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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 Low-Symmetry Copper Benzenehexathiol Coordination Polymer with In-Plane electrical Anisotropy

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Angewandte Chemie 2025年

作者： Dr. Zhiyong Wang Dr. Hio-Ieng Un Tsai-Jung Liu Dr. Baokun Liang Dr. Miroslav Polozij Dr. Mike Hambsch Jonas F. Pöhls Prof. R. Thomas Weitz Prof. Stefan C. B. Mannsfeld Prof. Ute Kaiser Prof. Thomas Heine Prof. Henning Sirringhaus Prof. Xinliang Feng Prof. Renhao Dong Max Planck Institute of Microstructure Physics Weinberg 2 06120 Halle (Saale) Germany Center for Advancing Electronics Dresden (cfaed) Faculty of Chemistry and Food Chemistry Dresden University of Technology 01062 Dresden Germany Optoelectronics Group Cavendish Laboratory University of Cambridge JJ Thomson Avenue Cambridge CB3 0HE UK Ulm University 89081 Ulm Germany Helmholtz-Zentrum Dresden-Rossendorf HZDR Bautzner Landstr. 400 01328 Dresden Germany Center for Advanced Systems Understanding CASUS Conrad-Schiedt-Straße 20 02826 Görlitz Germany Center for Advancing Electronics Dresden (cfaed) Faculty of Electrical and Computer Engineering Dresden University of Technology 01062 Dresden Germany First Institute of Physics Georg August University of Göttingen 37077 Göttingen Germany Department of Chemistry The University of Hong Kong Hong Kong 999077 China Materials Innovation Institute for Life Sciences and Energy (MILES) HKU-SIRI Shenzhen 518048 China

electrically conductive coordination polymers (ECCPs), particularly those incorporating benzenehexathiol (BHT) ligands, are emerging as a distinctive class of electronic materials with tunable semiconducting and metallic properties. However, the exploration of novel ECCPs with low-symmetry structures and electrical anisotropy remains under development. Here, we report the on-water surface synthesis of a novel ECCP, namely Cu 5 BHT, which exhibits a low-symmetry structure and unique in-plane electrical anisotropy that differs from the well-known Cu 3 BHT phase. Utilizing imaging and diffraction techniques, we elucidate the unit cell and crystal structure of Cu 5 BHT, revealing an asymmetric arrangement of the kagome resembling lattice connected by two different secondary building units: square planar CuS 4 and non-planar Cu 2 S 4 . Theoretical studies indicate that Cu 5 BHT is metallic and exhibits in-plane electrical anisotropy due to the structure arranged in interconnected well-conducting CuS 4 chains and less-conducting Cu 2 S 4 slabs oriented along single crystal direction. Single-crystal electrical measurements confirm a metallic character characterized by the increase of conductance upon cooling. Notably, the measured conductance along different crystal directions within the ab plane unambiguously reveals a significant anisotropy, with an anisotropic factor reaching ~8. This work demonstrates a novel low-symmetry ECCP and highlights its potential for achieving in-plane electrical anisotropy.

关键词： electrically conductive coordination polymers metallic kagome lattice low-symmetry structure in-plane electrical anisotropy

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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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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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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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DESI Strong Lens Foundry I: HST Observations and Modeling with GIGA-Lens

arXiv

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arXiv 2025年

作者： Huang, X. Baltasar, S. Ratier-Werbin, N. Storfer, C. Sheu, W. Agarwal, S. Tamargo-Arizmendi, M. Schlegel, D.J. Aguilar, J. Ahlen, S. Aldering, G. Banka, S. BenZvi, S. Bianchi, D. Bolton, A. Brooks, D. Cikota, A. Claybaugh, T. de la Macorra, A. Dey, A. Doel, P. Edelstein, J. Filipp, A. Forero-Romero, J.E. Gaztañaga, E. Gontcho, S.A. Gontcho Gu, A. Gutierrez, G. Honscheid, K. Jullo, E. Juneau, S. Kehoe, R. Kirkby, D. Kisner, T. Kremin, A. Kwon, K.J. Lambert, A. Landriau, M. Lang, D. Le Guillou, L. Liu, J. Meisner, A. Miquel, R. Moustakas, J. Myers, A.D. Perlmutter, S. Pérez-Ràfols, I. Prada, F. Rossi, G. Rubin, D. Sanchez, E. Schubnell, M. Shu, Y. Silver, E. Sprayberry, D. Suzuki, N. Tarlé, G. Weaver, B.A. Zou, H. Department of Physics & Astronomy University of San Francisco San FranciscoCA94117 United States Physics Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Department of Physics Complutense University of Madrid Madrid28040 Spain Department of Mathematics Complutense University of Madrid Madrid28040 Spain Institute for Astronomy University of Hawai’i HonoluluHI96822-1897 United States Department of Physics & Astronomy University of California Los Angeles Los AngelesCA90095 United States University of Chicago Department of Astronomy ChicagoIL60615 United States Department of Physics & Astronomy University of Pittsburgh PittsburghPA15260 United States Physics Dept. Boston University 590 Commonwealth Avenue BostonMA02215 United States Department of Electrical Engineering & Computer Sciences University of California Berkeley BerkeleyCA94720 United States Department of Physics & Astronomy University of Rochester 206 Bausch and Lomb Hall P.O. Box 270171 RochesterNY14627-0171 United States Dipartimento di Fisica "Aldo Pontremoli" Università degli Studi di Milano Via Celoria 16 MilanoI-20133 Italy NSF’s National Optical-Infrared Astronomy Research Laboratory TucsonAZ85719 United States Department of Physics & Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Gemini Observatory NSF’s NOIRLab Casilla 603 La Serena Chile Instituto de Física Universidad Nacional Autónoma de México Circuito de la Investigación Científica Ciudad Universitaria Cd. de MéxicoC. P. 04510 Mexico Space Sciences Laboratory University of California Berkeley 7 Gauss Way BerkeleyCA94720 United States Université de Montréal Physics Department 1375 Av. Thérèse-Lavoie-Roux MontréalQCH2V 0B3 Canada Ciela – Montreal Institute for Astrophysical Data Analysis and Machine Learning 1375 Av. Thérèse-Lavoie-Roux MontréalQCH2V 0B3 Canada Technical University Munich TUM School of Natural Sciences

We present the Dark Energy Spectroscopic Instrument (DESI) Strong Lens Foundry. We discovered ∼ 3500 new strong gravitational lens candidates in the DESI Legacy Imaging Surveys using residual neural networks (ResNet). We observed a subset (51) of our candidates using the Hubble Space Telescope (HST). All of them were confirmed to be strong lenses. We also briefly describe spectroscopic follow-up observations by DESI and Keck NIRES programs. From this very rich dataset, a number of studies will be carried out, including evaluating the quality of the ResNet search candidates and lens modeling. In this paper, we present our initial effort in these directions. In particular, as a demonstration, we present the lens model for DESI-165.4754-06.0423, with imaging data from HST, and lens and source redshifts from DESI and Keck NIRES, respectively. In this effort, we have applied a fully forward-modeling Bayesian approach (GIGA-Lens), using multiple GPUs, for the first time in both regards, to a strong lens with HST data, or any high resolution imaging. © 2025, CC BY.

关键词： Dark energy

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

arXiv

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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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