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Geographic information science in the era of geospatial big data: A cyberspace perspective

The Innovation 2022年第5期3卷 21-22页

作者： Xintao Liu Min Chen Christophe Claramunt Michael Batty Mei-Po Kwan Ahmad MSenousi Tao Cheng Josef Strobl Arzu Cöltekin John Wilson Temenoujka Bandrova Milan Konecny Paul MTorrens Fengyuan Zhang Li He Jinfeng Wang Carlo Ratti Olaf Kolditz Alexander Klippel Songnian Li Hui Lin Guonian Lu Department of Land Surveying and Geo-Informatics Hong Kong Polytechnic UniversityHong Kong SARKowloonChina Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China The French Naval Academy Research Institute 29240 Lanveoc-PoulmicFrance The Bartlett Centre for Advanced Spatial Analysis(CASA) University College London(UCL)WC1E 6BT LondonUK Institute of Space and Earth Information Science The Chinese University of Hong KongHong Kong SARShatin 999077China Department of Civil Engineering Cairo University12613 CairoEgypt SpaceTimeLab Department of CivilEnvironmental and Geomatic EngineeringUniversity College London(UCL)WC1E 6BT LondonUK Department of Geoinformatics University of Salzburg5020 SalzburgAustria Institute of Interactive Technologies(IIT) University of Applied Sciences and Arts Northwestern Switzerland FHNW5210 WindischSwitzerland Spatial Sciences Institute University of Southern CaliforniaLos AngelesCA 90007USA Laboratory on Cartography Department of Photogrammetry and CartographyUniversity of ArchitectureCivil Engineering and Geodesy1164 SofiaBulgaria Laboratory on Geoinformatics and Cartography Department of GeographyMasaryk University61137 BrnoCeskáRepublika Computer Science and Engineering Tandon School&Center for Urban Science+ProgressNew York UniversityNew YorkNY 10012USA School of Humanities and Social Science Xi’an Jiaotong UniversityXi’an 710049China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China Department of Urban Studies and Planning Massachusetts Institute of TechnologyCambridgeMA 02139USA Centre for Environmental Research Helmholtz Centre for Environmental Research-UFZ04318 LeipzigGermany Laboratory of Geo-information Science and Remote Sensing Wageningen University&ResearchWageningen 6708the Netherlands Department of Civil Engineering Toronto Metropolitan UniversityTorontoON M5B 2K3Canada School of Geography and Env

The advent of information and communication technology and the Internet of Things have led our society toward a digital *** proliferation of personal computers,smartphones,intelligent autonomous sensors,and pervasive network interactions with individuals have gradually shifted human activities from offline to online and from in person to *** transformation has brought a series of challenges in a variety of fields,such as the dilemma of placelessness,some aspects of timelessness(no time relevance),and the changing relevance of distance in the field of geographic information science(GIscience).In the last two decades,“cyber thinking”in GIscience has received significant attention from different *** instance,human activities in“cyberspace”need to be reconsidered when coupled with the geographic space to observe the first law of geography.

关键词： computer cyber instance

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Improved Upper Bounds on the Growth Constants of Polyominoes and Polycubes

arXiv

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arXiv 2019年

作者： Barequet, Gill Shalah, Mira Center for Graphics Geometric Computing Computer Science Department Technion—Israel Institute of Technology Haifa3200003 Israel Computer Science Department Stanford University CA United States

A d-dimensional polycube is a facet-connected set of cells (cubes) on the d-dimensional cubical lattice Zd. Let Ad(n) denote the number of d-dimensional polycubes (distinct up to translations) with n cubes, and λd denote the limit of the ratio Ad(n+1)/Ad(n) as n → ∞. The exact value of λd is still unknown rigorously for any dimension d ≥ 2;the asymptotics of λd, as d → ∞, also remained elusive as of today. In this paper, we revisit and extend the approach presented by Klarner and Rivest in 1973 to bound A2(n) from above. Our contributions are: • Using available computing power, we prove that λ2 ≤ 4.5252. This is the first improvement of the upper bound on λ2 in almost half a century;• We prove that λd ≤ (2d−2)e+o(1) for any value of d ≥ 2, using a novel construction of a rational generating function which dominates that of the sequence (Ad(n));• For d = 3, this provides a subtantial improvement of the upper bound on λ3 from 12.2071 to 9.8073;• However, we implement an iterative process in three dimensions, which improves further the upper bound on λ3 to 9.3835;Copyright © 2019, The Authors. All rights reserved.

