A Tool for Shared Analysis and Video Dissemination 18 ZEYU WANG, WEIQI SHI, KIRAZ AKOGLU, ELENI KOTOULA, YING YANG, and HOLLY RUSHMEIER, Yale University
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The Lumigraph
The Lumigraph The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Gortler, Steven J., Radek Grzeszczuk, Richard Szeliski, and Michael F. Cohen. 1996. The Lumigraph. Proceedings of the 23rd annual conference on computer graphics and interactive techniques (SIGGRAPH 1996), August 4-9, 1996, New Orleans, Louisiana, ed. SIGGRAPH, Holly Rushmeier, and Theresa Marie Rhyne, 43-54. Computer graphics annual conference series. New York, N.Y.: Association for Computing Machinery. Published Version http://dx.doi.org/10.1145/237170.237200 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:2634291 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA e umigra !"#$#% &' ()*"+#* ,-.#/ (*0#10203/ ,425-*. !0#+41/4 6425-#+ 7' 8)5#% 642*)1)9" ,#1#-*25 strat 1#*4#1 )9 2-;"3*#. #%$4*)%=#%" =-;1 -++)< - 31#* ") look around - 12#%# 9*)= "K#. ;)4%"1 4% 1;-2#' U%# 2-% -+1) !4; "5*)3>5 .499#*? :541 ;-;#* .412311#1 - %#< =#"5). 9)* 2-;"3*4%> "5# 2)=;+#"# -;? #%" $4#<1 )9 -% )@B#2" ") 2*#-"# "5# 4++314)% )9 - MG =).#+' 85#% -%. ;#-*-%2#)9 @)"5 1A%"5#"42 -%. *#-+ <)*+. )@B#2"1 -%. 12#%#1C *#;*#1? I4++4-=1 QVS -%. I#*%#* #" -+ QMWS 5-$# 4%$#1"4>-"#. 1=))"5 4%"#*? #%"4%> "541 4%9)*=-"4)%C -%. "5#% 314%> "541 *#;*#1#%"-"4)% ") *#%.#* ;)+-"4)% @#"<##% 4=->#1 @A =).#+4%> "5# =)"4)% )9 ;4K#+1 X4'#'C "5# 4=->#1 )9 "5# )@B#2" 9*)= %#< 2-=#*- ;)14"4)%1' D%+4/# "5# 15-;# optical !owY -1 )%# =)$#1 9*)= )%# 2-=#*- ;)14"4)% ") -%)"5#*' E% 2-;"3*# ;*)2#11 "*-.4"4)%-++A 31#. -
The Fourth Paradigm
ABOUT THE FOURTH PARADIGM This book presents the first broad look at the rapidly emerging field of data- THE FOUR intensive science, with the goal of influencing the worldwide scientific and com- puting research communities and inspiring the next generation of scientists. Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets. The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud- computing technologies. This collection of essays expands on the vision of pio- T neering computer scientist Jim Gray for a new, fourth paradigm of discovery based H PARADIGM on data-intensive science and offers insights into how it can be fully realized. “The impact of Jim Gray’s thinking is continuing to get people to think in a new way about how data and software are redefining what it means to do science.” —Bill GaTES “I often tell people working in eScience that they aren’t in this field because they are visionaries or super-intelligent—it’s because they care about science The and they are alive now. It is about technology changing the world, and science taking advantage of it, to do more and do better.” —RhyS FRANCIS, AUSTRALIAN eRESEARCH INFRASTRUCTURE COUNCIL F OURTH “One of the greatest challenges for 21st-century science is how we respond to this new era of data-intensive -
The Diversity of Data and Tasks in Event Analytics
The Diversity of Data and Tasks in Event Analytics Catherine Plaisant, Ben Shneiderman Abstract— The growing interest in event analytics has resulted in an array of tools and applications using visual analytics techniques. As we start to compare and contrast approaches, tools and applications it will be essential to develop a common language to describe the data characteristics and diverse tasks. We propose a characterisation of event data along 3 dimensions (temporal characteristics, attributes and scale) and propose 8 high-level user tasks. We look forward to refining the lists based on the feedback of workshop attendees. KEYWORDS having the same time stamp, medical data are often Temporal analysis, pattern analysis, task analysis, taxonomy, big recorded in batches after the fact. data, temporal visualization o The relevant time scale may vary (from milliseconds to years), and may be homogeneous or not. INTRODUCTION o Data may represent changes over time of a status The growing interest in event analytics (e.g. Aigner