An Absorbing Markov Chain Based Model to Solve Computation and Communication Tradeoff in GPU-Accelerated MDRUs for Safety Confirmation in Disaster Scenarios
Bibliographic record
Abstract
The fast increasing chip processing capacities driven by the Moore's Law have encouraged the academia and industry to consider more about general hardware architectures since they allow the repeated use for multiple purposes through the installations of applications. Some techniques utilizing the general hardware architectures have been developed to improve the flexibility of computer networks, such as the Software Defined Networking (SDN) and the Network Functions Virtualization (NFV). For these networks, the applications are required to be computation/communication-efficient since the installed applications share the hardware. In this paper, we study the resource-limited disaster recovery networks constructed by the Movable and Deployable Resource Units (MDRUs) which consist of various general computation platforms. We propose an efficient safety confirmation method through the photo sharing by the survivors. In the proposal, the Absorbing Markov Chain is utilized to model the safety confirmation process, transition matrix of which can be adopted to choose the suitable photo size for optimizing the traffic overhead and buffer consumption. Through periodical update of the photo database, unnecessary packet transmissions can be further avoided with reasonable sacrifice of the computation overhead. To expedite the computation, the GPU-accelerated MDRU is considered to conduct the matrix calculations in a parallel fashion.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".