Packing of cutsets for a breach path detection problem
Bibliographic record
Abstract
The breach path detection reliability (BPDREL) problem is a core Wireless Sensor Networks (WSNs) surveillance problem discussed in the literature. The problem concerns WSNs deployed to guard an area with multiple entry-exit sides where intruders can cross the area through any specified subset of sides. Nodes in the network can fail randomly, and we ask what is the likelihood that the network can successfully detect intrusion events. Our work here develops methods for deriving upper bounds on the solutions by means of packing network nodes into cutsets having certain properties. The developed methods are efficient and can be used either as standalone tools, or as subroutines to improve the time-accuracy of other iterative methods that can achieve higher accuracy with increased number of iterations. The obtained numerical results are used to analyze the merits of the devised methods. In addition, we discuss and evaluate the applicability of our methods to tackle an optimum sink location design problem.
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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".