关键词： Rational functions

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Application-driven Validation of Posteriors in Inverse Problems

arXiv

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arXiv 2023年

作者： Adler, Tim J. Nölke, Jan-Hinrich Reinke, Annika Tizabi, Minu Dietlinde Gruber, Sebastian Trofimova, Dasha Ardizzone, Lynton Jaeger, Paul F. Buettner, Florian Köthe, Ullrich Maier-Hein, Lena Im Neuenheimer Feld 280 Heidelberg69120 Germany Faculty of Mathematics and Computer Science Heidelberg University Im Neuenheimer Feld 205 Heidelberg69120 Germany Heidelberg HI Helmholtz Imaging Im Neuenheimer Feld 280 Heidelberg69120 Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Im Neuenheimer Feld 460 Heidelberg69120 Germany Heidelberg Germany Heidelberg Interactive Machine Learning Group Im Neuenheimer Feld 280 Heidelberg69120 Germany Department of Informatics Goethe University Frankfurt Frankfurt Germany Department of Medicine Goethe University Frankfurt Frankfurt Germany partner site Frankfurt a partnership between DKFZ and UCT Frankfurt-Marburg Frankfurt Germany Im Neuenheimer Feld 280 Heidelberg69120 Germany Frankfurt Cancer Institute Frankfurt Germany

Current deep learning-based solutions for image analysis tasks are commonly incapable of handling problems to which multiple different plausible solutions exist. In response, posterior-based methods such as conditional Diffusion Models and Invertible Neural Networks have emerged;however, their translation is hampered by a lack of research on adequate validation. In other words, the way progress is measured often does not reflect the needs of the driving practical application. Closing this gap in the literature, we present the first systematic framework for the application-driven validation of posterior-based methods in inverse problems. As a methodological novelty, it adopts key principles from the field of object detection validation, which has a long history of addressing the question of how to locate and match multiple object instances in an image. Treating modes as instances enables us to perform mode-centric validation, using well-interpretable metrics from the application perspective. We demonstrate the value of our framework through instantiations for a synthetic toy example and two medical vision use cases: pose estimation in surgery and imaging-based quantification of functional tissue parameters for diagnostics. Our framework offers key advantages over common approaches to posterior validation in all three examples and could thus revolutionize performance assessment in inverse problems. © 2023, CC BY-NC-ND.

关键词： Deep learning

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The ZTF Source Classification Project: III. A Catalog of Variable Sources

arXiv

引用

arXiv 2023年

作者： Healy, Brian F. Coughlin, Michael W. Mahabal, Ashish A. Laz, Theophile Jegou du Drake, Andrew Graham, Matthew J. Hillenbrand, Lynne A. van Roestel, Jan Szkody, Paula Zielske, LeighAnna Guiga, Mohammed Hassan, Muhammad Yusuf Hughes, Jill L. Nir, Guy Parikh, Saagar Park, Sungmin Purohit, Palak Rebbapragada, Umaa Reed, Draco Warshofsky, Daniel Wold, Avery Bloom, Joshua S. Masci, Frank J. Riddle, Reed Smith, Roger School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States Center for Data Driven Discovery California Institute of Technology PasadenaCA91125 United States Department of Astronomy California Institute of Technology PasadenaCA91125 United States Anton Pannekoek Institute for Astronomy University of Amsterdam Amsterdam1090 GE Netherlands Department of Astronomy University of Washington SeattleWA98195 United States Department of Physics Pacific Lutheran University TacomaWA98447 United States Department of Computer Science and Engineering University of Minnesota MinneapolisMN55455 United States Department of Electrical Engineering Indian Institute of Technology Gandhinagar Gujarat 382055 India Department of Astronomy University of California BerkeleyCA94720 United States Lawrence Berkeley National Laboratory 1 Cyclotron Road MS 50B-4206 BerkeleyCA94720 United States Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA15213 United States Computer Vision Imaging and Graphics Lab Indian Institute of Technology Gujarat Gandhinagar382055 India School of Statistics University of Minnesota MinneapolisMN55455 United States Division of Engineering and Applied Science California Institute of Technology PasadenaCA91125 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91125 United States IPAC California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States Caltech Optical Observatories California Institute of Technology PasadenaCA91125 United States

