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arXiv 2019年

作者： Altenhofen, Christian Attene, Marco Barrowclough, Oliver Chiumenti, Michele Livesu, Marco Marini, Federico Martinelli, Massimiliano Skytt, Vibeke Tamellini, Lorenzo Fraunhofer Institute for Computer Graphics Research IGD Interactive Engineering Technologies Darmstadt Germany Interactive Graphics Systems Group Technische Universität Darmstadt Darmstadt Germany Consiglio Nazionale Delle Ricerche Genova Italy SINTEF Digital Mathematics and Cybernetics Oslo Norway Universidad Politécnica de Catalunã Barcelona Spain Consiglio Nazionale Delle Ricerche Pavia Italy

In the industrial practice, additive manufacturing processes are often followed by post-processing operations such as subtractive machining, milling, etc. to achieve the desired surface quality and dimensional accuracy. Hence, a given part must be 3D printed with extra material to enable such finishing phase. This combined additive/subtractive technique can be optimized to reduce manufacturing costs by saving printing time and reducing material and energy usage. In this work, a numerical methodology based on parametric shape optimization is proposed for optimizing the thickness of the extra material, allowing for minimal machining operations while ensuring the finishing requirements. Moreover, the proposed approach is complemented by a novel algorithm for generating inner structures leading to reduced distortion and improved weight reduction. The computational effort induced by classical constrained optimization methods is alleviated by replacing both the objective and constraint functions by their sparse-grid surrogates. Numerical results showcase the effectiveness of the proposed approach. Copyright © 2019, The Authors. All rights reserved.

关键词： 3D printers

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Authenticating Users Through Fine-Grained Channel Information

Authenticating Users Through Fine-Grained Channel Informatio...

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作者： Liu, Hongbo Wang, Yan Liu, Jian Yang, Jie Chen, Yingying Poor, H. Vincent Department of Computer Information and Graphics Technology Indiana University-Purdue University Indianapolis IndianapolisIN46222 United States Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States Department of Computer Science Florida State University TallahasseeFL32306 United States Department of Electrical Engineering Princeton University PrincetonNJ08544 United States

User authentication is the critical first step in detecting identity-based attacks and preventing subsequent malicious attacks. However, the increasingly dynamic mobile environments make it harder to always apply cryptographic-based methods for user authentication due to their infrastructural and key management overhead. Exploiting non-cryptographic based techniques grounded on physical layer properties to perform user authentication appears promising. In this work, the use of channel state information (CSI), which is available from off-the-shelf WiFi devices, to perform fine-grained user authentication is explored. Particularly, a user-authentication framework that can work with both stationary and mobile users is proposed. When the user is stationary, the proposed framework builds a user profile for user authentication that is resilient to the presence of a spoofer. The proposed machine learning based user-authentication techniques can distinguish between two users even when they possess similar signal fingerprints and detect the existence of a spoofer. When the user is mobile, it is proposed to detect the presence of a spoofer by examining the temporal correlation of CSI measurements. Both office building and apartment environments show that the proposed framework can filter out signal outliers and achieve higher authentication accuracy compared with existing approaches using received signal strength (RSS). © 2017 IEEE.

关键词： Channel state information

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A new approach for motion estimation and correction of thermographic images in brain surgery 16

A new approach for motion estimation and correction of therm...

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16th International Workshop on Cellular Nanoscale Networks and Their Applications, CNNA 2018

作者： Moshaei-Nezhad, Yahya Müller, Jens Tetzlaff, Ronald Hoffmann, Nico Institute of Circuits and Systems Faculty of Electronic and Computer Engineering Technische Universität Dresden Dresden01062 Germany Clinical Sensoring and Monitoring Anesthesiology and Intensive Care Medicine Faculty of Medicine Carl Gustav Carus Technische Universität Dresden Dresden01307 Germany Institute of Computer Graphics and Visualization Faculty of Computer Science Technische Universität Dresden Dresden01062 Germany

