home.social

#edges — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #edges, aggregated by home.social.

  1. Sporting Braga managed a 2-1 victory over SC Freiburg in the first leg of the Europa League, securing the win only in added time.The home side struck early, tak... news.osna.fm/?p=44038 | #news #braga #dramatic #edges #europa

  2. Ah yes, another "whirlwind introduction" to *dataflow graphs*—because who doesn't want to spend their free time swimming in a sea of #nodes and edges? 🌪️📊 The blog that truly believes we all wanted our #bedtime #stories to be about the exciting world of background material that should've stayed just that—background. 💤💡
    fgiesen.wordpress.com/2018/03/ #dataflowgraphs #introduction #edges #backgroundmaterial #HackerNews #ngated

  3. Advancing Digital Earth Modeling - Hexagonal Multi-Structural Elements In Icosahedral DGGS For Enhanced Geospatial Data Processing
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    doi.org/10.1016/j.envsoft.2023 <-- shared paper
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    en.wikipedia.org/wiki/Discrete <-- DGD wiki page
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    [the math is way over my head, hence the wiki page leak, but a good read nonetheless]
    “HIGHLIGHTS:
    • Hexagonal multi-structural elements enhance Earth's surface modeling precision.
    • Integration of indexing and conversion rules improves geospatial data computation.
    • DGGRID implementation shows increased precision in raster and vector data modeling.
    • Addresses limitations in existing software for Earth observation data.
    • Pioneering approach expands geospatial data processing applications…"
    #GIS #spatial #mapping #DiscreteGlobalGrid #DGG #DGGS #indexing #conversion #rules #computation #Hexagonal #DGGRID #raster #vector #data #model #modeling #earthobservation #remotesensing #grid #vertices #edges #icosahedral #projections #coordinates #representation

  4. How far can it go?

    I worked with this little watercolor painting tonight from the spontaneous watercolor landscape workshop.[…]
    quiltr.com/?p=23968

  5. @christianp The number of trees you need for a graph is called its arboricity — see en.wikipedia.org/wiki/Arborici

    It always equals the maximum, over subgraphs, of #edges(subgraph)/(#vertices(subgraph)-1), and can be computed in polynomial time.