et al, 2011; indicator, e.g. changes of cancer stages, student status or Shneiderman and Plaisant, 2016) has resulted in an array of novel physical presence in various hospital services, or may tools and applications using visual analytics techniques. As represent a set of events or actions that are not exclusive researchers compare and contrast approaches it will be helpful to from one another, e.g. actions in a computer log or series develop a common language to describe the diverse tasks and data of symptoms and medical tests. characteristics analysts encounter. o Patterns may be very cyclical or not, and this may vary The methodology of design studies - which primarily focus on over time. -
F I N a L P R O G R a M 1998
1998 FINAL PROGRAM October 18 • October 23, 1998 Sheraton Imperial Hotel Research Triangle Park, NC THE INSTITUTE OF ELECTRICAL IEEE IEEE VISUALIZATION 1998 & ELECTRONICS ENGINEERS, INC. COMPUTER IEEE SOCIETY Sponsored by IEEE Computer Society Technical Committee on Computer Graphics In Cooperation with ACM/SIGGRAPH Sessions include Real-time Volume Rendering, Terrain Visualization, Flow Visualization, Surfaces & Level-of-Detail Techniques, Feature Detection & Visualization, Medical Visualization, Multi-Dimensional Visualization, Flow & Streamlines, Isosurface Extraction, Information Visualization, 3D Modeling & Visualization, Multi-Source Data Analysis Challenges, Interactive Visualization/VR/Animation, Terrain & Large Data Visualization, Isosurface & Volume Rendering, Simplification, Marine Data Visualization, Tensor/Flow, Key Problems & Thorny Issues, Image-based Techniques and Volume Analysis, Engineering & Design, Texturing and Rendering, Art & Visualization Get complete, up-to-date listings of program information from URL: http://www.erc.msstate.edu/vis98 http://davinci.informatik.uni-kl.de/Vis98 Volvis URL: http://www.erc.msstate.edu/volvis98 InfoVis URL: http://www.erc.msstate.edu/infovis98 or contact: Theresa-Marie Rhyne, Lockheed Martin/U.S. EPA Sci Vis Center, 919-541-0207, [email protected] Robert Moorhead, Mississippi State University, 601-325-2850, [email protected] Direct Vehicle Loading Access S Dock Salon Salon Sheraton Imperial VII VI Imperial Convention Center HOTEL & CONVENTION CENTER Convention Center Phones -
Quantitative Literacy to New Quantitative Literacies
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/319160466 Quantitative Literacy to New Quantitative Literacies Chapter · May 2017 CITATIONS READS 0 39 3 authors, including: Rohit Mehta James P. Howard California State University, Fresno Johns Hopkins University 32 PUBLICATIONS 42 CITATIONS 13 PUBLICATIONS 0 CITATIONS SEE PROFILE SEE PROFILE Some of the authors of this publication are also working on these related projects: MSU-Wipro STEM & Leadership View project I Wonder: Research To Practice View project All content following this page was uploaded by Rohit Mehta on 18 October 2017. The user has requested enhancement of the downloaded file. 5 Quantitative Literacy to New Quantitative Literacies Jeffrey Craig, Rohit Mehta; James P. Howard II Michigan State University; University of Maryland University College 5.1 Introduction Steen and colleagues [42] made their Case for Quantitative Literacy based on the premise that the 21st-century, pri- marily due to technology changes, is a significantly more quantitative environment than any previous time in history. The design team who wrote the case made rhetorical observations about the increasing prevalence of numbers in society in the United States; phrases like [a] world awash in numbers [42] precede nearly every piece of literature regarding quantitative literacy (or numeracy). The authors used this rhetoric to position people relative to the demands of social systems in the world. Specifically, numeracy has emerged as a partner to literacy because the social world is being integrated with numbers due to recent technological advances. Steen argued that as the printing press gave the power of letters to the masses, so the computer gives the power of number to ordinary citizens [40, p. -
