A Prediction-Enhanced Physical-to-Virtual Twin Connectivity Framework for Human Digital Twin
Bibliographic record
Abstract
This paper proposes a new secure and privacy-preserving prediction-enhanced solution for reliable physical-to-virtual communications in human digital twin (HDT) systems. With such a prediction-enhanced connectivity (PeHDT) framework, the evolution of any virtual twin (VT) could be triggered in real-time or in advance using the expected state of its physical counterpart. This ensures the continuous maintenance of a true replica of each physical twin (PT), thus relieving the need for timely PT-VT synchronization while the VT-experienced delay is reduced to zero or close to zero. We adopted a secured federated multi-task learning technique to meet the security and privacy constraints of HDT and employed a single server discrete-time batch-service queue framework when characterizing the batching process to reduce the communication burden. Furthermore, we introduced a prediction verification framework to improve the performance of the proposed PeHDT framework. The resulting problem was formulated as a constrained Markov decision process and was solved by introducing a primary-dual deep deterministic policy gradient (DDPG) algorithm. Through a joint investigation of communication, batching and prediction verification schemes, the simulation results show that the proposed PeHDT framework can greatly reduce both the VT-experienced delay and the PT-VT communication time without compromising the specific requirements of HDT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".