ISBN: (纸本)9783800747665

Motion estimation and correction implementations are important components of medical image analysis. We present a new approach for motion estimation and correction of thermographic images in brain surgery. In a pre-processing step, the Phase Correlation (PC) method is performed in order to detect large displacements of objects in two successive images. Additionally, due to noise in thermographic images, a Cellular Nonlinear Network (CNN) based image enhancement method is applied. Then, in the following processing step, the Optical Flow (OF) method is employed to compensate local motion artifacts. The proposed algorithm is evaluated during an offline analysis of the recorded dataset of brain surgeries and the performance evaluation between different algorithms is made based on the determination of the Normalized Cross-Correlation (NCC). The results clearly indicate that the proposed algorithm is able to reduce breathing motion artifacts effectively as well as the NCC evaluation show better results in comparison to other mentioned algorithms. © VDE VERLAG GMBH.

关键词： Surgery

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Comparative cellular analysis of motor cortex in human, marmoset and mouse (vol 598, pg 111, 2021)

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NATURE 2022年第7904期604卷 E8-E8页

作者： Bakken, Trygve E. Jorstad, Nikolas L. Hu, Qiwen Lake, Blue B. Tian, Wei Kalmbach, Brian E. Crow, Megan Hodge, Rebecca D. Krienen, Fenna M. Sorensen, Staci A. Eggermont, Jeroen Yao, Zizhen Aevermann, Brian D. Aldridge, Andrew I. Bartlett, Anna Bertagnolli, Darren Casper, Tamara Castanon, Rosa G. Crichton, Kirsten Daigle, Tanya L. Dalley, Rachel Dee, Nick Dembrow, Nikolai Diep, Dinh Ding, Song-Lin Dong, Weixiu Fang, Rongxin Fischer, Stephan Goldman, Melissa Goldy, Jeff Graybuck, Lucas T. Herb, Brian R. Hou, Xiaomeng Kancherla, Jayaram Kroll, Matthew Lathia, Kanan van Lew, Baldur Li, Yang Eric Liu, Christine S. Liu, Hanqing Lucero, Jacinta D. Mahurkar, Anup McMillen, Delissa Miller, Jeremy A. Moussa, Marmar Nery, Joseph R. Nicovich, Philip R. Niu, Sheng-Yong Orvis, Joshua Osteen, Julia K. Owen, Scott Palmer, Carter R. Pham, Thanh Plongthongkum, Nongluk Poirion, Olivier Reed, Nora M. Rimorin, Christine Rivkin, Angeline Romanow, William J. Sedeno-Cortes, Adriana E. Siletti, Kimberly Somasundaram, Saroja Sulc, Josef Tieu, Michael Torkelson, Amy Tung, Herman Wang, Xinxin Xie, Fangming Yanny, Anna Marie Zhang, Renee Ament, Seth A. Behrens, M. Margarita Bravo, Hector Corrada Chun, Jerold Dobin, Alexander Gillis, Jesse Hertzano, Ronna Hof, Patrick R. Hollt, Thomas Horwitz, Gregory D. Keene, C. Dirk Kharchenko, Peter V. Ko, Andrew L. Lelieveldt, Boudewijn P. Luo, Chongyuan Mukamel, Eran A. Pinto-Duarte, Antonio Preiss, Sebastian Regev, Aviv Ren, Bing Scheuermann, Richard H. Smith, Kimberly Spain, William J. White, Owen R. Koch, Christof Hawrylycz, Michael Tasic, Bosiljka Macosko, Evan Z. McCarroll, Steven A. Ting, Jonathan T. Zeng, Hongkui Zhang, Kun Feng, Guoping Ecker, Joseph R. Linnarsson, Sten Lein, Ed S. Allen Institute for Brain Science Seattle WA USA Department of Physiology and Biophysics University of Washington Seattle WA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Bioengineering University of California San Diego La Jolla CA USA The Salk Institute for Biological Studies La Jolla CA USA Epilepsy Center of Excellence Department of Veterans Affairs Medical Center Seattle WA USA Stanley Institute for Cognitive Genomics Cold Spring Harbor Laboratory Cold Spring Harbor NY USA Department of Genetics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA LKEB Department of Radiology Leiden University Medical Center Leiden The Netherlands Pattern Recognition and Bioinformatics group Delft University of Technology Delft The Netherlands J. Craig Venter Institute La Jolla CA USA Department of Pathology University of California San Diego CA USA Division of Vaccine Discovery La Jolla Institute for Immunology La Jolla CA USA Genomic Analysis Laboratory The Salk Institute for Biological Studies La Jolla CA USA Computer Science and Engineering Program University of California San Diego La Jolla CA USA Howard Hughes Medical Institute The Salk Institute for Biological Studies La Jolla CA USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA Institute for Genomes Sciences University of Maryland School of Medicine Baltimore MD USA Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Ludwig Institute for Cancer Research La Jolla CA USA Department of Computer Science University of Maryland College Park College Park MD USA Sanford Burnham Prebys Medical Discovery Institute La Jolla CA USA Biomedical Sciences Program School of Medicine University of California San Diego La Jolla CA USA University of Connecticut Storrs CT USA Department