COURSE NOTES 8 CASE STUDY: Scanning Michelangelo's Florentine Pieta`
SIGGRAPH 99 26th International Conference on Computer Graphics and Interactive Techniques COURSE NOTES 8 CASE STUDY: Scanning Michelangelo's Florentine Pieta` Sunday, August 9, 1999 Two Hour Tutorial ORGANIZER Holly Rushmeier IBM TJ Watson Research Center LECTURERS Fausto Bernardini Joshua Mittleman Holly Rushmeier IBM TJ Watson Research Center 1-1 ABSTRACT We describe a recent project to create a 3D digital model of Michelangelo's Florentine Pieta`. The emphasis is on the practical issues such as equipment selection and modi®cation, the planning of data acquisition, dealing with the constraints of the museum environment, overcoming problems encountered with "real" rather than idealized data, and presenting the model in a form that is suitable for the art historian who is the end user. Art historian Jack Wasserman, working with IBM, initiated a project to create a digital model of Michelangelo's Florentine Pieta` to assist in a scholarly study. In the course of the project, we encountered many practical problems related to the size and topology of the work, and with completing the project within constraints of time, budget and the access allowed by the museum. While we have and will continue to publish papers on the individual new methods we have developed in the in course of solving various problems, a typical technical paper or presentation does not allow for the discussion of many important practical issues. We expect this course to be of interest to practitioners interested in acquiring digital models for computer graphics applications, end users who are interested in understanding what quality can be expected from acquired models, and researchers interested in ®nding research opportunities in the "gaps" in current acquisition methods. -
Surfacing Visualization Mirages
Surfacing Visualization Mirages Andrew McNutt Gordon Kindlmann Michael Correll University of Chicago University of Chicago Tableau Research Chicago, IL Chicago, IL Seattle, WA [email protected] [email protected] [email protected] ABSTRACT In this paper, we present a conceptual model of these visual- Dirty data and deceptive design practices can undermine, in- ization mirages and show how users’ choices can cause errors vert, or invalidate the purported messages of charts and graphs. in all stages of the visual analytics (VA) process that can lead These failures can arise silently: a conclusion derived from to untrue or unwarranted conclusions from data. Using our a particular visualization may look plausible unless the an- model we observe a gap in automatic techniques for validating alyst looks closer and discovers an issue with the backing visualizations, specifically in the relationship between data data, visual specification, or their own assumptions. We term and chart specification. We address this gap by developing a such silent but significant failures visualization mirages. We theory of metamorphic testing for visualization which synthe- describe a conceptual model of mirages and show how they sizes prior work on metamorphic testing [92] and algebraic can be generated at every stage of the visual analytics process. visualization errors [54]. Through this combination we seek to We adapt a methodology from software testing, metamorphic alert viewers to situations where minor changes to the visual- testing, as a way of automatically surfacing potential mirages ization design or backing data have large (but illusory) effects at the visual encoding stage of analysis through modifications on the resulting visualization, or where potentially important to the underlying data and chart specification. -
Ways of Visualizing Data on Curves
