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Author Correction: A Satellite Imagery Dataset for Long-Term Sustainable Development in United States Cities

Scientific data 2023年第1期10卷 928页

作者： Yanxin Xi Yu Liu Tong Li Jingtao Ding Yunke Zhang Sasu Tarkoma Yong Li Pan Hui Department of Computer Science University of Helsinki Helsinki Finland. Beijing National Research Center for Information Science and Technology (BNRist) Beijing P. R. China. Department of Electronic Engineering Tsinghua University Beijing P. R. China. Beijing National Research Center for Information Science and Technology (BNRist) Beijing P. R. China. liyong07@***. Department of Electronic Engineering Tsinghua University Beijing P. R. China. liyong07@***. Department of Computer Science University of Helsinki Helsinki Finland. panhui@ust.hk. Computational Media and Arts Thrust Hong Kong University of Science and Technology (Guangzhou) Guangzhou P. R. China. panhui@ust.hk. Division of Emerging Interdisciplinary Areas Hong Kong University of Science and Technology Hong Kong P. R. China. panhui@ust.hk.

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Compressive sensing based channel estimation for millimeter-wave full-dimensional MIMO with lens-array

arXiv

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arXiv 2019年

作者： Wan, Ziwei Gao, Zhen Shim, Byonghyo Yang, Kai Mao, Guoqiang Alouini, Mohamed-Slim School of Information and Electronics Beijing Institute of Technology Beijing100081 China Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing100081 China Institute of New Media and Communications School of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of School of Electrical and Data Engineering University of Technology Sydney Australia Thuwal Makkah Province Saudi Arabia

Channel estimation (CE) for millimeter-wave (mmWave) lens-array suffers from prohibitive training overhead, whereas the state-of-the-art solutions require an extra complicated radio frequency phase shift network. By contrast, lens-array using antenna switching network (ASN) simplifies the hardware, but the associated CE is a challenging task due to the constraint imposed by ASN. This paper proposes a compressive sensing (CS)-based CE solution for full-dimensional (FD) lens-array, where the mmWave channel sparsity is exploited. Specifically, we first propose an approach of pilot training under the more severe haraware constraint imposed by ASN, and formulate the associated CE of lens-array as a CS problem. Then, a redundant dictionary is tailored for FD lens-array to combat the power leakage caused by the continuous angles of multipath components. Further, we design the baseband pilot signals to minimize the total mutual coherence of the measurement matrix based on CS theory for more reliable CE performance. Our solution provides a framework for applying CS techniques to lens-array using simple and practical ASN. Simulation results demonstrate the effectiveness of the proposed scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： Channel estimation

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Cs-vqa: Visual question answering with compressively sensed images

arXiv

引用

arXiv 2018年

作者： Huang, Li-Chi Kulkarni, Kuldeep Jha, Anik Lohit, Suhas Jayasuriya, Suren Turaga, Pavan School of Arts Media and Engineering Arizona State University School of Electrical Computer and Energy Engineering Arizona State University Department of Electrical and Computer Engineering Carnegie Mellon University

Visual Question Answering (VQA) is a complex semantic task requiring both natural language processing and visual recognition. In this paper, we explore whether VQA is solvable when images are captured in a sub-Nyquist compressive paradigm. We develop a series of deep-network architectures that exploit available compressive data to increasing degrees of accuracy, and show that VQA is indeed solvable in the compressed domain. Our results show that there is nominal degradation in VQA performance when using compressive measurements, but that accuracy can be recovered when VQA pipelines are used in conjunction with state-of-the-art deep neural networks for CS reconstruction. The results presented yield important implications for resource-constrained VQA *** Codes 68 Copyright © 2018, The Authors. All rights reserved.

关键词： computer vision

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Layered image compression using scalable auto-encoder

arXiv

引用

arXiv 2019年

作者： Jia, Chuanmin Liu, Zhaoyi Wang, Yao Ma, Siwei Gao, Wen Department of Electrical and Computer Engineering Tandon School of Engineering New York University BrooklynNY11201 United States Peking University Institute of Digital Media Beijing100871 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China

This paper presents a novel convolutional neural network (CNN) based image compression framework via scalable auto-encoder (SAE). Specifically, our SAE based deep image codec consists of hierarchical coding layers, each of which is an end-to-end optimized auto-encoder. The coarse image content and texture are encoded through the first (base) layer while the consecutive (enhance) layers iteratively code the pixel-level reconstruction errors between the original and former reconstructed images. The proposed SAE structure alleviates the need to train multiple models for different bit-rate points by recently proposed auto-encoder based codecs. The SAE layers can be combined to realize multiple rate points, or to produce a scalable stream. The proposed method has similar rate-distortion performance in the low-to-medium rate range as the state-of-the-art CNN based image codec (which uses different optimized networks to realize different bit rates) over a standard public image dataset. Furthermore, the proposed codec generates better perceptual quality in this bit rate range. Copyright © 2019, The Authors. All rights reserved.