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VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images

arXiv

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arXiv 2020年

作者： Sekuboyina, Anjany Kumar Husseini, Malek E. Bayat, Amirhossein Löffler, Maximilian Liebl, Hans Li, Hongwei Tetteh, Giles Kukačka, Jan Payer, Christian Štern, Darko Urschler, Martin Chen, Maodong Cheng, Dalong Lessmann, Nikolas Hu, Yujin Wang, Tianfu Yang, Dong Xu, Daguang Ambellan, Felix Amiranashvili, Tamaz Ehlke, Moritz Lamecker, Hans Lehnert, Sebastian Lirio, Marilia de Olaguer, Nicolás Pérez Ramm, Heiko Sahu, Manish Tack, Alexander Zachow, Stefan Jiang, Tao Ma, Xinjun Angerman, Christoph Wang, Xin Brown, Kevin Kirszenberg, Alexandre Puybareau, Élodie Chen, Di Bai, Yiwei Rapazzo, Brandon H. Yeah, Timyoas Zhang, Amber Xu, Shangliang Hou, Feng He, Zhiqiang Zeng, Chan Xiangshang, Zheng Liming, Xu Netherton, Tucker J. Mumme, Raymond P. Court, Laurence E. Huang, Zixun He, Chenhang Wang, Li-Wen Ling, Sai Ho Huynh, Lê Duy Boutry, Nicolas Jakubicek, Roman Chmelik, Jiri Mulay, Supriti Sivaprakasam, Mohanasankar Paetzold, Johannes C. Shit, Suprosanna Ezhov, Ivan Wiestler, Benedikt Glocker, Ben Valentinitsch, Alexander Rempfler, Markus Menze, Björn H. Kirschke, Jan S. Department of Informatics Technical University of Munich Germany Munich School of BioEngineering Technical University of Munich Germany Department of Neuroradiology Klinikum Rechts der Isar Germany Department for Quantitative Biomedicine University of Zurich Switzerland Friedrich Miescher Institute for Biomedical Engineering Switzerland Institute of Biological and Medical Imaging Helmholtz Zentrum München Germany Department of Computing Imperial College London United Kingdom Institute of Computer Graphics and Vision Graz University of Technology Austria Gottfried Schatz Research Center: Biophysics Medical University of Graz Austria School of Computer Science The University of Auckland New Zealand Computer Vision Group iFLYTEK Research South China China Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen Netherlands Shenzhen Research Institute of Big Data China School of Biomedical Engineering Health Science Center Shenzhen University China NVIDIA Corporation United States Zuse Institute Berlin Germany 1000shapes GmbH Berlin Germany Damo Academy Alibaba Group China Department of Mathematics University of Innsbruck Austria Department of Electronic Engineering Fudan University China New York University United States France Deep Reasoning AI Inc United States Technical University of Munich Germany East China Normal University China Chinese Academy of Sciences China Lenovo Group China Ping An Technologies China College of Computer Science and Technology Zhejiang University China Real Doctor AI Research Centre Zhejiang University China The University of Texas MD Anderson Cancer Center United States Department of Electronic and Information Engineering The Hong Kong Polytechnic University China Department of Computing The Hong Kong Polytechnic University China The School of Biomedical Engineering University of Technology Sydney Australia Department of Biomedical Engineering Brno University of Technology Czech Republic India

Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://***/nqjyw/, https://***/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://***/anjany/verse. © 2020, CC BY-NC-ND.

