Proceedings of the 2007 international workshop on Symbolic-numeric computation
Bibliographic record
Abstract
Algorithms that combine ideas from symbolic and numeric computation have been of increasing interest over the past decade. Not only do the usual algorithms of computer algebra break down when applied to inexact values, but the analytic setting itself allows many new questions to be asked. This, together with the growing demand for speed, accuracy and reliability in mathematical computing, has accelerated the process of blurring the distinction between two areas of research that were previously quite separate. The goal of the present workshop is to support this interaction and integration of symbolic and numeric computing. Earlier meetings in this series include the SNAP 96 workshop, held in Sophia Antipolis, France, and the SNC 2005 meeting, held in Xi'an, China. Following the tradition, Symbolic-Numeric Computation 2007 will be held July 25-27 in London, Canada. SNC 2007 is affiliated with the 2007 International Symposium on Symbolic and Algebraic Computation (ISSAC 2007). Co-located with this workshop is PASCO 2007, the 2007 International Workshop on Parallel Symbolic Computation. SNC and PASCO are held immediately prior to the ISSAC 2007 meeting, both in London at the University of Western Ontario. ISSAC 2007 takes place nearby in Waterloo, Canada. The call for papers solicited contributions from all areas of symbolic-numeric computing, including: hybrid symbolic-numeric algorithmsapproximate polynomial GCD and factorizationsymbolic-numeric methods for solving polynomial systemsresultants and structured matrices for symbolic-numeric computationdifferential equations for symbolic-numeric computationsymbolic-numeric methods for geometric computationsymbolic-numeric algorithms in algebraic geometrysymbolic-numeric algorithms for nonlinear optimizationnumeric computation of characteristic sets and Gröbner basesimplementation of symbolic-numeric algorithmsmodel construction by approximate algebraic algorithms (e.g. numerical sparse interpolation, the approximate Buchberger-Moeller algorithm)applications of symbolic-numeric computation.. In response, 38 submissions were received: 9 extended abstracts and 29 full papers. The program committee collected 113 referee reports --- on average three per submission. After careful considerations, 6 of the 9 extended abstracts and 19 of the 29 papers were accepted. In addition, we are grateful that the majority of the invited speakers contributed full papers as well. One of our invited speakers, Anthony Kennedy, is joint with PASCO and we also share our after-dinner speaker, Keith Geddes, at the joint SNC-PASCO banquet. and many anonymous reviewers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.085 | 0.033 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".