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PHINN: Persistent Homology Inspired Neural Network for Rare-Event Time Series Generation

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arXiv:2606. 15452v1 Announce Type: cross Abstract: Rare events in time series are critical to model but hard to learn due to data scarcity.

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Authors: Emre Yusuf, Ren Takahashi, Jayabrata Bhaduri
Released: 2026-06-16 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because PHINN: Persistent Homology Inspired Neural Network for Rare-Event Time Series Generation overlaps with clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", experiment "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: line, language, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.15452

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