As California turns to satellite imagery, remote cameras watched by AI and heat detection sensors placed throughout wildlands to detect fires earlier, one Orange County group is keeping it old-school. Whenever the National Weather Service issues a red flag warning, a sign that dangerous fire weather is imminent, Renalynn Funtanilla swiftly sends alerts to her more than 300 volunteers' phones and inboxes. She wheels TVs into a conference room turned makeshift command center, sets up computers and phones around the table and dispatches volunteers to dozens of trailheads and roadways in Orange County's wildland-urban interface: likely spots for the county's next devastating fire to erupt. The volunteers -- sporting bright yellow vests and navy blue hats with an "Orange County Fire Watch" emblem -- slap large fire watch magnets to the sides of their vehicles, grab some binoculars and start to watch. Amid California's coastal sage scrub and chaparral ecosystems that are plagued with frequent fast-moving fires, preventing ignitions and stamping out fires before they become unmanageable is the name of the game.

Welcome to Slate's weekly news quiz. It's Friday, which means it's time to test your knowledge of the week's news events. Your host, Ray Hamel, has concocted questions on news topics ranging from politics to business, from culture to sports to science. At the end of the quiz, you'll be able to compare your score with that of the average contestant, as well as that of a Slatester who has agreed to take the quiz on the record. This week's contestant is senior editor Rebecca Onion.

We provide theoretical results which prove that average pooling corresponds to projection within the space of square-integrable functions and show that U-Nets with average pooling implicitly learn a Haar wavelet basis representation of the data.

This work is motivated by the observation that adversarially injected edges effectively can be viewed as additional samples to a node's neighborhood aggregation

This accuracy makes it possible to derive properties such as energies and forces directly from the wavefunction in an end-to-end manner.