In the sequel, we empirically show the effect of different numbers of local updates on the fixed point. We consider cases withK = 1, K = 10, K = 20, K = 50. From Assumption 1, it is obvious thatgi(x,y) is convex-concave. Then, we conclude that there exists someη1 > 0 such that h(η) > 0, 0 < η < η1.

NATO officials are kicking around a new set of questions for member states on artificial intelligence in defense applications, as the alliance seeks common ground ahead of a strategy document planned for this summer. The move comes amid a grand effort to sharpen NATO's edge in what officials call emerging and disruptive technologies, or EDT. Autonomous and artificial intelligence-enabled weaponry is a key element in that push, aimed at ensuring tech leadership on a global scale. Exactly where the alliance falls on the spectrum between permitting AI-powered defense technology in some applications and disavowing it in others is expected to be a hotly debated topic in the run-up to the June 14 NATO summit. "We have agreed that we need principles of responsible use, but we're also in the process of delineating specific technologies," David van Weel, the alliance's assistant secretary-general for emerging security challenges, said at a web event earlier this month organized by the Estonian Defence Ministry.

Technology is reshaping every aspect of our lives. Once a week in The Future Of, we examine innovations in important fields, from farming to transportation, and what they will mean in the years and decades to come. The case was complicated: Shoulder arthroplasty, to deal with an advanced case of arthritis affecting the patient's glenoid -- the ball part of the ball-and-socket joint in the shoulder. To handle the case most effectively, the surgeon wanted assistance from the best. But the best was physically half a world away.

By accurately predicting industrial aging processes (IAPs), it is possible to schedule maintenance events further in advance, thereby ensuring a cost-efficient and reliable operation of the plant. So far, these degradation processes were usually described by mechanistic models or simple empirical prediction models. In this paper, we evaluate a wider range of data-driven models for this task, comparing some traditional stateless models (linear and kernel ridge regression, feed-forward neural networks) to more complex recurrent neural networks (echo state networks and LSTMs). To examine how much historical data is needed to train each of the models, we first examine their performance on a synthetic dataset with known dynamics. Next, the models are tested on real-world data from a large scale chemical plant. Our results show that LSTMs produce near perfect predictions when trained on a large enough dataset, while linear models may generalize better given small datasets with changing conditions.

Cristina Might drew close to her son. He was listless and groggy after weeks of battling a puzzling illness that had filled his lungs with fluid and, hours earlier, stopped his breathing entirely. A code team had rushed to Buddy's bedside and jolted him back to life, but now the 11-year-old with the broad smile was gray, his eyes unable to focus. His mom leaned nearer still. It was time to say goodbye. But Cristina's words to her son, a brown-eyed boy who loved dolphins and his aquarium, offered no hint of her desperation: "I was telling him it was all going to be OK, that his fishies couldn't wait to see him again and that he had to hurry up and come home." Somehow, Buddy made it through that night this past May, allowing doctors at Children's Hospital of Alabama to insert a tube to drain his lungs. His illness had caused a frightening cascade of symptoms: a yellowish substance in his bones and a bulging abdomen, on top of the deluge of fluid.