Edinburgh Research Explorer Ways of Visualizing Curves Citation for published version: Bach, B, Ren, Q, Perin, C & Dragicevic, P 2018, Ways of Visualizing Curves. in Proceedings of the 5th Biennial Transdisciplinary Imaging Conference 2018. Edinburgh, UK, The Latent Image, Edinburgh, United Kingdom, 18/04/18. https://doi.org/10.6084/m9.figshare.6104705 Digital Object Identifier (DOI): 10.6084/m9.figshare.6104705 Link: Link to publication record in Edinburgh Research Explorer Document Version: Peer reviewed version Published In: Proceedings of the 5th Biennial Transdisciplinary Imaging Conference 2018 General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 24. Sep. 2021 Transimage 2018 Proceedings of the 5th Biennial Transdisciplinary Imaging Conference 2018 Ways of Visualizing Data on Curves Benjamin Bach [email protected] Charles Perin [email protected] University of Edinburgh, Edinburgh, UK City University, London, UK Qiuyuan Ren [email protected] Pierre Dragicevic [email protected] University of Edinburgh, Edinburgh, UK Inria, Saclay, France Visual variables used to encode data on the curve in the enrichment stage. -
IBM Research Report Reverse Engineering Methods for Digital
RC23991 (W0606-132) June 28, 2006 Computer Science IBM Research Report Reverse Engineering Methods for Digital Restoration Applications Ioana Boier-Martin IBM Research Division Thomas J. Watson Research Center P.O. Box 704 Yorktown Heights, NY 10598 Holly Rushmeier Yale University New Haven, CT Research Division Almaden - Austin - Beijing - Haifa - India - T. J. Watson - Tokyo - Zurich LIMITED DISTRIBUTION NOTICE: This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. I thas been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g ,. payment of royalties). Copies may be requested from IBM T. J. Watson Research Center , P. O. Box 218, Yorktown Heights, NY 10598 USA (email: [email protected]). Some reports are available on the internet at http://domino.watson.ibm.com/library/CyberDig.nsf/home . Reverse Engineering Methods for Digital Restoration Applications Ioana Boier-Martin Holly Rushmeier IBM T. J. Watson Research Center Yale University Hawthorne, New York, USA New Haven, Connecticut, USA [email protected] [email protected] Abstract In this paper we focus on the demands of restoring dig- ital objects for cultural heritage applications. However, the In this paper we discuss the challenges of processing methods we present are relevant to many other areas. and converting 3D scanned data to representations suit- This is a revised and extended version of our previous pa- able for interactive manipulation in the context of virtual per [12]. -
Juan Morales Data Scientist
Juan Morales Data Scientist Researcher in Visual Analytics and Human-Computer Interaction. Programming lover. Avid learner. Personal Information Name: Juan Morales del Olmo Birth Date: July 18th, 1985 Homepage: http://bit.ly/juanmorales Education 2009–2013 PhD on Advanced Computing for Science and Engineering, Universidad Politécnica de Madrid (UPM), Madrid, Graduated with honor, Cum laude. 2008–2009 Artificial Intelligence MSc, Universidad Politécnica de Madrid (UPM), Madrid, pending Final Project. 2003–2008 Computer Science, Universidad Politécnica de Madrid (UPM), Madrid, Grade. 90th percentile by marks Research Experience 2013–Present Postdoc Researcher, UPM, Madrid. Developing interactive tools that help neuroscientist make sense of their data. Designing the architecture of the Visualization Framework to be used in the Human Brain Project, in collaboration with 3 international teams. May–Nov 12 Visitor Researcher at HCIL, UMD, Maryland. Under the advise of Ben Shneiderman and Catherine Plaisant. Involved in EventFlow project, a tool for the analysis of millions of event sequences. Publications 9 Published Most of them in JCR Q1 Journals like Neuroinformatics, Journal of Neuroscience or papers Frontiers in Neuroanatomy. H-Index 3. Google Scholar Conferences 6 Internat. Most of them are top congresses like IEEE VIS, ACM CHI or ECML PKDD. Lecturing conferences in three of them. http://bit.ly/juanmorales T SkypeID: juanmoralesdelolmo • H +34 608 029 224 B [email protected] 1/2 Computer skills Python Pandas, Numpy, Scipy, Matplotlib, JavaScript D3, Lodash, React, Angular, Back- VTK, ITK, Qt, Django, Flask, ZMQ, bone, jQuery, jQuery UI Gevent, Celery R Shiny, ggplot2, Statspat C++ Qt, VTK, ITK, STD, ZMQ Sys Admin Linux, Docker, Nginx, Apache Development Emacs, Eclipse, Qt Creator, R Studio, tools Git, SVN, CMake Database SQL, MongoDB Reporting LATEX, Sweave Languages Spanish First Language English Full professional Academic Experience 2014, 2015 Data Visualization, University Master in Graphical Computation and Simulation, U-tad, Madrid. -
