If you were around for the electropop zeitgeist of the early 2010s, chances are that Purity Ring feature prominently on your nostalgia playlist. And if you were a young adult at that time, well, there's also a high chance that you played Japanese role-playing games as a teenager – whether that was Chrono Trigger on an SNES or Final Fantasy on a PlayStation. Purity Ring's new single Many Lives is an attempt to recapture the feeling of the RPG that you discovered as a 12-year-old and immediately made into your whole personality. Inspired by games such as Skies of Arcadia, Phantasy Star Online and Secret of Mana, it is poised to tug on the heartstrings of fans of a certain vintage. This is a bold decision for a band who have previously collaborated with Deftones and covered Eurodance classics, but members Megan James and Corin Roddick have the gaming expertise to pull it off. "We're huge fans of the JRPG genre," they say, naming Nier: Automata and Final Fantasy X as major influences on the sonic atmosphere of their latest work.

A smiling face made from living human skin could one day be attached to a humanoid robot, allowing machines to emote and communicate in a more life-like way, say researchers. Its wrinkles could also prove useful for the cosmetics industry. The living tissue is a cultured mix of human skin cells grown in a collagen scaffold and placed on top of a 3D-printed resin base. Unlike previous similar experiments, the skin also contains the equivalent of the ligaments that, in humans and other animals, are buried in the layer of tissue beneath the skin, holding it in place and giving it incredible strength and flexibility. This robot predicts when you're going to smile – and smiles back Michio Kawai at Harvard University and his colleagues call these ligament equivalents "perforation-type anchors" because they were created by perforating the robot's resin base and allowing tiny v-shaped cavities to fill with living tissue.

We consider the problem of stratified sampling for Monte-Carlo integration. We model this problem in a multi-armed bandit setting, where the arms represent the strata, and the goal is to estimate a weighted average of the mean values of the arms. We propose a strategy that samples the arms according to an upper bound on their standard deviations and compare its estimation quality to an ideal allocation that would know the standard deviations of the strata.

After washing, the 70-year-old executive dons a helmet and heads for its noisy forging plant instead of his corporate office suite. There, he passes on his knowledge of craftsmanship to the younger factory workers, a daily routine that has remained the same since he was promoted to one of the automaker's vice presidents in April 2017, the first graduate of Toyota Motor Corp.'s own vocational school to do so. For Kawai, sitting in a cushy executive office chair holds no appeal. "It's suffocating to go somewhere where factory sounds and smells don't exist," Kawai said in an interview with The Japan Times. "The factory is where I grew up for over 50 years."

On the assembly line in Toyota's low-strung, sprawling Georgetown, Kentucky factory, worker ingenuity pops up in the least expected places. For instance, normally in auto plants installing a gas tank is a tedious, relatively complicated procedure. Because the tank is so heavy, a crane usually positions and holds it against the skeletal frame while employees tighten its straps and bolts from under the chassis, a strained and time-consuming maneuver that requires keeping arms up in the air for long periods of time. To allay the obvious shortcomings in this process, a group of Toyota workers designed an ingenuous device–a multi-armed piece of industrial machinery that in a single action lifts the tank in the air, places it in its crevice and reaches underneath the vehicle's skeletal body to permanently attach the tank to the chassis. The process is fast, seamless, and ergonomically safe.

We consider the problem of stratified sampling for Monte-Carlo integration. We model this problem in a multi-armed bandit setting, where the arms represent the strata, and the goal is to estimate a weighted average of the mean values of the arms. We propose a strategy that samples the arms according to an upper bound on their standard deviations and compare its estimation quality to an ideal allocation that would know the standard deviations of the arms. We provide two regret analyses: a distribution-dependent bound O(n^{-3/2}) that depends on a measure of the disparity of the arms, and a distribution-free bound O(n^{-4/3}) that does not. To the best of our knowledge, such a finite-time analysis is new for this problem.