Matjuice: a matlab to java script static compiler
Bibliographic record
Abstract
A large number of scientists, engineers, and researchers in fields as varied as physics, musicology, biology, and statistics use MATLAB daily as part of their work.These users appreciate the conciseness and expressiveness of the MATLAB language, the impressive number of powerful matrix operations and visualization functions, the easy-to-use IDE, and its interactive environment.At the same time, the web platform keeps growing and innovating.At the center of this evolution is the JavaScript language.Though it was initially used only for simple tasks in web pages such as form validation, JavaScript is today the driving technology behind extremely powerful and complex applications such as Google Maps, the diagram tool draw.io, and the presentation tool Prezi.One very desirable property of web applications is their universality; whether it's the smart phone in our pocket, the laptop on our desk, or the powerful workstation in our lab, all these devices have a modern web browser that can execute an application on the web.The advantage for end-users is that they can use their favorite tools from the device of their choice and wherever they are without fear of compatibility issues.The developers of these applications also benefit by being able to deploy and update applications multiple times per day at a low cost.MatJuice is a tool to connect MATLAB users to the web: it automatically translates MATLAB code into JavaScript.Scientists need not spend time manually converting their applications to JavaScript, nor become experts in web technologies to publish the fruit of their labor on the web.This thesis will present MatJuice, discuss the challenges of converting from one i dynamic language to another, how to handle the differences in semantics, and how to make the output code fast.8.2 Benchmark results (times in seconds) . . . . . . . . . . . . . . . . . . . .
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.052 | 0.052 |
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".