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EAI/Springer Innovations in Communication and Computing 2022年 vii-viii页

作者： Kathiresh, M. Kanagachidambaresan, G.R. Williamson, Sheldon S. Department of Electrical and Electronics Engineering PSG College of Technology Coimbatore India Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Electric Energy Storage Systems for Transportation Electrification Ontario Tech University OshawaON Canada

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Distributed Cloud Association and Beamforming in Downlink Multi-Cloud Radio Access Networks

Distributed Cloud Association and Beamforming in Downlink Mu...

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IEEE International Conference on Communications Workshops, ICC

作者： Alaa Alameer Ahmad Hayssam Dahrouj Anas Chaaban Aydin Sezgin Tareq Y. Al-Naffouri Mohamed-Slim Alouini Institute of Digital Communication Systems Ruhr-Universität Bochum Germany Department of Electrical and Computer Engineering Effat University Saudi Arabia School of Engineering The University of British Columbia Kelowna Canada King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

ISBN: (数字)9781728174402

ISBN: (纸本)9781728174419

Conventional cloud-radio access networks assume the existence of a single processor, responsible for managing a plurality of devices. To cope with the current drastic increase in the number of data-hungry systems, several clouds would be practically needed, and so the joint provisioning of inter-cloud and intra-cloud interference becomes a fundamental challenge. This paper considers a multi-cloud radio access network model (MC-RAN) where each cloud is connected to a distinct set of base stations (BSs) via limited capacity fronthaul links. The paper investigates the problem of jointly assigning users to clouds and determining their beamforming vectors so as to maximize the network-wide sum-rate utility. The paper solves such a difficult non-convex combinatorial problem using a heuristic algorithm which uses fractional programming techniques to deal with the non-convexity of the continuous part of the problem, and 10-norm approximation to account for the binary association part. A highlight of the proposed algorithm is its ability to be implemented in a distributed fashion across the multiple clouds. The simulations illustrate how close is the sum-rate performance of the proposed approach as compared to the sum-rate achieved by a centralized single processor, especially in dense networks.

关键词： Array signal processing Cloud computing Optimization Downlink Interference Approximation algorithms Programming

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Antifragility in complex dynamical systems

npj Complexity

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npj Complexity 2024年第1期1卷 1-8页

作者： Cristian Axenie Oliver López-Corona Michail A. Makridis Meisam Akbarzadeh Matteo Saveriano Alexandru Stancu Jeffrey West Department of Computer Science and Center for Artificial Intelligence Nuremberg Institute of Technology Georg Simon Ohm Nuremberg Germany Investigadores por México (IxM) at Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS) Universidad Nacional Autónoma de México (UNAM) Ciudad Universitaria CDMX Mexico Institute for Transport Planning and Systems (IVT) ETH Zurich Switzerland Department of Transportation Engineering Isfahan University of Technology Isfahan Iran Department of Industrial Engineering University of Trento Trento Italy Department of Electrical and Electronic Engineering The University of Manchester Manchester UK Department of Integrated Mathematical Oncology H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA

Antifragility characterizes the benefit of a dynamical system derived from the variability in environmental perturbations. Antifragility carries a precise definition that quantifies a system’s output response to input variability. systems may respond poorly to perturbations (fragile) or benefit from perturbations (antifragile). In this manuscript, we review a range of applications of antifragility theory in technical systems (e.g., traffic control, robotics) and natural systems (e.g., cancer therapy, antibiotics). While there is a broad overlap in methods used to quantify and apply antifragility across disciplines, there is a need for precisely defining the scales at which antifragility operates. Thus, we provide a brief general introduction to the properties of antifragility in applied systems and review relevant literature for both natural and technical systems’ antifragility. We frame this review within three scales common to technical systems: intrinsic (input–output nonlinearity), inherited (extrinsic environmental signals), and induced (feedback control), with associated counterparts in biological systems: ecological (homogeneous systems), evolutionary (heterogeneous systems), and interventional (control). We use the common noun in designing systems that exhibit antifragile behavior across scales and guide the reader along the spectrum of fragility–adaptiveness–resilience–robustness–antifragility, the principles behind it, and its practical implications.

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Fast mesh denoising with data driven normal filtering using deep autoencoders

Fast mesh denoising with data driven normal filtering using ...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Stavros Nousias Gerasimos Arvanitis Aris S. Lalos Konstantinos Moustakas Department of Electrical & Computer Engineering University of Patras Greece Industrial Systems Institute Athena Research Center

Through the years, several works have demonstrated high-quality 3D mesh denoising. Despite the high reconstruction quality, there are still challenges that need to be addressed ranging from variations in configuration parameters to high computational complexity. These drawbacks are crucial especially if the reconstructed models have to be used for quality check, inspection or repair in manufacturing environments where we have to deal with large objects resulting in very dense 3D meshes. Recently, deep learning techniques have shown that are able to automatically learn and find more accurate and reliable results, without the need for setting manually parameters. In this work, motivated by the aforementioned requirements, we propose a fast and reliable denoising method that can be effectively applied for reconstructing very dense noisy 3D models. The proposed method applies conditional variational autoencoders on face normals. Extensive evaluation studies carried out using a variety of 3D models verify that the proposed approach achieves plausible reconstruction outputs, very relative or even better of those proposed by the literature, in considerably faster execution times.

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Resonant beam communications with echo interference elimination

arXiv

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arXiv 2021年

作者： Xiong, Mingliang Liu, Qingwen Wang, Gang Giannakis, Georgios B. Zhang, Sihai Zhu, Jinkang Huang, Chuan College of Electronics and Information Engineering Tongji University Shanghai201804 China State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Key Lab of Wireless-Optical Communications University of Science and Technology of China Hefei Anhui230026 China School of Science and Engineering Chinese University of Hong Kong Shenzhen Guangdong518172 Hong Kong

Resonant beam communications (RBCom) is capable of providing wide bandwidth when using light as the carrier. Besides, the RBCom system possesses the characteristics of mobility, high signal-to-noise ratio (SNR), and multiplexing. Nevertheless, the channel of the RBCom system is distinct from other light communication technologies due to the echo interference issue. In this paper, we reveal the mechanism of the echo interference and propose the method to eliminate the interference. Moreover, we present an exemplary design based on frequency shifting and optical filtering, along with its mathematic model and performance analysis. The numerical evaluation shows that the channel capacity is greater than 15 bit/s/Hz. Copyright © 2021, The Authors. All rights reserved.

关键词： Internet of things

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Sparse modeling and optimization tools for energy efficient and reliable IoT

Sparse modeling and optimization tools for energy efficient ...

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International Conference on Societal Automation (SA)

作者： Stavros Nousias Aris S. Lalos Athanasios Kalogeras Christos Alexakos Christos Koulamas Konstantinos Moustakas Industrial Systems Institute Athena Research Center Department of Electrical & Computer Engineering University of Patras Greece

ISBN: (纸本)9781728133461

The continuous expansion of the Internet of Things, with even more devices expected to connect in the following years, leads to a great increase in the amount of data required to be transmitted and stored, identifying the need for robust, reliable and energy efficient data flow in the context of an ever-growing network. This study presents a matrix completion based approach for big data and large matrices facilitating local smoothness constraints combined with active subspace computing. We demonstrate that the presented approaches exhibit lower reconstruction error with respect to relevant literature for the specific problem. We also examine the effect of matrix size on the reconstruction accuracy so as to investigate the suitability of the proposed approaches for big data.

关键词： Big Data Laplace equations Sparse matrices Optimization Matrix decomposition Internet of Things Image reconstruction

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When counterpoint meets chinese folk melodies 20

When counterpoint meets chinese folk melodies

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Proceedings of the 34th International Conference on Neural Information Processing systems

作者： Nan Jiang Sheng Jin Zhiyao Duan Changshui Zhang Institute for Artificial Intelligence Tsinghua University (THUAI) and State Key Lab of Intelligent Technologies and Systems and Beijing National Research Center for Information Science and Technology (BNRist) and Department of Automation Tsinghua University Beijing China Department of Electrical and Computer Engineering University of Rochester

ISBN: (纸本)9781713829546

Counterpoint is an important concept in Western music theory. In the past century, there have been significant interests in incorporating counterpoint into Chinese folk music composition. In this paper, we propose a reinforcement learning-based system, named FolkDuet, towards the online countermelody generation for Chinese folk melodies. With no existing data of Chinese folk duets, FolkDuet employs two reward models based on out-of-domain data i.e., Bach chorales, and monophonic Chinese folk melodies. An interaction reward model is trained on the duets formed from outer parts of Bach chorales to model counterpoint interaction, while a style reward model is trained on monophonic melodies of Chinese folk songs to model melodic patterns. With both rewards, the generator of FolkDuet is trained to generate countermelodies while maintaining the Chinese folk style. The entire generation process is performed in an online fashion, allowing real-time interactive human-machine duet improvisation. Experiments show that the proposed algorithm achieves better subjective and objective results than the baselines.

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Linear system identification under multiplicative noise from multiple trajectory data

arXiv

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arXiv 2020年

作者： Xing, Yu Gravell, Ben He, Xingkang Johansson, Karl H. Summers, Tyler Key Lab of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Department of Mechanical Engineering University of Texas Dallas RichardsonTX United States Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

The study of multiplicative noise models has a long history in control theory but is re-emerging in the context of complex networked systems and systems with learning-based control. We consider linear system identification with multiplicative noise from multiple state-input trajectory data. We propose exploratory input signals along with a least-squares algorithm to simultaneously estimate nominal system parameters and multiplicative noise covariance matrices. The asymptotic consistency of the least-squares estimator is demonstrated by analyzing first and second moment dynamics of the system. The results are illustrated by numerical simulations. Copyright © 2020, The Authors. All rights reserved.

关键词： Linear systems

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Consensus Statement on Brillouin Light Scattering Microscopy of Biological Materials

arXiv

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arXiv 2024年

作者： Bouvet, Pierre Bevilacqua, Carlo Ambekar, Yogeshwari Antonacci, Giuseppe Au, Joshua Caponi, Silvia Chagnon-Lessard, Sophie Czarske, Juergen Dehoux, Thomas Fioretto, Daniele Fu, Yujian Guck, Jochen Hamann, Thorsten Heinemann, Dag Jähnke, Torsten Jean-Ruel, Hubert Kabakova, Irina Koski, Kristie Koukourakis, Nektarios Krause, David La Cavera, Salvatore Landes, Timm Li, Jinhao Margueritat, Jeremie Mattarelli, Maurizio Monaghan, Michael Overby, Darryl R. Perez-Cota, Fernando Pontecorvo, Emanuele Prevedel, Robert Ruocco, Giancarlo Sandercock, John Scarcelli, Giuliano Scarponi, Filippo Testi, Claudia Török, Peter Vovard, Lucie Weninger, Wolfgang Yakovlev, Vladislav Yun, Seok-Hyun Zhang, Jitao Palombo, Francesca Bilenca, Alberto Elsayad, Kareem Center for Anatomy and Cell Biology Medical University of Vienna Austria Cell Biology and Biophysics Unit European Molecular Biology Laboratory Germany Fischell Department of Bioengineering University of Maryland United States Specto Srl. Milan Italy Unità di Perugia Italy LightMachinery Inc. Canada Departement of Electronics Carleton University Canada TU Dresden Germany Cluster of Excellence Physics of Life TU Dresden Germany Competence Center for Biomedical Computational Laser Systems TU Dresden Germany Institut Lumière Matière UMR5306 Université Lyon 1 CNRS Université de Lyon France Dipartimento di Fisica e Geologia Università di Perugia Italy Max Planck Institute for the Science of Light Erlangen Germany Department of Biology Norwegian University of Science and Technology Trondheim Norway Hannover Centre for Optical Technologies Leibniz University Hannover Germany CellSense Technologies GmbH Berlin Germany School of Mathematical and Physical Sciences University of Technology Sydney Australia Department of Chemistry University of California Davis United States Optics & Photonics Group Faculty of Engineering University of Nottingham United Kingdom Discipline of Mechanical Manufacturing & Biomedical Engineering Trinity College Dublin Ireland Department of Bioengineering Imperial College London United Kingdom CREST Optics S.p.A. Rome Italy Heidelberg Germany Center for Life Nano- & Neuro-Science Istituto Italiano di Tecnologia Rome Italy The Table Stable Ltd. Mettmenstetten Switzerland School of Physical & Mathematical Sciences Nanyang Technological University Singapore Nanyang Technological University Singapore Institute for Digital Molecular Analytics & Sciences Nanyang Technological University Singapore Department of Biomedical Engineering Texas A&M University United States Department of Electrical and Computer Engineering Texas A&M University United States Department of Physics and Astronomy Texas A&M University United States Harvard Me

Brillouin Light Scattering (BLS) spectroscopy is a non-invasive, non-contact, label-free optical technique that can provide information on the mechanical properties of a material on the sub-micron scale. Over the last decade it has seen increased applications in the life sciences, driven by the observed significance of mechanical properties in biological processes, the realization of more sensitive BLS spectrometers and its extension to an imaging modality. As with other spectroscopic techniques, BLS measurements not only detect signals characteristic of the investigated sample, but also of the experimental apparatus, and can be significantly affected by measurement conditions. The aim of this consensus statement is to improve the comparability of BLS studies by providing reporting recommendations for the measured parameters and detailing common artifacts. Given that most BLS studies of biological matter are still at proof-of-concept stages and use different-often self-built-spectrometers, a consensus statement is particularly timely to assure unified advancement. © 2024, CC BY.

关键词： Brillouin scattering

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