New algorithms for the exact computation of the sign of algebraic expressions
Bibliographic record
Abstract
The paper considers the problem of exact computation of the sign of algebraic expressions of real numbers represented in floating point arithmetic. We describe a new method for the exact computation and suggest some variations to improve the efficiency. The input data for the algorithm is represented by normalized floating point numbers with fixed mantissa length (machine numbers). The algorithm computes the exact value of the sign of the sum of machine numbers and it can be applied to exactly compute the sign of almost any algebraic expression. We suggest several variations of the original Exact Sign of a Sum Algorithm (ESSA) to improve the performance of the algorithm and we test the algorithms on different data sets. This includes the implementation of floating point filters based on interval analysis, a special algorithm for performing multiple bit-wise transformations on the numbers in lists and the application of different rules to reduce the number of iterations of the algorithm. The theoretical upper bound on the complexity of ESSA is O(l/sup 2/), where l is the number of elements of the input. The expected average, experimental complexity of the suggested algorithms is proportional to the length of the input lists and it is close to l/2 in most cases. We perform a comparison analysis among the algorithms. The comparisons are based on the computational efficiency of the algorithms on both well posed and ill posed data sets. The algorithms are verified by the computer implementations.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".