Visualization of Repeated Patterns in Multivariate Discrete Sequences
Bibliographic record
Abstract
The availability and affordability of mobile devices, wearables, sensors, IoT devices and electronic social networks produce big data in the form of complex systems of multivariate discrete sequences such as bio-informatics, natural language processing, social network corpus, etc. or non-discrete time series such as weather data, network traffic, workout data, etc. At the same time, the increased availability of advanced hardware in the form of powerful computers or high performance clusters provide us with the opportunity to analyze the aforementioned datasets that could produce vast amounts of results in diverse forms. One problem that has recently got focus is the one of discovering all repeated patterns in multivariate sequences. Novel algorithms have appeared such as ARPaD that address the specific problem however there are still no appropriate visualization methods to represent the complex results of the algorithm. In this paper, we attempt to create a visualization method that presents the common repeated patterns in multivariate discrete sequences. The visualization algorithm has been applied in a dataset of different text sequences of varying length and the results are presented in two novel type of plots, the Pattern Positional Alignment (PaPA) plot and the Stacked PaPA plot.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".