Optimum Digital Twin Response Time for Time-Sensitive Applications
Bibliographic record
Abstract
As the digital replica of a physical system (PS), a digital twin (DT) is responsible for providing real-time information of its PS to applications. However, random network conditions result in uncertainty in future age of information (AoI) at the DT, which makes it complicated for a DT to decide when to response an application request in order to maintain the best information freshness at the application. In this work, we consider the effect of random wireless channel condition between the PS and the DT on the AoI changes at the DT, and formulate a Markov decision process that finds the optimum response time for the DT to send the PS information to an application after receiving a request from the application. The objective is to minimize the average AoI at the application. The MDP has delayed reward, and is solved by redistributing the reward with LSTM network and then finding the optimal policies using Dueling Double Deep Q-learning. Numerical results show that the solutions provide close-to-optimum average AoI performance.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".