Fast Multibase Methods and Other Several Optimizations for Elliptic Curve Scalar Multiplication.
Bibliographic record
Abstract
method was introduced and shown to speed up the execution of the scalar multiplication with an efficient use of multiple bases to represent the scalar. In this work, we first optimize the previous method using fractional windows, and then introduce further improvements to achieve additional cost reductions. Moreover, we present new improvements in the point operation formulae. Specifically, we reduce further the cost of composite operations such as quintupling and septupling of a point, which are relevant for the speed up of multibase methods in general. Remarkably, our tests show that, in the case of standard elliptic curves, the refined mbNAF method can be as efficient as Window-w NAF using an optimal fractional window size. Thus, this is the first published method that does not require precomputations to achieve comparable efficiency to the standard window-based NAF method using precomputations. On other highly efficient curves as Jacobi quartics and Edwards curves, our tests show that the refined mbNAF currently attains the highest performance for both scenarios using precomputations and those without precomputations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".