A Dimension Abstraction Approach to Vectorization in Matlab
Bibliographic record
Abstract
Matlab is a matrix-processing language that offers very efficient built-in operations for data organized in arrays. However Matlab operation is slow when the program accesses data through interpreted loops. Often during the development of a Matlab application writing loop-based code is more intuitive than crafting the data organization into arrays. Furthermore, many Matlab users do not command the linear algebra expertise necessary to write efficient code. Thus loop-based Matlab coding is a fairly common practice. This paper presents a tool that automatically converts loop-based Matlab code into equivalent array-based form and built-in Matlab constructs. Array-based code is produced by checking the input and output dimensions of equations within loops, and by transposing terms when necessary to generate correct code. This paper also describes an extensible loop pattern database that allows user-defined patterns to be discovered and replaced by more efficient Matlab routines that perform the same computation. The safe conversion of loop-based into more efficient array-based code is made possible by the introduction of a new abstract representation for dimensions
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".