Extraction of causal time-domain network parameters from their band-limited frequency-domain counterparts using rational functions
Bibliographic record
Abstract
Network parameters of a lumped element device are usually given in a limited frequency band of interest, or in an operation frequency range. To include them in time-domain simulations, they need to be converted to their causal time-domain correspondences. However, direct conversion with a simple technique such as the Fourier transform often leads to noncausal time-domain network parameters. Even when causal parameters are found, convolutions with them in the time domain are usually too computationally inefficient to carry out for a simulation with a large number of time steps. In this paper, a technique that extracts the causal time-domain parameters using rational function approximations is proposed. It results in time-domain parameters which are not only causal but also of exponential form in time. Consequently, the convolutions with them can be performed in a recursive manner. Numerical examples are provided to verify the effectiveness.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".