The classification of variable objects provides insight into a wide variety of astrophysics ranging from stellar interiors to galactic nuclei. The Zwicky Transient Facility (ZTF) provides time-series observations that record the variability of more than a billion sources. The scale of these data necessitates automated approaches to make a thorough analysis. Building on previous work, this paper reports the results of the ZTF Source Classification Project (SCoPe), which trains neural network and XGBoost machine-learning (ML) algorithms to perform dichotomous classification of variable ZTF sources using a manually constructed training set containing 170,632 light curves. We find that several classifiers achieve high precision and recall scores, suggesting the reliability of their predictions for 209,991,147 light curves across 77 ZTF fields. We also identify the most important features for XGB classification and compare the performance of the two ML algorithms, finding a pattern of higher precision among XGB classifiers. The resulting classification catalog is available to the public, and the software developed for SCoPe is open-source and adaptable to future time-domain surveys. Copyright © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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NTIRE 2023 Quality Assessment of Video Enhancement Challenge

arXiv

引用

arXiv 2023年

作者： Liu, Xiaohong Min, Xiongkuo Sun, Wei Zhang, Yulun Zhang, Kai Timofte, Radu Zhai, Guangtao Gao, Yixuan Cao, Yuqin Kou, Tengchuan Dong, Yunlong Jia, Ziheng Li, Yilin Wu, Wei Hu, Shuming Deng, Sibin Xiao, Pengxiang Chen, Ying Li, Kai Zhao, Kai Yuan, Kun Sun, Ming Cong, Heng Wang, Hao Fu, Lingzhi Zhang, Yusheng Zhang, Rongyu Shi, Hang Xu, Qihang Xiao, Longan Ma, Zhiliang Agarla, Mirko Celona, Luigi Rota, Claudio Schettini, Raimondo Huang, Zhiwei Li, Yanan Wang, Xiaotao Lei, Lei Liu, Hongye Hong, Wei Chuang, Ironhead Lin, Allen Guan, Drake Chen, Iris Lou, Kae Huang, Willy Tasi, Yachun Kao, Yvonne Fan, Haotian Kong, Fangyuan Zhou, Shiqi Liu, Hao Lai, Yu Chen, Shanshan Wang, Wenqi Wu, Haoning Chen, Chaofeng Zhu, Chunzheng Guo, Zekun Zhao, Shiling Yin, Haibing Wang, Hongkui Meftah, Hanene Brachemi Fezza, Sid Ahmed Hamidouche, Wassim Déforges, Olivier Shi, Tengfei Mansouri, Azadeh Motamednia, Hossein Bakhtiari, Amir Hossein Aznaveh, Ahmad Mahmoudi Shanghai Jiao Tong University China ETH Zürich Switzerland University of Würzburg Germany Department of Tao Technology Alibaba Group China Kuaishou Technology China Interactive Entertainment Group of Netease Inc Guangzhou China Transsion China Vrobotit Lab Beijing University of Posts and Telecommunications China Hefei University of Technology China Department of Informatics Systems and Communication University of Milano - Bicocca Italy Xiaomi Inc. China China Ji Liang University China FreeTech China Advanced Research Center KKCompany Taiwan ByteDance China MGTV China Fuzhou University China Shopee Information Technology Co. Ltd. China S-Lab Nanyang Technological University Singapore Hunan University China Hangzhou Dianzi University China INSA Rennes CNRS IETR - UMR 6164 Rennes France National Higher School of Telecommunications and ICT Oran Algeria BUAA China Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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A Filtering Approach Based on MMAE for a SINS/CNS Integrated Navigation System

