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Record W4245796625 · doi:10.1111/cgf.13235

Front Matter

2017· paratext· en· W4245796625 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueComputer Graphics Forum · 2017
Typeparatext
Languageen
FieldComputer Science
TopicFace recognition and analysis
Canadian institutionsnot available
FundersUniversity of Massachusetts AmherstUniversité de LyonTechnische Universität BerlinUniversität des SaarlandesZhejiang UniversityTechnische Universität WienTechnische Universiteit DelftUniversity College LondonInstitute of Science and Technology AustriaUniversity of California, IrvineUniversity of California, Santa BarbaraMcGill UniversityMicrosoft Research AsiaDartmouth CollegeČeské Vysoké Učení Technické v PrazePurdue UniversityNational Taiwan Normal UniversityUniverzita Karlova v PrazeNvidiaUniversidad de ZaragozaMicrosoft Research
KeywordsComputer scienceComputer graphics (images)Front (military)Geology

Abstract

fetched live from OpenAlex

Welcome to the 2017 Proceedings of the Eurographics Symposium on Rendering!This is the 28th edition of the conference, which is a premier venue for research in rendering and related areas.This year's conference is held in Helsinki, Finland on 19-21 June 2017, and co-located with the Workshop on Material Appearance Modeling.We look forward to welcoming researchers eager to meet and discuss the various areas and applications of rendering.As in earlier years, EGSR 2017 offers two submission tracks.The traditional "CGF track", with papers that are reviewed for publication in Computer Graphics Forum, is accompanied by an "Experimental Ideas and Implementation" (EI&I) track.The latter targets submissions with fresh ideas, algorithmic details, or best-practice solutions that might still require further validation, but that would be inspiring for the community.We received a total of 74 abstract submissions (53 in the CGF track and 21 in the EI&I track).After some withdrawals in the CGF track, we had a total of 41 full CGF paper submissions for review.The IPC accepted 16 full CGF papers and 12 EI&I papers for a total of 28 papers (two more than in 2016).In addition, we offered invitations to three CGF papers to be presented in our program.Thus, we will have a total of 31 presentations in an exciting and packed 2.5-day event.In addition to the paper talks, our program includes two great keynote talks given by Prof. Ren Ng from UC Berkeley and Prof. Kun Zhou from Zhejiang University.We are very excited to hear about their latest work and thank them for accepting our invitation to present at EGSR.We would like to thank both the authors for the high quality of the submitted papers as well as the IPC members for their great effort during this very tight multi-stage review process.We have kept the review process the same as in the previous years, with three IPC reviewers per paper submission.Some of the papers rejected to CGF track were given the opportunity to present in the EI&I track.We further thank Stefanie Behnke for her tremendous help in producing the EGSR proceedings, and for very quickly addressing a variety of unexpected issues that came up at different times throughout.We are very grateful to be able to count on her during the entire process.Additionally, we would like to thank Min Chen,

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.826
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0040.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.132

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.255
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it