Identifying tropical cyclones that generate destructive storm tides for risk assessment, such as from large downscaled storm catalogs for climate studies, is often intractable because it entails many expensive Monte Carlo hydrodynamic simulations. Here, we show that surrogate models are promising from accuracy, recall, and precision perspectives, and they "generalize" to novel climate scenarios. We then present an informative online learning approach to rapidly search for extreme storm tide-producing cyclones using only a few hydrodynamic simulations. Starting from a minimal subset of TCs with detailed storm tide hydrodynamic simulations, a surrogate model selects informative data to retrain online and iteratively improves its predictions of damaging TCs. Results on an extensive catalog of downscaled TCs indicate 100% precision in retrieving rare destructive storms using less than 20% of the simulations as training. The informative sampling approach is efficient, scalable to large storm catalogs, and generalizable to climate scenarios.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. U.S. Ambassador to Japan Rahm Emanuel accused China on Friday of using "economic coercion" against Japan by banning imports of Japanese seafood in response to the release of treated wastewater from the damaged Fukushima nuclear plant into the ocean, while Chinese boats continue to fish off Japan's coasts. "Economic coercion is the most persistent and pernicious tool in their economic toolbox," Emanuel said in a speech Friday in Tokyo, calling China's ban on Japanese seafood the latest example. China is the biggest market for Japanese seafood, and the ban has badly hurt Japan's fishing industry.

The partial lifting of U.S. metals tariffs slapped on Japan under former U.S. President Donald Trump's administration is the latest bid by U.S. President Joe Biden's government to mend ties with a major ally and counterbalance an increasingly powerful China. Biden inherited a global network of alliances that had been battered by Trump's repeated questioning of their value to the United States, even as many saw such ties as increasingly important, given China's growing wealth and military might. "First, you treat allies as allies," U.S. Ambassador to Japan Rahm Emanuel said in a phone interview Tuesday. "Second, you begin to make a down payment on both climate and standing up for a rules-based system by recognizing nonmarket forces like China have wreaked havoc." The deal on steel tariffs comes as the U.S. seeks to redefine its role in how trade policy is made in Asia following Trump's rejection of the Trans-Pacific Partnership regional trade pact his country once spearheaded.

Making payments using your cellphone is easier than ever. In fact, the process of buying something now often starts on a mobile device, and the relative ease of paying virtually is eliminating the need for cash or even a credit card--a trend that is sending ripples through the business world. "Why do you need cash? It's fundamentally less efficient," said Dan Schulman, the president and chief executive of payments company PayPal Holdings Inc. He said he believes digital payments will become an ever-larger industry, with many key players.

Two mayors discussed how they are using artificial intelligence and machine learning to improve their cities and prepare for the workforce of the future at a conference held April 23 in Chicago. The event was hosted by news organization Axios and the United States Conference of Mayors and led by Axios Executive Editor Mike Allen. Also joining the discussion was Imir Arifi, head of artificial intelligence and machine learning at Health Care Service Corporation. According to Arifi, the main use of AI and machine learning is through historical data to predict future events. In a city, for example, Arifi said AI can be used to predict how many potholes the city will need to fill in a year based on data from previous years.

The recent National Labor Relations Board (NLRB) decision that effectively reinstated the Obama era's over-reaching joint employer regulation is a perfect example of how the left plays by its own set of rules. The joint employer rule made headlines in 2015 when the NLRB, under President Obama, rewrote the definition of what the government considered a "joint employer." Traditionally, a joint employer was an employer who shared direct control over an employee's workplace or employment with another employer. The idea was that since all employers shared and exercised similar and immediate control over employees, all should be responsible for making sure the employees had safe and reasonable working conditions. It also meant all joint employers were responsible for mistakes or bad behavior at their businesses.

Radiologists bring home $395,000 each year, on average. In the near future, however, those numbers promise to drop to $0. Don't blame Obamacare, however, or even Trumpcare (whatever that turns out to be), but rather blame the rise of machine learning and its applicability to these two areas of medicine that are heavily focused on pattern matching, a job better done by a machine than a human. This is the argument put forward by Dr. Ziad Obermeyer of Harvard Medical School and Brigham and Women's Hospital and Ezekiel Emanuel, PhD, of the University of Pennsylvania, in an article for the New England Journal of Medicine, one of the medical profession's most prestigious journals. Machine learning will produce big winners and losers in healthcare, according to the authors, with radiologists and pathologists among the biggest losers.