引用

IEEE/CAA Journal of Automatica Sinica 2018年第6期5卷 1113-1120页

作者： Fangfang Zhao Cuiqiao Chen Wei He Shuzhi Sam Ge School of Computer Science and Engineering and Center for RoboticsUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Automation and Electrical Engineering University of Science and Technology of BeijingBeijing 100083China Social Robotics Laboratory Interactive Digital Media Instituteand Department of Electrical and Computer EngineeringNational University of SingaporeSingapore 117576Singapore Singapore 117576 Singapore

This paper explores multiple model adaptive estimation(MMAE) method, and with it, proposes a novel filtering algorithm. The proposed algorithm is an improved Kalman filter— multiple model adaptive estimation unscented Kalman filter(MMAE-UKF) rather than conventional Kalman filter methods,like the extended Kalman filter(EKF) and the unscented Kalman filter(UKF). UKF is used as a subfilter to obtain the system state estimate in the MMAE method. Single model filter has poor adaptability with uncertain or unknown system parameters,which the improved filtering method can overcome. Meanwhile,this algorithm is used for integrated navigation system of strapdown inertial navigation system(SINS) and celestial navigation system(CNS) by a ballistic missile's motion. The simulation results indicate that the proposed filtering algorithm has better navigation precision, can achieve optimal estimation of system state, and can be more flexible at the cost of increased computational burden.

关键词： Celestial navigation integrated navigation multiple model adaptive estimation unscented Kalman filter （MMAEUKF） strap-down inertial navigation

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Graph-based parallel large scale structure from motion

arXiv

引用

arXiv 2019年

作者： Chen, Yu Shen, Shuhan Chen, Yisong Wang, Guoping Graphics and Interactive Lab Department of Computer Science and Technology School of Electronic Engineering and Computer Science Peking University National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

While Structure from Motion (SfM) achieves great success in 3D reconstruction, it still meets challenges on large scale scenes. In this work, large scale SfM is deemed as a graph problem, and we tackle it in a divide-and-conquer manner. Firstly, the images clustering algorithm divides images into clusters with strong connectivity, leading to robust local reconstructions. Then followed with an image expansion step, the connection and completeness of scenes are enhanced by expanding along with a maximum spanning tree. After local reconstructions, we construct a minimum spanning tree (MST) to find accurate similarity transformations. Then the MST is transformed into a Minimum Height Tree (MHT) to find a proper anchor node and is further utilized to prevent error accumulation. When evaluated on different kinds of datasets, our approach shows superiority over the state-of-the-art in accuracy and efficiency. Our algorithm is open-sourced in https://***/AIBluefisher/GraphSfM. Copyright © 2019, The Authors. All rights reserved.

关键词： Clustering algorithms

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Utilizing mobile nodes for congestion control in wireless sensor networks

arXiv

引用

arXiv 2019年

作者： Nicolaou, Antonia Temene, Natalie Sergiou, Charalampos Georgiou, Chryssis Vassiliou, Vasos Department of Computer Science University of Cyprus Nicosia Cyprus RISE Research Center on Interactive Media Smart Systems and Emerging Technologies Nicosia Cyprus

Congestion control and avoidance in Wireless Sensor Networks (WSNs) is a subject that has attracted a lot of research attention in the last decade. Besides rate and resource control, the utilization of mobile nodes has also been suggested as a way to control congestion. In this work, we present a Mobile Congestion Control (MobileCC) algorithm with two variations, to assist existing congestion control algorithms in facing congestion in WSNs. The first variation employs mobile nodes that create locally-significant alternative paths leading to the sink. The second variation employs mobile nodes that create completely individual (disjoint) paths to the sink. Simulation results show that both variations can significantly contribute to the alleviation of congestion in WSNs. Copyright © 2019, The Authors. All rights reserved.

