Efficient Computation of Throughput Values of Context-Free Languages
Bibliographic record
Abstract
Abstract. We present the first deterministic polynomial time algorithm that computes the throughput value of a given context-free language L. The language is given by a grammar G of size n, together with a weight function assigning a positive weight to each symbol. The weight of a word w ∈ L is defined as the sum of weights of its symbols (with multiplicities), and the mean weight is the weight of w divided by length of w. The throughput of L, denoted by throughput(L), is the smallest real number t, such that the mean value of each word of L is not smaller than t. Our approach, to compute throughput(L), consists of two phases. In the first one we convert the input grammar G to a grammar G ′ , generating a finite language L ′ , such that throughput(L) = throughput(L ′). In the next phase we find a word of the smallest mean weight in a finite language L ′. The size of G ′ is polynomially related to the size of G. The problem is of practical importance in system-performance analysis, especially in the domain of network packet processing, where one of the important parameters is the “guaranteed throughput ” of a system for on-line network packet processing. 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".