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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

Mathematical Modelling of Electrically Controlled Filters of Microwave, subTHz and THz–bands on the Base of Grapheneand-Dielectric Multilayer Structure

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Journal of Physics: Conference Series 2020年第1期1443卷

作者： I A Smirnov L V Cherckesova E A Zhebrun D V Kleimenkin Department of Cybersecurity of Information Systems Don State Technical University Gagarin Sq. 1 Rostov–on–Don 344000 Russia Department of Mathematics and Computer Sciences Don State Technical University Gagarin Sq. 1 Rostov–on–Don 344000 Russia Joint Stock Company 'Research Institute of Microelectronic Equipment' 'Progress' Cherepanov passage 54 Moscow 125183 Russia Information System and Radio engineering Department Don State Technical University Gagarin Sq. 1 Rostov–on–Don 344000 Russia

Report is devoted to investigations of graphene meta–surfaces for the transmission of radiation induced by plasmons in subTHz and THz ranges, cell of which consists of structures based on graphene ring and graphene nano–tape. It create regimes of radiation transmission – transparency windows induced by electric dipole resonances. Resonant frequency of transparency window can be dynamically tuned in wide band of subTHz and THz bands by changing the chemical potential (Fermi energy) of graphene by applying external electric field (gating) instead of re–fabricating of structures. Questions of possibilities of electronic controlled filters creating of subTHz and THz bands grounded on different configurations of graphene meta–surfaces are discussed; their characteristics and frequency dependencies are investigated. Mathematical modelling and electrodynamic calculation of the filters characteristics of subTHz and THz bands grounded on multilayer structures of 'graphene–dielectric' type are carried out. From results of mathematical modelling it follows that periodic layered microstructures 'graphene–dielectric' type can be used for creation of subTHz and THz bands broadband filters of planar construction, controlling by electric field and fast tuning at small changes in Fermi energy level of graphene.

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Erratum to: Search for heavy Higgs bosons decaying to a top quark pair in proton-proton collisions at s = 13 TeV

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Journal of High Energy Physics 2022年第3期2022卷 1-21页

作者： Sirunyan, A. M. Tumasyan, A. Adam, W. Ambrogi, F. Bergauer, T. Brandstetter, J. Dragicevic, M. Erö, J. Escalante Del Valle, A. Flechl, M. Frühwirth, R. Jeitler, M. Krammer, N. Krätschmer, I. Liko, D. Madlener, T. Mikulec, I. Rad, N. Schieck, J. Schöfbeck, R. Spanring, M. Spitzbart, D. Waltenberger, W. Wulz, C.-E. Zarucki, M. Drugakov, V. Mossolov, V. Suarez Gonzalez, J. Darwish, M. R. De Wolf, E. A. Di Croce, D. Janssen, X. Lauwers, J. Lelek, A. Pieters, M. Rejeb Sfar, H. Van Haevermaet, H. Van Mechelen, P. Van Putte, S. Van Remortel, N. Blekman, F. Bols, E. S. Chhibra, S. S. D’Hondt, J. De Clercq, J. Lontkovskyi, D. Lowette, S. Marchesini, I. Moortgat, S. Moreels, L. Python, Q. Skovpen, K. Tavernier, S. Van Doninck, W. Van Mulders, P. Van Parijs, I. Beghin, D. Bilin, B. Brun, H. Clerbaux, B. De Lentdecker, G. Delannoy, H. Dorney, B. Favart, L. Grebenyuk, A. Kalsi, A. K. Luetic, J. Popov, A. Postiau, N. Starling, E. Thomas, L. Vander Velde, C. Vanlaer, P. Vannerom, D. Cornelis, T. Dobur, D. Khvastunov, I. Niedziela, M. Roskas, C. Trocino, D. Tytgat, M. Verbeke, W. Vermassen, B. Vit, M. Zaganidis, N. Bondu, O. Bruno, G. Caputo, C. David, P. Delaere, C. Delcourt, M. Giammanco, A. Lemaitre, V. Magitteri, A. Prisciandaro, J. Saggio, A. Vidal Marono, M. Vischia, P. Zobec, J. Alves, F. L. Alves, G. A. Correia Silva, G. Hensel, C. Moraes, A. Rebello Teles, P. Belchior Batista Das Chagas, E. Carvalho, W. Chinellato, J. Coelho, E. Da Costa, E. M. Da Silveira, G. G. De Jesus Damiao, D. De Oliveira Martins, C. Fonseca De Souza, S. Huertas Guativa, L. M. Malbouisson, H. Martins, J. Matos Figueiredo, D. Medina Jaime, M. Melo De Almeida, M. Mora Herrera, C. Mundim, L. Nogima, H. Prado Da Silva, W. L. Sanchez Rosas, L. J. Santoro, A. Sznajder, A. Thiel, M. Tonelli Manganote, E. J. Torres Da Silva De Araujo, F. Vilela Pereira, A. Ahuja, S. Bernardes, C. A. Calligaris, L. Fernandez Perez Tomei, T. R. Gregores, E. M. Lemos, D. S. Mercadante, P. G. Novaes, S. F. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, Yerevan Physics Institute Yerevan Armenia Institut für Hochenergiephysik Wien Austria Vienna University of Technology Vienna Austria Institute for Nuclear Problems Minsk Belarus Universiteit Antwerpen Antwerpen Belgium Vrije Universiteit Brussel Brussel Belgium Université Libre de Bruxelles Bruxelles Belgium Ghent University Ghent Belgium IRFU CEA Université Paris-Saclay Gif-sur-Yvette France Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual de Campinas Campinas Brazil Federal University of Rio Grande do Sul Porto Alegre Brazil UFMS Nova Andradina Brazil Universidade Federal de Pelotas Pelotas Brazil Universidade Estadual Paulista São Paulo Brazil Universidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Beihang University Beijing China Department of Physics Tsinghua University Beijing China Institute of High Energy Physics Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical Engineering and Naval Architecture Split Croatia University of Split Faculty of Science Split Croatia Institute Rudjer Boskovic Zagreb Croatia Institute for Theoretical and Experimental Physics named by A.I. Alikhanov of NRC ‘Kurchatov Institute’ Moscow Russia University of Cyprus Nicosia Cyprus Charles University Prague Czech Republic Joint Institute for Nuclear Research Dubna Russia Escuela Politecnica Nacional Quito Ecuador Universidad San Francisco de Quito Quito Ecuador Cairo University Cairo Egypt Academy of Scientific Research and Technolog

