Four short links: 24 Nov 2020
OpenStreetMap Numbers, Drone Warfare, Pattern-Aware Graph Mining, and Declining Researcher Productivity
- OpenStreetMap is Having a Moment — Apple was responsible for more edits in 2019 than Mapbox accounted for in its entire corporate history. See also the 2020: Curious Cases of Corporations in OpenStreetMap talk from State of the Map. (via Simon Willison)
- Drone Warfare — The second point, “SkyNet”, is the interesting bit. Azerbaijan and Armenia fought a war and drones enabled some very asymmetric outcomes. Quoting a Washington Post story, Azerbaijan, frustrated at a peace process that it felt delivered nothing, used its Caspian Sea oil wealth to buy arms, including a fleet of Turkish Bayraktar TB2 drones and Israeli kamikaze drones (also called loitering munitions, designed to hover in an area before diving on a target). […] Azerbaijan used surveillance drones to spot targets and sent armed drones or kamikaze drones to destroy them, analysts said. […] Their tally, which logs confirmed losses with photographs or videos, listed Armenian losses at 185 T-72 tanks; 90 armored fighting vehicles; 182 artillery pieces; 73 multiple rocket launchers; 26 surface-to-air missile systems, including a Tor system and five S-300s; 14 radars or jammers; one SU-25 war plane; four drones and 451 military vehicles. (via John Birmingham)
- Peregrine — an efficient, single-machine system for performing data mining tasks on large graphs. Some graph mining applications include: Finding frequent subgraphs; Generating the motif/graphlet distribution; Finding all occurrences of a subgraph. Peregrine is highly programmable, so you can easily develop your own graph mining applications using its novel, declarative, graph-pattern-centric API. To write a Peregrine program, you describe which graph patterns you are interested in mining, and what to do with each occurrence of those patterns. You provide the what and the runtime handles the how.
- Declining Marginal Returns of Researchers — (Tamay Besiroglu) I found that the marginal returns of researchers are rapidly declining. There is what’s called a “standing on toes” effect: researcher productivity declines as the field grows. Because ML has recently grown very quickly, this makes better ML models much harder to find. (Dissertation)