关键词： Image enhancement

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Perfect optical coherence lattices

arXiv

引用

arXiv 2021年

作者： Chunhao, Liang Xin, Liu Zhiheng, Xu Fei, Wang Sergey, A. Ponomarenko Yangjian, Cai Ma, Pujuan Wei, Wen Shandong Provincial Engineering and Technical Center of Light Manipulation & Shandong Provincial Key Laboratory of Optics and Photonic Devices School of Physics and Electronics Shandong Normal University Jinan250014 China Collaborative Innovation Center of Light Manipulations and Applications Shandong Normal University Jinan250358 China School of Physical Science and Technology Soochow University Suzhou215006 China College of Mathematics and Physics Science Hunan University of Arts and Science Changde415000 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3J 2X4 Canada Department of Physics and Atmospheric Science Dalhousie University HalifaxNSB3H 4R2 Canada

We advance and experimentally implement a protocol to generate perfect optical coherence lattices (OCL) that are not modulated by an envelope field. Structuring the amplitude and phase of an input partially coherent beam in a Fourier plane of an imaging system lies at the heart of our protocol. In the proposed approach, the OCL node profile depends solely on the degree of coherence (DOC) of the input beam such that, in principle, any lattice structure can be attained via proper manipulations in the Fourier plane. Moreover, any genuine partially coherent source can serve as an input to our lattice generating imaging system. Our results are anticipated to find applications to optical field engineering and multi-target probing among others. © 2021, CC BY.

关键词： Imaging systems

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Personalized Healthcare: Expandable Polymer Assisted Wearable Personalized Medicinal Platform (Adv. Mater. Technol. 10/2020)

引用

Advanced Materials Technologies 2020年第10期5卷

作者： Wedyan Babatain Abdurrahman Gumus Irmandy Wicaksono Ulrich Buttner Nazek El-atab Mutee Ur Rehman Nadeem Qaiser David Conchouso Muhammad Mustafa Hussain Electrical Engineering Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955 6900 Saudi Arabia mmh Labs Electrical Engineering Computer Electrical Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Department of Electrical and Electronics Engineering Izmir Institute of Technology Urla Izmir 35430 Turkey MIT Media Lab Massachusetts Institute of Technology Cambridge MA 02139 USA EECS Cory Hall University of California Berkeley Berkeley CA 94720 USA

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Why is the Winner the Best?

Why is the Winner the Best?

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Quantum Games and Interactive Tools for Quantum Technologies Outreach and Education

arXiv

引用

arXiv 2022年

作者： Seskir, Zeki C. Migdal, Piotr Weidner, Carrie Anupam, Aditya Case, Nicky Davis, Noah Decaroli, Chiara Ercan, İlke Foti, Caterina Gora, Pawel Jankiewicz, Klementyna La Cour, Brian R. Malo, Jorge Yago Maniscalco, Sabrina Naeemi, Azad Nita, Laurentiu Parvin, Nassim Scafirimuto, Fabio Sherson, Jacob F. Surer, Elif Wootton, James Yeh, Lia Zabello, Olga Chiofalo, Marilù Karlsruhe Institute of Technology ITAS Karlstraße 11 Karlsruhe76133 Germany Quantum Flytrap ul. Ceglowska 29 Warsaw01-809 Poland Quantum Engineering Technology Laboratories H.H. Wills Physics Laboratory Department of Electrical and Electronic Engineering University of Bristol BristolBS8 1FD United Kingdom Georgia Institute of Technology School of Literature Media and Communication 686 Cherry St NW AtlantaGA30313 United States The University of Texas at Austin Center for Quantum Research Applied Research Laboratories 10000 Burnet Rd AustinTX78758 United States National Quantum Computing Center Oxfordshire DidcotOX11 0GD United Kingdom TU Delft Department of Microelectronics Mekelweg 4 Delft2628 CD Netherlands QPlayLearn Aalto University InstituteQ - The Finnish Quantum Institute Department of Applied Physics AaltoFI-00076 11000 Finland Quantum AI Foundation Sanocka 9/103 Warsaw02-110 Poland University of Pisa Department of Physics Largo Bruno Pontecorvo 3 PisaI-56126 Italy University of Helsinki InstituteQ - The Finnish Quantum Institute Department of Physics Helsinki Finland Georgia Institute of Technology School of Electrical and Computer Engineering 777 Atlantic Drive NW AtlantaGA30332 United States Quarks Interactive Strada Bailor 93a Miercurea-Ciuc Harghita4500 Romania IBM Quantum IBM Research - Zurich Säumerstrasse 4 Rüschlikon8803 Switzerland ScienceAtHome Fuglesangs Allé 4 Aarhus8210 Denmark Aarhus University Institute for Physics and Astronomy Ny Munkegade 120 Aarhus8000 Denmark Middle East Technical University Graduate School of Informatics Department of Modeling and Simulation Üniversiteler Mah. Dumlupınar Blv. No:1 Ankara06800 Turkey University of Oxford Department of Computer Science Wolfson Building Parks Road OxfordOX1 3QD United Kingdom IBM Quantum IBM T. J. Watson Research Center Yorktown HeightsNY10598 United States Offenburg University of Applied Sciences Department of Business and Industrial Eng