A Pattern Division Multiple Access Scheme with Low Complexity Iterative Receiver for 5G Wireless Communication systems

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A Pattern Division Multiple Access Scheme with Low Complexit...

IEEE Vehicular Technology Conference

作者： Kai Zhang Jin Xu Changchuan Yin Chunyu Pan Weiqiang Tan Guixian Xu Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing 100876 P. R. China Zhengzhou University of Light Industry School of Computer and Communication Engineering Zhengzhou 450001 P. R. China Guangzhou University School of Computer Science and Educational Software Guangzhou 510006 P. R. China Aalborg University Department of Electronic Systems Aalborg 9400 Denmark

ISBN: (纸本)9781538663592;9781538663585

Non-orthogonal multiple access schemes with higher spectral efficiency and the capability of accessing more users than orthogonal multiple access (OMA) scheme is preferred for the fifth generation (5G) wireless networks. In this work, a non-orthogonal transmission scheme called pattern division multiple access (PDMA) is proposed for the 5G wireless communication systems. Two pattern matrices are proposed for 150% and 200% overloaded PDMA systems. Then, a low complexity symbol-wise belief propagation iterative detection and decoding (BPIDD) algorithm for multiple user detection is proposed. Numerical results and analysis provided by utilizing extrinsic information transfer chart show that the proposed scheme outperforms the traditional OMA systems significantly, and the performance of the proposed scheme is better than that of the existing schemes up to 1 dB. Finally, simulation results show that the proposed scheme is superior to the existing schemes and the proposed BPIDD algorithm is better than that of the BP algorithm over 0.5 dB.

关键词： 5G mobile communication iterative decoding matrix algebra multi-access systems multiuser detection Multi access systems matrix algebra Multiuser detection Iterative decoding multiple access Wireless communication systems Scheme iterative receiver spectral efficiency Information Transfer

Model-driven deep learning for physical layer communications

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arXiv 2018年

作者： He, Hengtao Jin, Shi Wen, Chao-Kai Gao, Feifei Li, Geoffrey Ye Xu, Zongben National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Institute of Communications Engineering National Sun Yat-sen University Kaohsiung804 Taiwan State Key Lab of Intelligent Technologies and Systems Tsinghua University Department of Automation Tsinghua University Beijing China School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Institute for Information and System Sciences Xian Jiaotong University Xi'an710049 China

Intelligent communication is gradually becoming a mainstream direction. As a major branch of machine learning, deep learning (DL) has been applied in physical layer communications and demonstrated an impressive performance improvement in recent years. However, most of the existing works related to DL focus on data-driven approaches, which consider the communication system as a black box and train it by using a huge volume of data. Training a network requires sufficient computing resources and extensive time, both of which are rarely found in communication devices. By contrast, modeldriven DL approaches combine communication domain knowledge with DL to reduce the demand for computing resources and training time. This article discusses the recent advancements in model-driven DL approaches in physical layer communications, including transmission schemes, receiver design, and channel information recovery. Several open issues for future research are also highlighted. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

Optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning

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arXiv 2018年

作者： Chen, Xianfu Zhang, Honggang Wu, Celimuge Mao, Shiwen Ji, Yusheng Bennis, Mehdi VTT Technical Research Centre of Finland Finland College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Graduate School of Informatics and Engineering University of Electro-Communications Tokyo Japan Department of Electrical and Computer Engineering Auburn University AuburnAL United States Information Systems Architecture Research Division National Institute of Informatics Tokyo Japan Centre for Wireless Communications University of Oulu Finland

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services. Nevertheless, the design of computation offloading policies for a virtual MEC system remains challenging. Specifically, whether to execute a computation task at the mobile device or to offload it for MEC server execution should adapt to the time-varying network dynamics. In this paper, we consider MEC for a representative mobile user in an ultra-dense sliced RAN, where multiple base stations (BSs) are available to be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to maximize the long-term utility performance whereby an offloading decision is made based on the task queue state, the energy queue state as well as the channel qualities between MU and BSs. To break the curse of high dimensionality in state space, we first propose a double deep Q-network (DQN) based strategic computation offloading algorithm to learn the optimal policy without knowing a priori knowledge of network dynamics. Then motivated by the additive structure of the utility function, a Q-function decomposition technique is combined with the double DQN, which leads to novel learning algorithm for the solving of stochastic computation offloading. Numerical experiments show that our proposed learning algorithms achieve a significant improvement in computation offloading performance compared with the baseline policies. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

Performance optimization in mobile-edge computing via deep reinforcement learning

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arXiv 2018年

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is emerging as a promising paradigm by providing computing capabilities within radio access networks in close proximity. Nevertheless, the design of computation offloading policies for a MEC system remains challenging. Specifically, whether to execute an arriving computation task at local mobile device or to offload a task for cloud execution should adapt to the environmental dynamics in a smarter manner. In this paper, we consider MEC for a representative mobile user in an ultra dense network, where one of multiple base stations (BSs) can be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to minimize the long-term cost and an offloading decision is made based on the channel qualities between the mobile user and the BSs, the energy queue state as well as the task queue state. To break the curse of high dimensionality in state space, we propose a deep Q-network-based strategic computation offloading algorithm to learn the optimal policy without having a priori knowledge of the dynamic statistics. Numerical experiments provided in this paper show that our proposed algorithm achieves a significant improvement in average cost compared with baseline policies. Copyright © 2018, The Authors. All rights reserved.

关键词： Reinforcement learning

2025 photonics for agrifood roadmap: towards a sustainable and healthier planet

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Journal of Physics: Photonics 2025年第3期7卷 032501-032501页

