Multi-view POI-level Cellular Trajectory Reconstruction for Digital Contact Tracing of Infectious Diseases
Bibliographic record
Abstract
Digital contact tracing is an effective solution to prevent such a pandemic, but the low adoption rate of a required mobile app hinders its effectiveness. A large collection of cellular trajectories from mobile subscribers can be an out-of-the-box solution that is free from the low adoption issue, but has been overlooked due to its low spatial resolution. In this paper, to increase the resolution of this cellular trajectory, we present a new problem that estimates the user’s visited places at the point-of-interest(POI) level, which we call POI-level cellular trajectory reconstruction. We propose a novel algorithm, Pincette, that accomplishes more accurate POI reconstruction by leveraging various external data such as road networks and POI contexts. Specifically, Pincette comprises multi-view feature extraction and GCN-LSTM-based POI estimation. In the multi-view feature extraction, Pincette extracts three complementary features from three views: efficiency, periodicity, and popularity. In the GCN-LSTM-based POI estimation, these three views are seamlessly integrated, where spatio-temporal periodic patterns are captured by graph convolutional networks (GCNs) and an LSTM. With extensive experiments on two real data collections of two cities, we show that Pincette outperforms four POI estimation baselines by up to 21.20%. We believe that our work sheds light on the use of cellular trajectories for digital contact tracing. We release the source code at https://github.com/kaist-dmlab/Pincette.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".