In this article, we provide an extensive overview of a wide range of quantum games and interactive tools that have been employed by the community in recent years. The paper presents selected tools, as described by their developers. The list includes Hello Quantum, Hello Qiskit, Particle in a Box, Psi and Delta, QPlayLearn, Virtual Lab by Quantum Flytrap, Quantum Odyssey, ScienceAtHome, and The Virtual Quantum Optics Laboratory. Additionally, we present events for quantum game development: hackathons, game jams, and semester projects. Furthermore, we discuss the Quantum Technologies Education for Everyone (QUTE4E) pilot project, which illustrates an effective integration of these interactive tools with quantum outreach and education activities. Finally, we aim at providing guidelines for incorporating quantum games and interactive tools in pedagogic materials to make quantum technologies more accessible for a wider population. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum optics

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Clusters and the entropy in opinion dynamics on complex networks

arXiv

引用

arXiv 2019年

作者： Han, Wenchen Feng, Yuee Qian, Xiaolan Yang, Qihui Huang, Changwei College of Physics and Electronic Engineering Sichuan Normal University Chengdu610101 China College of Arts and Sciences Jiangsu Aviation Technical College Zhenjiang212134 China School of Media and Engineering Communication University of Zhejiang Hangzhou310018 China Department of Electrical and Computer Engineering Kansas State University ManhattanKS66506 United States School of Computer Electronics and Information Guangxi University Nanning530004 China

In this work, we investigate a heterogeneous population in the modified Hegselmann-Krause opinion model on complex networks. We introduce the Shannon information entropy about all relative opinion clusters to characterize the cluster profile in the final configuration. Independent of network structures, there exists the optimal stubbornness of one subpopulation for the largest number of clusters and the highest entropy. Besides, there is the optimal bounded confidence (or subpopulation ratio) of one subpopulation for the smallest number of clusters and the lowest entropy. However, network structures affect cluster profiles indeed. A large average degree favors consensus for making different networks more similar with complete graphs. The network size has limited impact on cluster profiles of heterogeneous populations on scale-free networks but has significant effects upon those on small-world networks. Copyright © 2019, The Authors. All rights reserved.

关键词： Small-world networks

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Rate-adaptive neural networks for spatial multiplexers

arXiv

引用

arXiv 2018年

作者： Lohit, Suhas Singh, Rajhans Kulkarni, Kuldeep Turaga, Pavan Department of Electrical Computer and Electrical Engineering Department of Arts Media and Engineering Arizona State University TempeAZ United States Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States

In resource-constrained environments, one can employ spatial multiplexing cameras to acquire a small number of measurements of a scene, and perform effective reconstruction or high-level inference using purely data-driven neural networks. However, once trained, the measurement matrix and the network are valid only for a single measurement rate (MR) chosen at training time. To overcome this drawback, we answer the following question: How can we jointly design the measurement operator and the reconstruction/inference network so that the system can operate over a range of MRs? To this end, we present a novel training algorithm, for learning rate-adaptive networks. Using standard datasets, we demonstrate that, when tested over a range of MRs, a rate-adaptive network can provide high quality reconstruction over a the entire range, resulting in up to about 15 dB improvement over previous methods, where the network is valid for only one MR. We demonstrate the effectiveness of our approach for sample-efficient object tracking where video frames are acquired at dynamically varying MRs. We also extend this algorithm to learn the measurement operator in conjunction with image recognition networks. Experiments on MNIST and CIFAR-10 confirm the applicability of our algorithm to different tasks. Copyright © 2018, The Authors. All rights reserved.

关键词： Compressed sensing

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