Operation research tools and methodology for the design and provisioning of survivable optical networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While the design and the provisioning of survivable optical networks have already been studied for a long time, networks across the world are still experiencing a phenomenal growth in data traffic, leading to more complex design and provisioning problems. Network architectures are changing rapidly to meet the new end-user requirements, with many new technovirtual developments and new economical/environmental concerns such as, e.g., network virtualization, anycast routing, elastic networking, energy minimization. While operational research methods have made significant progress over the last 20 years, not much has been done in order to use the full potential of these developments in managing more efficiently communication networks, while, in other areas of applications, they have been used to solve efficiently very large/huge scale optimization problems, e.g., in the transport industry or in financial engineering or in industrial location. This paper gives an overview of some of the developments in solving some optimization problems arising in the design of optical networks or grids. In terms of optimization techniques, we will focus on decomposition techniques, and will discuss their recent success for the protection of optical networks, and of virtual networks built upon optical networks/grids.
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.
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 | 0.003 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it