Iterative methods for extracting causal time-domain parameters
Why this work is in the frame
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Bibliographic record
Abstract
Recent interest in time-domain modeling techniques has been largely motivated by the demands for simulating broad-band electronic systems and high-speed digital circuits. These techniques normally require strict causality of any parameters to be used with them. However, most of parameters, such as the S-parameters of a transistor, are given only in the frequency domain and over a limited frequency range of interest. Direct applications of regular transformation techniques to these band-limited frequency-domain parameters, such as inverse Fourier transform, often lead to noncausal time-domain correspondents. Therefore, schemes need to be carefully developed for extraction of the time-domain parameters that are causal while retaining the original frequency-domain information within the frequency range of interest. In this paper, two iterative methods are proposed for the causal extraction, and numerical examples are given to validate the effectiveness. The errors of the methods are found to be approximately 1%.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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