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- OpenStreetMap is Having a Second — Apple was answerable for extra edits in 2019 than Mapbox accounted for in its total company historical past. See additionally the 2020: Curious Instances of Firms in OpenStreetMap speak from State of the Map. (through Simon Willison)
- Drone Warfare — The second level, “SkyNet”, is the fascinating bit. Azerbaijan and Armenia fought a struggle and drones enabled some very uneven outcomes. Quoting a Washington Submit story, Azerbaijan, pissed off at a peace course of that it felt delivered nothing, used its Caspian Sea oil wealth to purchase arms, together with a fleet of Turkish Bayraktar TB2 drones and Israeli kamikaze drones (additionally referred to as loitering munitions, designed to hover in an space earlier than diving on a goal). […] Azerbaijan used surveillance drones to identify targets and despatched armed drones or kamikaze drones to destroy them, analysts mentioned. […] Their tally, which logs confirmed losses with pictures or movies, listed Armenian losses at 185 T-72 tanks; 90 armored combating automobiles; 182 artillery items; 73 a number of rocket launchers; 26 surface-to-air missile techniques, together with a Tor system and 5 S-300s; 14 radars or jammers; one SU-25 struggle airplane; 4 drones and 451 army automobiles. (through John Birmingham)
- Peregrine — an environment friendly, single-machine system for performing information mining duties on massive graphs. Some graph mining functions embody: Discovering frequent subgraphs; Producing the motif/graphlet distribution; Discovering all occurrences of a subgraph. Peregrine is extremely programmable, so you possibly can simply develop your personal graph mining functions utilizing its novel, declarative, graph-pattern-centric API. To jot down a Peregrine program, you describe which graph patterns you have an interest in mining, and what to do with every prevalence of these patterns. You present the what and the runtime handles the how.
- Declining Marginal Returns of Researchers — (Tamay Besiroglu) I discovered that the marginal returns of researchers are quickly declining. There’s what’s referred to as a “standing on toes” impact: researcher productiveness declines as the sphere grows. As a result of ML has just lately grown in a short time, this makes higher ML fashions a lot more durable to search out. (Dissertation)
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