作者： Lien Smeesters Francesca Venturini Stefan Paulus Anne-Katrin Mahlein David Perpetuini Daniela Cardone Arcangelo Merla Benjamin Gac Stephane Perrin Denis Trégoat Luiz Poffo Antoine Fournier Giuseppe Bonifazi Silvia Serranti Wenzhi Liao Noureddin Osseiran Mathias Vanwolleghem Romain Peretti Aparajita Bandyopadhyay Karl Bertling Krishnan Parameswaran Aleksandar D Rákic Amartya Sengupta Sune Svanberg Małgorzata Kujawinska Daniel Cozzolino Dolores Pérez Marín José A Entrenas de León Irina Torres Rodríguez Marietta Fodor Eszter Benes Anna Matkovits Zsuzsa Jókai Simona M Cristescu Amir Khodabakhsh Khalil Jahromi Mohammadreza Nematollahi Frans H Harren Eugene Rokx Ewa Sikorska Kay Sowoidnich Martin Maiwald Bernd Sumpf Matthieu Roussey Kai-Erik Peiponen Antoni Femenias Polina Fomina Vjekoslav Kokoric Robert Stach Boris Mizaikoff Maria Konstantaki Stavros Pissadakis Umberto Michelucci Sławomir Paśko Anna Pakuła Richard A Crocombe Ellen V Miseo Andreas C Liapis Faisal Ahmed Zhipei Sun Chrysoula Chandrinou Marianneza Chatzipetrou Ioanna Zergioti Ulrich Trog Anja Haase Martin Smolka Lex Oosterveld Chris Van Hoof André Müller Michael Hlavatsch Diellza Bajrami Ernesto Olvera-Gonzalez Nivia Escalante-Garcia Arturo B Soro Koenraad Van Hoorde Brijesh K Tiwari Thomas Westerhoff Michael Kneissl Anna Grazia Mignani Department of Applied Physics and Photonics Vrije Universiteit Brussel and Flanders Make Brussels Photonics (B-PHOT) Pleinlaan 2 B-1050 Brussels Belgium School of Engineering ZHAW Zurich University of Applied Sciences 8401 Winterthur Switzerland TOELT LLC Research and Development 8600 Duebendorf Switzerland Institute of Sugar Beet Research Holtenser Landstr. 77 37079 Göttingen Germany Department of Engineering and Geology University ‘D’Annunzio’ of Chieti-Pescara Pescara 65127 Italy Arvalis 45 Voie Romaine 41240 Beauce la Romaine France Université de Rennes CNRS Institut FOTON—UMR 6082 F-22305 Lannion France Photonics Bretagne 4 Rue Louis de Broglie 22300 Lannion France University of Limoges XLIM UMR CNRS 7252 Limoges France Department of Chemical Engineering Materials and Environment Sapienza—University of Rome via Eudossiana 18 00184 Rome Italy Flanders Make 8500 Kortrijk Belgium Department of Telecommunications and Information Processing Ghent University 9000 Ghent Belgium Institut d’Electronique de Microélectronique et de Nanotechnologie (IEMN) CNRS–UMR 8520 Université de Lille 59652 Villeneuve d’Ascq France DRDO-Industry-Academia Centre of Excellence (DIA-CoE) Indian Institute of Technology Delhi New Delhi 110016 India School of Electrical Engineering and Computer Science University of Queensland Brisbane QLD 4072 Australia Cambridge Consultants Boston MA United States of America Department of Physics Indian Institute of Technology Delhi New Delhi 110016 India Physics Department Lund University SE-221 00 Lund Sweden Institute of Micromechanics and Photonics Warsaw University of Technology 8 Sw. Andrzeja Boboli St 02-525 Warsaw Poland Central Office of Measures 2 Elektoralna St. 00-139 Warsaw Poland Centre for Nutrition and Food Sciences Queensland Alliance for Agriculture and Food Innovation (QAAFI) The University of Queensland St.Lucia Brisbane QLD 4072 Australia Non-Destructive Spectral Sensors Unit Department of Animal Production Fa

Photonics technologies play a crucial role in driving technological advancements within the agrifood industry, aiming to deliver a sustainable food and agriculture, and offering healthy, nutritious and safe food for all of us. Particularly, optical sensors and imaging systems, together with machine-learning processing and advanced lighting, play a pivotal role in monitoring crop and soil health with unprecedented precision, while safeguarding the food supply chain. This roadmap aims to provide an overview of the state-of-the-art photonics technologies benefitting agrifood applications, including a view on their current limitations, challenges and future potential, while addressing practical case studies. Future trends towards multimodal sensors and sensor fusion, artificial intelligence and digital twins, miniaturization and controlled farming are highlighted. The revolutionizing capabilities of the photonics technologies are indicated, inspiring future applications and developments, and paving the way towards optimized resource utilization, increased crop yields, stopping land degradation and reduction of food waste.

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Multi-tenant cross-slice resource orchestration: A deep reinforcement learning approach

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arXiv 2018年

作者： Chen, Xianfu Zhao, Zhifeng Wu, Celimuge Bennis, Mehdi Liu, Hang Ji, Yusheng Zhang, Honggang VTT Technical Research Centre of Finland Oulu Finland Zhejiang Lab College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Graduate School of Informatics and Engineering University of Electro-Communications Tokyo Japan Centre for Wireless Communications University of Oulu Finland Department of Electrical Engineering and Computer Science Catholic University of America United States Information Systems Architecture Research Division National Institute of Informatics Tokyo Japan College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

With the cellular networks becoming increasingly agile, a major challenge lies in how to support diverse services for mobile users (MUs) over a common physical network infrastructure. Network slicing is a promising solution to tailor the network to match such service requests. This paper considers a system with radio access network (RAN)-only slicing, where the physical infrastructure is split into slices providing computation and communication functionalities. A limited number of channels are auctioned across scheduling slots to MUs of multiple service providers (SPs) (i.e., the tenants). Each SP behaves selfishly to maximize the expected long-term payoff from the competition with other SPs for the orchestration of channels, which provides its MUs with the opportunities to access the computation and communication slices. This problem is modelled as a stochastic game, in which the decision makings of a SP depend on the global network dynamics as well as the joint control policy of all SPs. To approximate the Nash equilibrium solutions, we first construct an abstract stochastic game with the local conjectures of channel auction among the SPs. We then linearly decompose the per-SP Markov decision process to simplify the decision makings at a SP and derive an online scheme based on deep reinforcement learning to approach the optimal abstract control policies. Numerical experiments show significant performance gains from our scheme. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning