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arXiv 2019年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Xu, Wei Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Technology and Business Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom NCRL Southeast University Nanjing China School of Computer Technology and Engineering Changchun Institute of Technology Changchun China School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom

In this paper, we consider a hybrid mobile edge computing (H-MEC) platform, which includes ground stations (GSs), ground vehicles (GVs) and unmanned aerial vehicle (UAVs), all with mobile edge cloud installed to enable user equipments (UEs) or Internet of thing (IoT) devices with intensive computing tasks to offload. Our objective is to obtain an online offloading algorithm to minimize the energy consumption of all the UEs, by jointly optimizing the positions of GVs and UAVs, user association and resource allocation in real-time, while considering the dynamic environment. To this end, we propose a hybrid deep learning based online offloading (H2O) framework where a large-scale path-loss fuzzy c-means (LSFCM) algorithm is first proposed and used to predict the optimal positions of GVs and UAVs. Secondly, a fuzzy membership matrix U-based particle swarm optimization (U-PSO) algorithm is applied to solve the mixed integer nonlinear programming (MINLP) problems and generate the sample datasets for the deep neural network (DNN) where the fuzzy membership matrix can capture the small-scale fading effects and the information of mutual interference. Thirdly, a DNN with the scheduling layer is introduced to provide user association and computing resource allocation under the practical latency requirement of the tasks and limited available computing resource of H-MEC. In addition, different from traditional DNN predictor, we only input one UEs information to the DNN at one time, which will be suitable for the scenarios where the number of UE is varying and avoid the curse of dimensionality in DNN. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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A Novel modeling approach for all-dielectric metasurfaces using deep neural networks

arXiv

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arXiv 2019年

作者： An, Sensong Fowler, Clayton Zheng, Bowen Shalaginov, Mikhail Y. Tang, Hong Li, Hang Zhou, Li Ding, Jun Agarwal, Anuradha Murthy Rivero-Baleine, Clara Richardson, Kathleen A. Gu, Tian Hu, Juejun Zhang, Hualiang Department of Electrical & Computer Engineering University of Massachusetts Lowell LowellMA United States Department of Materials Science & Engineering Massachusetts Institute of Technology CambridgeMA United States Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Lockheed Martin Corporation OrlandoFL United States CREOL University of Central Florida OrlandoFL United States

Metasurfaces have become a promising means for manipulating optical wavefronts in flat and high-performance optical devices. Conventional metasurface device design relies on trial-and-error methods to obtain target electromagnetic (EM) response, an approach that demands significant efforts to investigate the enormous number of possible meta-atom structures. In this paper, a deep neural network approach is introduced that significantly improves on both speed and accuracy compared to techniques currently used to assemble metasurface-based devices. Our neural network approach overcomes three key challenges that have limited previous neural-network-based design schemes: input/output vector dimensional mismatch, accurate EM-wave phase prediction, as well as adaptation to 3-D dielectric structures, and can be generically applied to a wide variety of metasurface device designs across the entire electromagnetic spectrum. Using this new methodology, examples of neural networks capable of producing on-demand designs for meta-atoms, metasurface filters, and phase-change reconfigurable metasurfaces are demonstrated. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Deep learning methods for lung cancer segmentation in whole-slide histopathology images - The ACDC@lungHP challenge 2019

arXiv

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arXiv 2020年

作者： Li, Zhang Zhang, Jiehua Tan, Tao Teng, Xichao Sun, Xiaoliang Li, Yang Liu, Lihong Xiao, Yang Lee, Byungjae Li, Yilong Zhang, Qianni Sun, Shujiao Zheng, Yushan Yan, Junyu Li, Ni Hong, Yiyu Ko, Junsu Jung, Hyun Liu, Yanling Chen, Yu-Cheng Wang, Ching-Wei Yurovskiy, Vladimir Maevskikh, Pavel Khanagha, Vahid Jiang, Yi Feng, Xiangjun Liu, Zhihong Li, Daiqiang Schüffler, Peter J. Yu, Qifeng Chen, Hui Tang, Yuling Litjens, Geert College of Aerospace Science and Engineering National University of Defense Technology Changsha410073 China Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands and ScreenPoint Medical Eindhoven Nijmegen5600 MB Netherlands Pingan Technology Shenzhen China Lunit Inc. Seoul Korea Republic of School of Electrical Engineering and Computer Science Queen Mary University of London London United Kingdom Image Processing Center School of Astronautics Beihang University Beijing102206 China Beijing Advanced Innovation Center for Biomedical Engineering Beijing100191 China Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Image Processing Center School of Astronautics Beihang University Beijing102206 China AstLab School of Automatationm Beihang University Beijing China Department of R&D Center Arontier Co. Ltd. Seoul Korea Republic of Frederick National Laboratory United States Center of Computer Vision and Medical Imaging Graduate Institute of Biomedical Engineering Graduate institute of Applied Science and Technology National Taiwan University of Science and Technology AI Explore Taipei Taiwan Research Department Sky-chain Global Yekaterinburg Russia Audio Solutions Team Motorola Solutions Inc. PlantationFL United States Second Xiangya Hospital Central South University Changsha China Hunan Biotechnology Ltd. Changsha China Hunan Cancer Hospital Central South University Changsha China Memorial Sloan Kettering Cancer Center United States First Hospital of Changsha City Changsha China Radboud University Medical Center Nijmegen Netherlands

Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology) challenge for evaluating different computer-aided diagnosis (CADs) methods on the automatic diagnosis of lung cancer. The ACDC@LungHP 2019 focused on segmentation (pixel-wise detection) of cancer tissue in whole slide imaging (WSI), using an annotated dataset of 150 training images and 50 test images from 200 patients. This paper reviews this challenge and summarizes the top 10 submitted methods for lung cancer segmentation. All methods were evaluated using the false positive rate, false negative rate, and DICE coefficient (DC). The DC ranged from 0.7354±0.1149 to 0.8372±0.0858. The DC of the best method was close to the inter-observer agreement (0.8398±0.0890). All methods were based on deep learning and categorized into two groups: multi-model method and single model method. In general, multi-model methods were significantly better (p Copyright © 2020, The Authors. All rights reserved.

关键词： Biological organs

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A freeform dielectric metasurface modeling approach based on deep neural networks

arXiv

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arXiv 2019年

作者： An, Sensong Zheng, Bowen Shalaginov, Mikhail Y. Tang, Hong Li, Hang Zhou, Li Ding, Jun Agarwal, Anuradha Murthy Rivero-Baleine, Clara Kang, Myungkoo Richardson, Kathleen A. Gu, Tian Hu, Juejun Fowler, Clayton Zhang, Hualiang Department of Electrical and Computer Engineering University of Massachusetts Lowell Lowell Massachusetts01854 United States Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge Massachusetts02139 United States Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200062 Lockheed Martin Corporation Orlando Florida32819 United States CREOL University of Central Florida Orlando Florida32816 United States

Metasurfaces have shown promising potentials in shaping optical wavefronts while remaining compact compared to bulky geometric optics devices. Design of meta-atoms, the fundamental building blocks of metasurfaces, relies on trial-and-error method to achieve target electromagnetic responses. This process includes the characterization of an enormous amount of different meta-atom designs with different physical and geometric parameters, which normally demands huge computational resources. In this paper, a deep learning-based metasurface/meta-atom modeling approach is introduced to significantly reduce the characterization time while maintaining accuracy. Based on a convolutional neural network (CNN) structure, the proposed deep learning network is able to model meta-atoms with free-form 2D patterns and different lattice sizes, material refractive indexes and thicknesses. Moreover, the presented approach features the capability to predict meta-atoms' wide spectrum responses in the timescale of milliseconds, which makes it attractive for applications such as fast meta-atom/metasurface on-demand design and optimization. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Prediction of solar energetic events impacting space weather conditions

引用

Advances in Space Research 2024年

作者： Georgoulis, Manolis K. Yardley, Stephanie L. Guerra, Jordan A. Murray, Sophie A. Ahmadzadeh, Azim Anastasiadis, Anastasios Angryk, Rafal Aydin, Berkay Banerjee, Dipankar Barnes, Graham Bemporad, Alessandro Benvenuto, Federico Bloomfield, D. Shaun Bobra, Monica Campi, Cristina Camporeale, Enrico DeForest, Craig E. Emslie, A. Gordon Falconer, David Feng, Li Gan, Weiqun Green, Lucie M. Guastavino, Sabrina Hapgood, Mike Kempton, Dustin Kitiashvili, Irina Kontogiannis, Ioannis Korsos, Marianna B. Leka, K.D. Massa, Paolo Massone, Anna Maria Nandy, Dibyendu Nindos, Alexander Papaioannou, Athanasios Park, Sung-Hong Patsourakos, Spiros Piana, Michele Rawafi, Nour E. Sadykov, Viacheslav M. Toriumi, Shin Vourlidas, Angelos Wang, Haimin L. Wang, Jason T. Whitman, Kathryn Yan, Yihua Zhukov, Andrei N. Research Center for Astronomy and Applied Mathematics of the Academy of Athens Athens11527 Greece Department of Mathematics Physics and Electrical Engineering Northumbria University Newcastle upon TyneNE1 8ST United Kingdom Department of Meteorology University of Reading ReadingRG6 6ET United Kingdom Paseo Manuel de Lardizabal 4 San Sebastián20018 Spain CIRES University of Colorado BoulderCO80309 United States NOAA Space Weather Prediction Center BoulderCO80305 United States School of Cosmic Physics Dublin Institute for Advanced Studies Dunsink Observatory DublinD15 XR2R Ireland Computer Science Department Georgia State University AtlantaGA30303 United States National Observatory of Athens Penteli15236 Greece Uttarakhand Nainital263001 India NorthWest Research Associates BoulderCO80301 United States INAF Turin Astrophysical Observatory via Osservatorio 20 10025 Pino Torinese Torino Italy MIDA Dipartimento di Matematica Università di Genova via Dodecaneso 35 16146 Genova Italy State of California Office of Data and Innovation 401 I Street Ste 200 SacramentoCA95814 United States Southwest Research Institute BoulderCO80302 United States Department of Physics & Astronomy Western Kentucky University Bowling GreenKY42101 United States Center for Space Plasma and Aeronomic Research University of Alabama in Huntsville HuntsvilleAL35899 United States NASA/Marshall Space Flight Center HuntsvilleAL35812 United States Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China Mullard Space Science Laboratory University College London Holmbury St. MaryRH5 6NT United Kingdom University of Chinese Academy of Sciences Nanjing211135 China Visiting Scientist RAL Space STFC Rutherford Appleton Laboratory Oxfordshire DidcotOX11 0QX United Kingdom NASA Ames Research Center Moffett FieldCA94035 United States An der Sternwarte 16 Potsdam14482 Germany Dipartime

Aiming to assess the progress and current challenges on the formidable problem of the prediction of solar energetic events since the COSPAR/ International Living With a Star (ILWS) Roadmap paper of Schrijver et al. (2015), we attempt an overview of the current status of global research efforts. By solar energetic events we refer to flares, coronal mass ejections (CMEs), and solar energetic particle (SEP) events. The emphasis, therefore, is on the prediction methods of solar flares and eruptions, as well as their associated SEP manifestations. This work complements the COSPAR International Space Weather Action Teams (ISWAT) review paper on the understanding of solar eruptions by Linton et al. (2023) (hereafter, ISWAT review papers are conventionally referred to as ’Cluster’ papers, given the ISWAT structure). Understanding solar flares and eruptions as instabilities occurring above the nominal background of solar activity is a core solar physics problem. We show that effectively predicting them stands on two pillars: physics and statistics. With statistical methods appearing at an increasing pace over the last 40 years, the last two decades have brought the critical realization that data science needs to be involved, as well, as volumes of diverse ground- and space-based data give rise to a Big Data landscape that cannot be handled, let alone processed, with conventional statistics. Dimensionality reduction in immense parameter spaces with the dual aim of both interpreting and forecasting solar energetic events has brought artificial intelligence (AI) methodologies, in variants of machine and deep learning, developed particularly for tackling Big Data problems. With interdisciplinarity firmly present, we outline an envisioned framework on which statistical and AI methodologies should be verified in terms of performance and validated against each other. We emphasize that a homogenized and streamlined method validation is another open challenge. The performance of the

关键词： Solar energy

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Application of Surface Plasmon Resonance Sensor with Polypyrrole Chitosan Graphene Oxide layer to Detect the Napropamide 5

Application of Surface Plasmon Resonance Sensor with Polypyr...

引用

5th International Conference on Fundamental and Applied Sciences, ICFAS 2018

作者： Sadrolhosseini, Amir Reza Rashid, Suraya Abdul Soleimani, Hassan Shafie, Suhaidi Noor, A.S.M. Mohammadi, Ahmad Institute of Advanced Technology Universiti Putra Malaysia Serdang Selangor43400UPM Malaysia Materials Processing and Technology Laboratory Nanomaterials and Nanotechnology Group Institute of Advanced Technology Universiti Putra Malaysia Serdang Selangor43400UPM Malaysia Department of Fundamental and Applied Science Universiti Teknologi PETRONAS Seri Iskandar3175 Malaysia Department of Electrical and Electronic Engineering Faculty of Engineering Universiti Putra Malaysia Serdang Selangor43400 UPM Malaysia Faculty of Engineering Universiti Putra Malaysia Serdang Selangor43400UPM Malaysia Department of Electrical and Computer Engineering Buein Zahra Technical University Buein Zahra Qazvin Iran

A polypyrrole Chitosan Graphene Oxide (PPy-Chi/GO) composite layer was prepared using the electro-chemical method. The PPy-Chi/GO layer was used to measure the low concentration of napropamide. In this study, the PPy-Chi/GO composite layer was deposited on the gold layer side of a microscope's glass slide in different thicknesses. The morphology, thickness, roughness, and refractive index of the layer were obtained using field effect scanning electron microscopy, profilemeter, atomic force microscopy, and the surface plasmon resonance technique. The structure of the composite layer was investigated using X-Ray diffraction. The peak that were observed from X-ray diffraction was at 9.9°, corresponding to GO, and the broad peak between 25° and 27° was related to polypyrrole and chitosan. The PPy-Chi/GO layer was used to measure the concentration of napropamide dissolved in hexane in the range of 0.01 to 100 ppm using the surface plasmon resonance technique;the limitation of sensor was 0.1 ppm. © Published under licence by IOP Publishing Ltd.

关键词： Polypyrroles

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Machine learning for forecasting mid price movement using limit order book data

arXiv

引用

arXiv 2018年

作者： Nousi, Paraskevi Tsantekidis, Avraam Passalis, Nikolaos Ntakaris, Adamantios Kanniainen, Juho Tefas, Anastasios Gabbouj, Moncef Iosifidis, Alexandros Department of Informatics Aristotle University of Thessaloniki Greece Laboratory of Signal Processing Tampere University of Technology Tampere Finland Laboratory of Industrial and Information Management Tampere University of Technology Tampere Finland Department of Engineering Electrical and Computer Engineering Aarhus University Denmark

Forecasting the movements of stock prices is one the most challenging problems in financial markets analysis. In this paper, we use Machine Learning (ML) algorithms for the prediction of future price movements using limit order book data. Two different sets of features are combined and evaluated: handcrafted features based on the raw order book data and features extracted by ML algorithms, resulting in feature vectors with highly variant dimensionalities. Three classifiers are evaluated using combinations of these sets of features on two different evaluation setups and three prediction scenarios. Even though the large scale and high frequency nature of the limit order book poses several challenges, the scope of the conducted experiments and the significance of the experimental results indicate that Machine Learning highly befits this task carving the path towards future research in this field. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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Using deep learning for price prediction by exploiting stationary limit order book features

arXiv

引用

arXiv 2018年

作者： Tsantekidis, Avraam Passalis, Nikolaos Tefas, Anastasios Kanniainen, Juho Gabbouj, Moncef Iosifidis, Alexandros Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece Laboratory of Industrial and Information Management Tampere University of Technology Tampere Finland Laboratory of Signal Processing Tampere University of Technology Tampere Finland Department of Engineering Electrical and Computer Engineering Aarhus University Denmark

The recent surge in Deep Learning (DL) research of the past decade has successfully provided solutions to many difficult problems. The field of quantitative analysis has been slowly adapting the new methods to its problems, but due to problems such as the non-stationary nature of financial data, significant challenges must be overcome before DL is fully utilized. In this work a new method to construct stationary features, that allows DL models to be applied effectively, is proposed. These features are thoroughly tested on the task of predicting mid price movements of the Limit Order Book. Several DL models are evaluated, such as recurrent Long Short Term Memory (LSTM) networks and Convolutional Neural Networks (CNN). Finally a novel model that combines the ability of CNNs to extract useful features and the ability of LSTMs' to analyze time series, is proposed and evaluated. The combined model is able to outperform the individual LSTM and CNN models in the prediction horizons that are tested. Copyright © 2018, The Authors. All rights reserved.

关键词： Long short-term memory

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Density fluctuations associated with turbulence and waves: First observations by solar orbiter

arXiv

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arXiv 2021年

作者： Khotyaintsev, Yuri V. Graham, D.B. Vaivads, A. Steinvall, K. Edberg, N.J.T. Eriksson, A.I. Johansson, E.P.G. Sorriso-Valvo, L. Maksimovic, M. Bale, S.D. Chust, T. Krasnoselskikh, V. Kretzschmar, M. Lorfèvre, E. Plettemeier, D. Souček, J. Steller, M. Štverák, Š. Trávníček, P. Vecchio, A. Horbury, T.S. O’Brien, H. Evans, V. Angelini, V. Uppsala75121 Sweden Division of Space and Plasma Physics School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden Space and Plasma Physics Department of Physics and Astronomy Uppsala University Uppsala75120 Sweden LESIA Observatoire de Paris Université PSL CNRS Sorbonne Université Univ. Paris Diderot Sorbonne Paris Cité 5 place Jules Janssen Meudon92195 France Space Sciences Laboratory University of California BerkeleyCA United States Physics Department University of California BerkeleyCA United States LPP CNRS Ecole Polytechnique Sorbonne Université Observatoire de Paris Université Paris-Saclay Palaiseau Paris France LPC2E CNRS 3A avenue de la Recherche Scientifique Orléans France Université d’Orléans Orléans France CNES 18 Avenue Edouard Belin Toulouse31400 France Technische Universität Dresden Helmholtz Str. 10 DresdenD-01187 Germany Institute of Atmospheric Physics of the Czech Academy of Sciences Prague Czech Republic Space Research Institute Austrian Academy of Sciences Graz Austria Astronomical Institute of the Czech Academy of Sciences Prague Czech Republic Radboud Radio Lab Department of Astrophysics Radboud University Nijmegen Netherlands Imperial College London South Kensington Campus LondonSW7 2AZ United Kingdom

Aims. The aim of this work is to demonstrate that the probe-to-spacecraft potential measured by RPW on Solar Orbiter can be used to derive the plasma (electron) density measurement, which both has a high temporal resolution and is of high accuracy. To investigate the physical nature of the solar wind turbulence and waves we analyze the density and magnetic field fluctuations around the proton cyclotron frequency observed by Solar Orbiter during the first perihelion encounter (∼0.5 AU away from the Sun). Methods. We use the plasma density based on measurements of the probe-to-spacecraft potential in combination with magnetic field measurements by MAG to study fields and density fluctuations in the solar wind. In particular, we use the polarization of the wave magnetic field, the phase between the compressible magnetic field and density fluctuations, and the compressibility ratio (the ratio of the normalized density fluctuations to the normalized compressible fluctuations of B) to characterize the observed waves and turbulence. Results. We find that the density fluctuations are 180◦ out-of-phase (anti-correlated) with the compressible component of magnetic fluctuations for intervals of turbulence, while they are in phase for the circular-polarized waves. We analyze in detail two specific events with simultaneous presence of left- and right-handed waves at different frequencies. We compare observed wave properties to a prediction of the three-fluid (electrons, protons and alphas) model. We find a limit on the observed wavenumbers, 10-6 6 m-1, which corresponds to wavelength 7 × 106 > λ > 106 m. We conclude that most likely both the left- and right-handed waves correspond to the low-wavenumber part (close to the cut-off at ΩcHe++) proton-band electromagnetic ion cyclotron (left-handed wave in the plasma frame confined to the frequency range ΩcHe++ cH+) waves propagating in the outwards and inwards directions respectively. The fact that both wave polarizations are observ

关键词： Solar wind

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Publisher Correction: Brain imaging with portable low-field MRI

Nature Reviews Bioengineering

引用

Nature Reviews Bioengineering 2023年 680页

作者： W. Taylor Kimberly Annabel J. Sorby-Adams Andrew G. Webb Ed X. Wu Rachel Beekman Kevin N. Sheth Ritvij Bowry Steven J. Schiff Adam de Havenon Francis X. Shen Gordon Sze Pamela Schaefer Juan Eugenio Iglesias Matthew S. Rosen Department of Neurology and the Center for Genomic Medicine Massachusetts General Hospital and Harvard Medical School Boston MA USA Department of Radiology Leiden University Medical Center Leiden The Netherlands Laboratory of Biomedical Imaging and Signal Processing Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong SAR China Division of Neurocritical Care and Emergency Neurology Department of Neurology Yale New Haven Hospital and Yale School of Medicine Yale Center for Brain & Mind Health New Haven CT USA Departments of Neurosurgery and Neurology McGovern Medical School University of Texas Health Neurosciences Houston TX USA Department of Neurosurgery Yale School of Medicine New Haven CT USA Division of Vascular Neurology Department of Neurology Yale New Haven Hospital and Yale School of Medicine New Haven CT USA Harvard Medical School Center for Bioethics Harvard law School Boston MA USA Department of Psychiatry Massachusetts General Hospital Boston MA USA Department of Radiology Yale New Haven Hospital and Yale School of Medicine New Haven CT USA Division of Neuroradiology Department of Radiology Massachusetts General Hospital and Harvard Medical School Boston MA USA Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital and Harvard Medical School Boston MA USA Centre for Medical Image Computing University College London London UK Computer Science and AI Laboratory Massachusetts Institute of Technology Boston MA USA

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