On the Parameterized Complexity of Pooling Design
Bibliographic record
Abstract
Pooling design is a very helpful tool for reducing the number of tests in DNA library screening, which is a key process to obtain high-quality DNA libraries for studying gene functions. Three basic problems in pooling design are, given an m x n binary matrix and a positive integer d, to decide whether the matrix is d-separable (d-separable, or d-disjunct). The three problems are all known to be coNP-complete. Since in most applications, d is a small integer compared to n, it is interesting to investigate whether there are efficient algorithms solving the above problems when the value of d is small. In this article, we give a negative answer to the above question by studying the parameterized complexity of these three problems, with d as the parameter. We show that the parameterized versions of all the three problems are co-W[2]-complete. An immediate implication of our results is that, given an m x n binary matrix and a positive integer d, a deterministic algorithm with running time f(d) x (mn)(O(1)) (where f is an arbitrary computable function) to decide whether the matrix is d-separable (d-separable, or d-disjunct) should not be expected.
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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".