Parallel vector fitting of systems characterised by measured or simulated data
Bibliographic record
Abstract
During the past decade, technology in the electronics industry has advanced considerably. The integrated circuits we are using today are becoming more and more complex. As a result, modeling those complex systems has become a difficult task. The vector fitting method is a very efficient tool for building a model based on measured or simulated data. However, for large scale systems, the vector fitting method runs slowly or even fails to converge at the end. One of the solutions to the problem is the parallel vector fitting which was introduced a few years ago. Recently, the parallel computing and cloud computing have become more popular. It would be much more efficient if we can use the concept of parallel computing to do the vector fitting. Since each column in the admittance matrix Y is independent from each other. Calculations on one column will not affect the results of another column. Thus, we can do multiple column vector fittings at the same time. This concept leads to the idea of doing the vector fitting in a parallel way. During the algorithm, many columns are being vector fitted at the same time. There is one small model for each column. After all columns are done, an extra routine will be executed to combine all sub-models into one complete model. In this way, we can achieve a descent speedup factor which leads to less total computing time. The final model is verified so that it is as accurate as the one generated by the traditional vector fitting. In this thesis, detailed concepts will be presented. Methods will be explained step by step and examples will be tested and analyzed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".