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[2026-04-13 23:15] ajaxdavis: https://arxiv.org/html/2509.10415v2 [2026-04-13 23:16] ajaxdavis: "Modeling data as probability measures offers several advantages, particularly when the data possess geometric, spatial, or structural characteristics that traditional vector spaces fail to capture. Recent mathematical advancements in Wasserstein spaces have led to efficient computational algorithms, including manifold learning Hamm et al. [2025], regression Chen et al. [2023], and interpolation Chewi et al. [2021]; Fan and Müller [2024]. However, multiscale analysis within these spaces remains largely unexplored, leaving significant potential for both theoretical advances and practical applications." [2026-04-14 07:06] lisamegawatts: https://arxiv.org/abs/2602.03315 [2026-04-14 22:29] lisamegawatts: https://link.springer.com/chapter/10.1007/978-3-031-34048-2_3 [2026-04-17 09:42] lisamegawatts: https://x.com/akshay_pachaar/status/2045061615935176923?s=20 [2026-04-17 13:26] foxhop.: https://en.wikipedia.org/wiki/S-expression [2026-04-17 13:26] foxhop.: my scheme system uses this instead of JSON as a transport. [2026-04-18 11:47] lisamegawatts: https://huggingface.co/papers/2404.19756 [2026-04-18 12:09] lisamegawatts: https://en.wikipedia.org/wiki/Kuramoto_model [2026-04-18 12:09] lisamegawatts: (files: 330px-Spatial_synchronization_patterns_in_variants_of_the_Kuramoto_Model.png) [2026-04-18 12:30] ajaxdavis: https://dance-iota.vercel.app/