIHR) Blood and Transplant Research Unit (BTRU) in Donor Health and Genomics at the University of Cambridge Cambridge UK Department of Human Genetics Wellcome Sanger Institute Wellcome Genome Campus Hinxton Cambridge UK Department of Biostatistics and Epidemiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Center for Non-Communicable Diseases Karachi Pakistan Department of Genetics Novo Nordisk Research Centre Oxford Oxford UK Human Genetics Center Department of Epidemiology Human Genetics and Environmental Sciences School of Public Health University of Texas Health Science Center at Houston Houston TX USA Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Cardiovascular Research Center Center for Genomic Medicine

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Publisher Correction: The genome of the stable fly, Stomoxys calcitrans, reveals potential mechanisms underlying reproduction, host interactions, and novel targets for pest control

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BMC biology 2021年第1期19卷 150页

作者： Pia U Olafson Serap Aksoy Geoffrey M Attardo Greta Buckmeier Xiaoting Chen Craig J Coates Megan Davis Justin Dykema Scott J Emrich Markus Friedrich Christopher J Holmes Panagiotis Ioannidis Evan N Jansen Emily C Jennings Daniel Lawson Ellen O Martinson Gareth L Maslen Richard P Meisel Terence D Murphy Dana Nayduch David R Nelson Kennan J Oyen Tyler J Raszick José M C Ribeiro Hugh M Robertson Andrew J Rosendale Timothy B Sackton Perot Saelao Sonja L Swiger Sing-Hoi Sze Aaron M Tarone David B Taylor Wesley C Warren Robert M Waterhouse Matthew T Weirauch John H Werren Richard K Wilson Evgeny M Zdobnov Joshua B Benoit Livestock Arthropod Pests Research Unit USDA-ARS Kerrville TX USA. pia.olafson@usda.gov. Department of Epidemiology of Microbial Diseases Yale School of Public Health New Haven CT USA. Department of Entomology and Nematology University of California - Davis Davis CA USA. Livestock Arthropod Pests Research Unit USDA-ARS Kerrville TX USA. The Center for Autoimmune Genomics and Etiology Cincinnati Children's Hospital Medical Center Cincinnati OH USA. Department of Entomology Texas A & M University College Station TX USA. Department of Biological Sciences Wayne State University Detroit MI USA. Department of Electrical Engineering & Computer Science University of Tennessee Knoxville TN USA. Department of Biological Sciences University of Cincinnati Cincinnati OH USA. Department of Genetic Medicine and Development University of Geneva Medical School and Swiss Institute of Bioinformatics 1211 Geneva Switzerland. The European Molecular Biology Laboratory The European Bioinformatics Institute The Wellcome Genome Campus Hinxton CB10 1SD UK. Department of Entomology University of Georgia Athens GA USA. Department of Biology and Biochemistry University of Houston Houston TX USA. National Center for Biotechnology Information National Library of Medicine National Institutes of Health Bethesda MD USA. Arthropod-borne Animal Diseases Research Unit USDA-ARS Manhattan KS USA. Department of Microbiology Immunology and Biochemistry University of Tennessee Health Science Center Memphis TN USA. Section of Vector Biology Laboratory of Malaria and Vector Research National Institute of Allergy and Infectious Diseases Rockville MD USA. Department of Entomology University of Illinois at Urbana-Champaign Urbana IL USA. Department of Biology Mount St. Joseph University Cincinnati OH USA. Informatics Group Faculty of Arts and Sciences Harvard University Cambridge MA USA. Department of Entomology Texas A&M AgriLife Research and Extension Center Stephenville

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Roadmap on multimode light shaping

arXiv

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arXiv 2021年

作者： Piccardo, Marco Ginis, Vincent Forbes, Andrew Mahler, Simon Friesem, Asher A. Davidson, Nir Ren, Haoran Dorrah, Ahmed H. Capasso, Federico Dullo, Firehun T. Ahluwalia, Balpreet S. Ambrosio, Antonio Gigan, Sylvain Treps, Nicolas Hiekkamäki, Markus Fickler, Robert Kues, Michael Moss, David Morandotti, Roberto Riemensberger, Johann Kippenberg, Tobias J. Faist, Jérôme Scalari, Giacomo Picqué, Nathalie Hänsch, Theodor W. Cerullo, Giulio Manzoni, Cristian Lugiato, Luigi A. Brambilla, Massimo Columbo, Lorenzo Gatti, Alessandra Prati, Franco Shiri, Abbas Abouraddy, Ayman F. Alù, Andrea Galiffi, Emanuele Pendry, J.B. Huidobro, Paloma A. Center for Nano Science and Technology Fondazione Istituto Italiano di Tecnologia Milano Italy Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Data Lab/Applied Physics Vrije Universiteit Brussel Belgium School of Physics University of the Witwatersrand Johannesburg South Africa Department of Physics of Complex Systems Weizmann Institute of Science Rehovot Israel MQ Photonics Research Centre Department of Physics and Astronomy Macquarie University Macquarie ParkNSW Australia Department of Physics and Technology UiT-The Arctic University of Norway Tromsø Norway Laboratoire Kastler Brossel ENS-PSL Research University CNRS Sorbonne Université Collège de France Paris France Tampere University Photonics Laboratory Physics Unit Tampere Finland Institute of Photonics Leibniz University Hannover Germany Optical Sciences Centre Swinburne University of Technology Australia Energie Materiaux Telecommunications Institut National de la Recherche Scientifique Canada Lausanne Switzerland Institute for Quantum Electronics ETH Zurich Zürich Switzerland Max-Planck Institute of Quantum Optics Garching Germany Istituto di Fotonica e Nanotecnologia-CNR Dipartimento di Fisica Politecnico di Milano Milano Italy Dipartimento di Scienza e Alta Tecnologia Università dell'Insubria Como Italy Dipartimento di Fisica Interateneo and CNR-IFN Università e Politecnico di Bari Bari Italy Dipartimento di Elettronica e Telecomunicazioni Politecnico di Torino Torino Italy Istituto di Fotonica e Nanotecnologie IFN-CNR Milano Italy CREOL The College of Optics & Photonics University of Central Florida OrlandoFL United States Department of Electrical and Computer Engineering University of Central Florida OrlandoFL United States Advanced Science Research Center City University of New York United States The Blackett Laboratory Imperial College London London United Kingdom Instituto de Telecomunicações IST-University o

Our ability to generate new distributions of light has been remarkably enhanced in recent years. At the most fundamental level, these light patterns are obtained by ingeniously combining different electromagnetic modes. Interestingly, the modal superposition occurs in the spatial, temporal as well as spatio-temporal domain. This generalized concept of structured light is being applied across the entire spectrum of optics: generating classical and quantum states of light, harnessing linear and nonlinear light-matter interactions, and advancing applications in microscopy, spectroscopy, holography, communication, and synchronization. This Roadmap highlights the common roots of these different techniques and thus establishes links between research areas that complement each other seamlessly. We provide an overview of all these areas, their backgrounds, current research, and future developments. We highlight the power of multimodal light manipulation and want to inspire new eclectic approaches in this vibrant research community. Copyright © 2021, The Authors. All rights reserved.

关键词： Light

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Roadmap on spin-wave computing

arXiv

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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

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