关键词： computerized tomography

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SSBC 2020: Sclera Segmentation Benchmarking Competition in the Mobile Environment

SSBC 2020: Sclera Segmentation Benchmarking Competition in t...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： M. Vitek A. Das Y. Pourcenoux A. Missler C. Paumier S. Das I. De Ghosh D. R. Lucio L. A. Zanlorensi D. Menotti F. Boutros N. Damer J. H. Grebe A. Kuijper J. Hu Y. He C. Wang H. Liu Y. Wang Z. Sun D. Osorio-Roig C. Rathgeb C. Busch J. Tapia A. Valenzuela G. Zampoukis L. Tsochatzidis I. Pratikakis S. Nathan R. Suganya V. Mehta A. Dhall K. Raja G. Gupta J. N. Khiarak M. Akbari-Shahper F. Jaryani M. Asgari-Chenaghlu R. Vyas S. Dakshit P. Peer U. Pal V. Štruc University of Ljubljana (UL SI) Indian Statistical Institute (lSI IN) Grenoble Institute of Technology (INP FR) Ècole Nationale Supérieure de 1’Èlectronique et de ses Applications (ENSEA FR) University of Engineering and Management (UEM IN) Barrackpore Rastraguru Surendranath College (BRSNC IN) Federal University of Paraná (UFPR BR) Technische Universität Darmstadt (TUD DE) Fraunhofer Institute for Computer Graphics Research (IGD DE) Chinese Academy of Sciences (CAS CN) Beijing University of Civil Engineering and Architecture (BUCEA CN) Hochschule Darmstadt (HDA DE) Universidad de Santiago (USACH CL) TOC Biometrics (CL) Democritus University of Thrace (DUTH GR) Couger Inc. (JP) Thiagarajar College of Engineering (TCE IN) Indian Institute of Technology Ropar (IIT-RPR IN) Monash University (MU AU) Norwegian University of Science and Technology (NTNU NO) Warsaw University of Technology (WUT PL) Tabriz University (TU IR) Arak University (AU IR) Bennet University (BU IN) Calcutta Institute of Engineering and Management Maulana Abul Kalam University of Technology (MAKAUT IN) University of Texas at Dallas (UTD US

ISBN: (数字)9781728191867

ISBN: (纸本)9781728191874

The paper presents a summary of the 2020 Sclera Segmentation Benchmarking Competition (SSBC), the 7th in the series of group benchmarking efforts centred around the problem of sclera segmentation. Different from previous editions, the goal of SSBC 2020 was to evaluate the performance of sclera-segmentation models on images captured with mobile devices. The competition was used as a platform to assess the sensitivity of existing models to i) differences in mobile devices used for image capture and ii) changes in the ambient acquisition conditions. 26 research groups registered for SSBC 2020, out of which 13 took part in the final round and submitted a total of 16 segmentation models for scoring. These included a wide variety of deep-learning solutions as well as one approach based on standard image processing techniques. Experiments were conducted with three recent datasets. Most of the segmentation models achieved relatively consistent performance across images captured with different mobile devices (with slight differences across devices), but struggled most with low-quality images captured in challenging ambient conditions, i.e., in an indoor environment and with poor lighting.

关键词： Image segmentation Mobile handsets Performance evaluation Benchmark testing Training Lighting Iris recognition

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NTU place4dr: A Detailed-Routing-Driven Placer for Mixed-Size Circuit Designs with Technology and Region Constraints

NTU place4dr: A Detailed-Routing-Driven Placer for Mixed-Siz...