关键词： Sensor nodes

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Correction to “The Open Catalyst 2020 (OC20) Dataset and Community Challenges”

引用

ACS Catalysis 2021年第21期11卷 13062-13065页

作者： Lowik Chanussot Abhishek Das Siddharth Goyal Thibaut Lavril Muhammed Shuaibi Morgane Riviere Kevin Tran Javier Heras-Domingo Caleb Ho Weihua Hu Aini Palizhati Anuroop Sriram Brandon Wood Junwoong Yoon Devi Parikh C. Lawrence Zitnick Zachary Ulissi Facebook AI Research (FAIR) 1 Hacker Way Menlo Park California 94025 United States Department of Chemical Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Computer Science Department Stanford University Stanford California 94305 United States National Energy Research Scientific Computing Center (NERSC) Lawrence Berkeley National Laboratory Berkeley California 94720 United States School of Interactive Computing Georgia Tech Atlanta Georgia 30332 United States Scott Institute for Energy Innovation Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States

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CIRCA: comprehensible online system in support of chest X-rays-based COVID-19 diagnosis

arXiv

引用

arXiv 2022年

作者： Prazuch, Wojciech Suwalska, Aleksandra Socha, Marek Tobiasz, Joanna Foszner, Pawel Jaroszewicz, Jerzy Gruszczynska, Katarzyna Sliwinska, Magdalena Walecki, Jerzy Popiela, Tadeusz Przybylski, Grzegorz Cieszanowski, Andrzej Nowak, Mateusz Pawlowska, Malgorzata Flisiak, Robert Simon, Krzysztof Zapolska, Gabriela Gizycka, Barbara Szurowska, Edyta Marczyk, Michal Polanska, Joanna Department of Data Science and Engineering Silesian University of Technology Gliwice Poland Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Gliwice Poland Department of Infectious Diseases and Hepatology Medical University of Silesia Katowice Poland Department of Diagnostic Imaging Voivodship Specialist Hospital Wroclaw Poland Department of Diagnostic Radiology Central Clinical Hospital of The Ministry of Internal Affairs and Administration Warsaw Poland Department of Radiology Jagiellonian University Medical College Krakow Poland Department of Lung Diseases Cancer and Tuberculosis Kujawsko-Pomorskie Pulmonology Center Bydgoszcz Poland Department of Imaging Diagnostics National Institute of Oncology Warsaw Poland Department of Radiology Silesian Hospital Cieszyn Poland Department of Infectious Diseases and Hepatology Collegium Medicum in Bydgoszcz Nicolaus Copernicus University Torun Poland Department of Infectious Diseases and Hepatology Medical University of Bialystok Bialystok Poland Department of Infectious Diseases and Hepatology Wroclaw Medical University Wroclaw Poland Department of Radiology Czerniakowski Hospital Warsaw Poland PDepartment of Imaging Diagnostics MEGREZ Hospital Tychy Poland 2nd Department of Radiology Medical University of Gdansk Poland Yale Cancer Center Yale School of Medicine New HavenCT United States

Background: Due to the large accumulation of patients requiring hospitalization, the COVID-19 pandemic disease caused a high overload of health systems, even in developed countries. Deep learning techniques based on medical imaging data can help in the faster detection of COVID-19 cases and monitoring of disease progression. However, regardless of the numerous proposed solutions using lung X-rays, none of them is a product that can be used in the clinic. Methods: Five different datasets (clinical POLCOVID and AIforCOVID databases, a public collection of images called COVIDx set, pneumonia patients from NIH, and artificially generated data) were used to construct a representative dataset of 23 799 CXRs for model training, 1 050 images as a hold-out test set and 44 247 CXRs as independent test set (BIMCV database). A U-Net-based model was developed to identify a clinically relevant region of the CXR that contains the lungs and each class (normal, pneumonia, and COVID-19) was divided into 3 subtypes using a 2D Gaussian mixture model. A decision tree was used to aggregate predictions from the InceptionV3 convolutional network based on processed CXRs and a dense 7-layer neural network on radiomic features determined independently for the upper, middle, and lower lung fragments. Results: The lung segmentation model gave the Sorensen-Dice coefficient of 94.86% in the validation dataset, and 93.36% in the testing dataset. In 5-fold cross-validation, the accuracy for all classes ranged from 91% to 93%, keeping slightly higher specificity than sensitivity. Also, NPV was higher than PPV for each class to guarantee a low rate of false negative cases. In the hold-out test set, the balanced accuracy ranged between 68% and 100%. The highest performance was obtained for the subtypes N1, P1, and C1, while images assigned to subtypes N3, P3, or C3 have been wrongly predicted among themselves. A similar performance was obtained on the independent dataset for normal and COVID-19 class

关键词： COVID-19

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