INVARIANTS OF PERSISTENCE MODULES DEFINED BY ORDER-EMBEDDINGS
Bibliographic record
Abstract
One of the main objectives of topological data analysis is the study of discrete invariants for persistence modules, in particular when dealing with multiparameter persistence modules. In many cases, the invariants studied for these non-totally ordered posets $P$ can be obtained from restricting a given module to a subposet $X$ of $P$ that is totally ordered (or more generally, of finite representation type), and then computing the barcode (or the general direct sum decomposition) over $X$. We consider in this paper general order-preserving embeddings of representation-finite subposets $X$ into $P$ and study systematically the invariants obtained by decomposing the restriction of a given $P$-module $M$ to $X$ into its indecomposable summands. The restriction functor from $\mathrm{mod}\ P$ to $\mathrm{mod}\ X$ is well-studied, and it is known to be exact and admits both left and right adjoint functors, known as induction and co-induction functors. This allows us to obtain new homological insights, and also to re-interpret previous results. We use this approach also to determine bases of the image of these invariants, thus generalizing the concept of signed barcodes which is considered in the literature in relation to stability results. It turns out that considering only order-embeddings of one fixed poset $X$ into the poset $P$, and studying the set of all indecomposables obtained from $X$ introduces a lot of redundancy. We therefore also study iterated embeddings of several posets of increasing sizes, while limiting attention to only some indecomposables (that have not been obtained from embedding of smaller posets previously).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".