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作者： Huang, Chau-Chin Lee, Hsin-Ying Lin, Bo-Qiao Yang, Sheng-Wei Chang, Chin-Hao Chen, Szu-To Chang, Yao-Wen Chen, Tung-Chieh Bustany, Ismail Department of Electrical Engineering Graduate Institute of Electronics Engineering National Taiwan University Taipei106 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei106 Taiwan Maxeda Technologies Inc. Hsinchu300 Taiwan Place and Route Division Mentor Graphics FremontCA94538 United States

A placer without considering modern technology and region constraints could generate solutions with irresolvable detailed-routing (DR) violations or even illegal solutions. This paper presents a high-quality placement algorithm to satisfy technology and region constraints and optimize DR routability with five major techniques: 1) a clustering algorithm followed by two-round quadratic placement to obtain an initial placement satisfying region constraints;2) a novel density control technique to handle prefixed architectures and minimize global routing congestions;3) an analytical placement algorithm with new wirelength and density models to consider region constraints;4) a dynamic penalty increment strategy that reduces wirelength increments during global placement;and 5) a legalization algorithm that preserves the solution quality of global placement while satisfying technology and region constraints. Compared with the winning teams of the ISPD 2015 Blockage-Aware Detailed Routing-Driven Placement Contest and recent works, our placer achieves the best overall score and DR results. © 2017 IEEE.

关键词： Clustering algorithms

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Author Correction: A high-performance neuroprosthesis for speech decoding and avatar control

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Nature 2024年第8021期631卷 E13页

作者： Sean L Metzger Kaylo T Littlejohn Alexander B Silva David A Moses Margaret P Seaton Ran Wang Maximilian E Dougherty Jessie R Liu Peter Wu Michael A Berger Inga Zhuravleva Adelyn Tu-Chan Karunesh Ganguly Gopala K Anumanchipalli Edward F Chang Department of Neurological Surgery University of California San Francisco San Francisco CA USA. Weill Institute for Neuroscience University of California San Francisco San Francisco CA USA. University of California Berkeley-University of California San Francisco Graduate Program in Bioengineering Berkeley CA USA. Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA. Speech Graphics Ltd Edinburgh UK. Department of Neurology University of California San Francisco San Francisco CA USA. Department of Neurological Surgery University of California San Francisco San Francisco CA USA. edward.chang@ucsf.edu. Weill Institute for Neuroscience University of California San Francisco San Francisco CA USA. edward.chang@ucsf.edu. University of California Berkeley-University of California San Francisco Graduate Program in Bioengineering Berkeley CA USA. edward.chang@ucsf.edu.

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

arXiv

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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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TimeTubes: Visual Exploration of Observed Blazar Datasets

TimeTubes: Visual Exploration of Observed Blazar Datasets

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International Meeting on High-Dimensional Data-Driven Science, HD3 2017

作者： Fujishiro, Issei Sawada, Naoko Nakayama, Masanori Wu, Hsiang-Yun Watanabe, Kazuho Takahashi, Shigeo Uemura, Makoto Center for Information and Computer Science Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama223-8522 Japan Institute of Computer Graphics and Algorithms TU Wien Favoritenstr. 9 / 5. Stock ViennaA-1040 Austria Department of Computer Science and Engineering Toyohashi University of Technology 1-1 Hibarigaoka Tempaku-cho Toyohashi441-8580 Japan School of Computer Science and Engineering University of Aizu Aizu Wakamatsu Fukushima965-8580 Japan Astrophysical Science Center Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima739-8526 Japan

Blazars are attractive objects for astronomers to observe in order to burrow into the magnetic field in the relativistic jet. This paper presents TimeTubes as a novel visualization scheme that allows astronomers to interactively explore characteristic temporal variation patterns in observed blazar datasets. In the TimeTubes spatialization, the two Stokes parameters and their errors with a common timestamp are transformed into an ellipse. A series of such ellipses are aligned in parallel along the timeline to form a 3D volumetric tube. The resulting tube is then colorized by the observed intensities and colors of the blazar, and finally volume-rendered. A designated user interface is provided with visual exploration functions according to Shneiderman's Visual Information Seeking Mantra. In the latest version, an auxiliary mechanism, called visual data fusion, was incorporated to ameliorate data- and mapping-inherent uncertainties for more efficient and effective visual exploration. © Published under licence by IOP Publishing Ltd.

关键词： Data fusion

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