AspectMatlab: an aspect-oriented scientific programming language
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.
Bibliographic record
Abstract
There has been relatively little work done in the compiler research community for incorporating aspect-oriented features in scientific and dynamic programming languages.MATLAB R is a dynamic scientific programming language that is commonly used by scientists because of its convenient and high-level syntax for arrays, the fact that type declarations are not required, and the availability of a rich set of application libraries.This thesis introduces a new aspect-oriented scientific language, AspectMatlab.AspectMatlab introduces key aspect-oriented features in a way that is both accessible to scientists and where the aspect-oriented features concentrate on array accesses and loops, the core computation elements in scientific programs.One of the main contributions of this thesis is to provide a compiler implementation of the AspectMatlab language.It is supported by a collection of scientific use cases, which demonstrate the potential of aspectorientation for scientific problems.Introducing aspects into a dynamic language such as MATLAB also provides some new challenges.In particular, it is difficult to statically determine precisely where patterns match, resulting in many dynamic checks in the woven code.The AspectMatlab compiler uses flow analyses to eliminate many of those dynamic checks.This thesis reports on the language design of AspectMatlab, the amc compiler implementation, and also provides an overview of the use cases that are specific to scientific programming.By providing clear extensions to an already popular language, AspectMatlab will make aspect-oriented programming accessible to a new group of programmers including scientists and engineers.i
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it