Network tomography by Non Negative Matrix Factorization (NNMF)
Bibliographic record
Abstract
This paper presents the application of a matrix based technique to eliminate the assumption of a known routing matrix in network tomography. Network tomography is an effective means of determining network performance parameters such as delay and packet loss rate (PLR). It gives indirect inference of network characteristics using active probes or passive monitoring of packets. Most of the network tomography research unrealistically assumes that the routing matrix is known and models network tomography as an inverse problem. This motivates us to look for more appropriate methods for the inverse problem solution where the routing matrix is accommodated by the statistical ability of such methods as Non Negative Matrix Factorization (NNMF). NNMF is used to factorize a matrix into two factors (matrices). The whole process involves matrices and optimizing the residue (difference between the initial value and the current value of a cost function) to obtain the best result. We have applied NNMF to the data obtained from a laboratory test bed to perform delay tomography under various traffic conditions. The simulation results verify that NNMF performs network tomography accurately without a priori knowledge of the routing matrix.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".