References Cited [1] Salman Ahmad, Alexis Battle, Zahan Malkani, and Sepander Kamvar
References Cited [1] Salman Ahmad, Alexis Battle, Zahan Malkani, and Sepander Kamvar. The jabberwocky program- ming environment for structured social computing. In ACM User Interface Software and Technology (UIST), pages 53–64, 2011. [2] Saleema Amershi, James Fogarty, Ashish Kapoor, and Desney Tan. Overview based example se- lection in end user interactive concept learning. In ACM User Interface Software and Technology (UIST), pages 247–256, 2009. [3] Saleema Amershi, James Fogarty, Ashish Kapoor, and Desney Tan. Examining multiple potential models in end-user interactive concept learning. In ACM Human Factors in Computing Systems (CHI), pages 1357–1360, 2010. [4] Saleema Amershi, James Fogarty, and Daniel Weld. Regroup: Interactive machine learning for on- demand group creation in social networks. In ACM Human Factors in Computing Systems (CHI), pages 21–30, 2012. [5] Saleema Amershi, Bongshin Lee, Ashish Kapoor, Ratul Mahajan, and Blaine Christian. Cuet: Human-guided fast and accurate network alarm triage. In ACM Human Factors in Computing Systems (CHI), pages 157–166, 2011. [6] Apache lucene. http://lucene.apache.org/. [7] Alan R Aronson and Franc¸ois-Michel Lang. An overview of metamap: historical perspective and recent advances. Journal of the American Medical Informatics Association, 17(3):229–236, 2010. [8] David Bamman, Jacob Eisenstein, and Tyler Schnoebelen. Gender in twitter: Styles, stances, and social networks. CoRR, abs/1210.4567, 2012. [9] Michele Banko and Eric Brill. Scaling to very very large corpora for natural language disambiguation. In Proceedings of the 39th Annual Meeting on Association for Computational Linguistics, ACL ’01, pages 26–33, 2001. [10] Lee Becker, George Erhart, David Skiba, and Valentine Matula. -
NIH-NSF Visualization Research Challenges Report
NIH-NSF Visualization Research Challenges Report Chris Johnson1, Robert Moorhead2, Tamara Munzner3, Hanspeter Pfister4, Penny Rheingans5, and Terry S. Yoo6 1 Scientific Computing and Imaging Institute School of Computing University of Utah Salt Lake City, UT 84112 [email protected] 2 Department of Electrical and Computer Engineering Mississippi State University Mississippi State, MS 39762 [email protected] 3 Department of Computer Science University of British Columbia 2366 Main Mall Vancouver, BC V6T 1Z4 Canada [email protected] 4 MERL - Mitsubishi Electric Research Laboratories 201 Broadway Cambridge, MA 02139 [email protected] 5 Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore, MD 21250 [email protected] 6 Office of High Performance Computing and Communications National Library of Medicine, U.S. National Institutes of Health Bethesda, MD 20894 [email protected] October 20, 2005 Contents 1 Executive Summary . 3 2 The Value of Visualization . 5 3 The Process of Visualization . 7 3.1 Moving Beyond Moore’s Law. 7 3.2 Determining Success . 9 3.3 Supporting Repositories and Open Standards . 10 3.4 Achieving Our Goals . 10 4 The Power of Visualization . 13 4.1 Transforming Health Care . 13 4.2 Transforming Science and Engineering . 16 4.3 Transforming Life . 19 5 Roadmap . 23 6 State of the Field . 25 6.1 Other Reports . 25 6.2 National Infrastructure . 25 6.2.1 Visualization Hardware . 26 6.2.2 Networking . 27 6.2.3 Visualization Software . 27 7 Workshop Participants 2 EXECUTIVE SUMMARY 1 In this report we describe some of the remarkable security, and competitiveness, support for research and achievements visualization enables and discuss the major development in this critical, multidisciplinary field has been obstacles blocking the discipline’s advancement.