arXiv:2604.25209v2 Announce Type: replace-cross
Abstract: Dimensionality reduction methods such as UMAP and t-SNE are central tools for visualising high-dimensional data, but their local-neighborhood objectives can preserve sampling noise while distorting global topology. We show that standard local metrics reward this noise memorisation: top-performing embeddings invent cycles and disconnected islands absent from the data. We introduce a topology-faithfulness benchmark based on noisy manifolds with known homology, tune DiRe against it, and find Pareto-optimal configurations that match or beat GPU-accelerated UMAP on classification while recovering exact first Betti numbers on stress tests. On 723K arXiv paper embeddings, DiRe preserves 3-4 times more topological structure than UMAP at comparable wall-clock.
Differential acceptance of a national digital health platform among community and frontline health workers in Cote d’Ivoire: a cross-sectional study
IntroductionMobile-based digital health solutions are critical technologies that play a significant role in improving the quality of healthcare services. Cote d’Ivoire is digitizing its community-based