Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. For the past year, five fit, academically superior men and women have been spun in centrifuges, submerged for hours, deprived temporarily of oxygen, taught to camp in the snow, and schooled in physiology, anatomy, astronomy, meteorology, robotics, and Russian. On Monday, the five Europeans and an Australian graduated from basic training with a new title: astronaut. At a ceremony in Cologne, Germany, ESA added the five newcomers to its astronaut corps eligible for missions to the International Space Station, bringing the total to 11. HOW ASTRONAUTS ON THE ISS ARE TACKLING THE LATEST'UNEXPECTED CHALLENGES' MILES ABOVE THE EARTH ESA has negotiated with NASA for three places on future Artemis moon missions, although those places will likely go to the more senior astronauts, according to ESA Director General Josef Aschbacher.

Hybrid algorithms, which combine black-box machine learning methods with experience from traditional numerical methods and domain expertise from diverse application areas, are progressively gaining importance in scientific machine learning and various industrial domains, especially in computational science and engineering. In the present survey, several promising avenues of research will be examined which focus on the combination of machine learning (ML) and domain decomposition methods (DDMs). The aim of this survey is to provide an overview of existing work within this field and to structure it into domain decomposition for machine learning and machine learning-enhanced domain decomposition, including: domain decomposition for classical machine learning, domain decomposition to accelerate the training of physics-aware neural networks, machine learning to enhance the convergence properties or computational efficiency of DDMs, and machine learning as a discretization method in a DDM for the solution of PDEs. In each of these fields, we summarize existing work and key advances within a common framework and, finally, disuss ongoing challenges and opportunities for future research.

Check out all the on-demand sessions from the Intelligent Security Summit here. Seeking to target enterprise customers with AI language translation, Cologne, Germany-based DeepL announced a new funding raise that public reports estimate at well over $100 million. Language translation is an increasingly critical function for enterprises working across geographies and different demographics. Basic language translation capabilities have been available on for decades -- for example, services such as Google Translate. But the challenge has been enabling more advanced translation for business use cases that capture not just the literal meaning but the right tone and context.

Artificial intelligence startups, and (thanks to GPT and Open AI) specifically those helping humans communicate with each other, are commanding a lot of interest from investors, and today the latest of these is announcing a big round of funding. DeepL, a startup that provides instant translation-as-a-service both to businesses and to individuals -- competing with Google, Bing and other online tools -- has confirmed a fundraise at a €1 billion valuation (just over $1 billion at today's rates). Cologne, Germany-based DeepL is not disclosing the full amount that it's raised -- it doesn't want to focus on this aspect, CEO and founder Jaroslaw Kutylowski said in an interview -- but as we were working on this story we heard a range of figures. At one end, an investor that was pitched on the funding told TechCrunch that DeepL was aiming to raise $125 million. At the other end, a report with a rumor about the funding from back in November said the amount was around $100 million.

Royal Philips, a global leader in health technology, announced its SmartSpeed artificial intelligence (AI) powered MR acceleration software has received U.S. Food and Drug Administration (FDA) 510(k) clearance. Adding advanced AI data collection algorithms to Philips' existing Compressed SENSE MR acceleration engine, Philips SmartSpeed delivers higher image resolution with 3 times faster scan times [1] and virtually no loss in image quality, representing a major step forward in diagnostic confidence and MR department productivity. With personalized treatment for complex diseases such as cancer increasing the need for high-confidence precision diagnoses, coupled with soaring caseloads due to aging populations and high levels of clinician burnout, radiology departments are under increasing pressure to improve performance, productivity, and profitability. "Philips' AI-based SmartSpeed reconstruction is the new benchmark among acceleration techniques for us. It improves on the company's existing Compressed SENSE in all aspects and allows a reduction in scan times with excellent image quality and diagnostic confidence," said Dr. Grischa Bratke, radiologist and expert in musculoskeletal imaging at the University Hospital of Cologne, Cologne, Germany.

Ford is using two robotic test drivers – affectionately named Shelby and Miles – to trial its vehicles in extreme temperatures. The robots are conducting tests in environmental conditions that are too treacherous for any human worker to endure. Shelby and Miles can operate at temperatures ranging from -40 F to 176 F (-40 C to 80 C) as well as at extreme altitudes, Ford says. Their robotic legs extend to the accelerator, brake and clutch pedals, with one arm positioned to change gear and the other used to start and stop the engine. The tests are taking place at Ford's secretive'weather factory' in Cologne, Germany – a building the size of a football pitch that's dedicated to R&D work.

Crises like the COVID-19 pandemic pose a serious challenge to health-care institutions. They need to plan the resources required for handling the increased load, for instance, hospital beds and ventilators. To support the resource planning of local health authorities from the Cologne region, BaBSim.Hospital, a tool for capacity planning based on discrete event simulation, was created. The predictive quality of the simulation is determined by 29 parameters. Reasonable default values of these parameters were obtained in detailed discussions with medical professionals. We aim to investigate and optimize these parameters to improve BaBSim.Hospital. First approaches with "out-of-the-box" optimization algorithms failed. Implementing a surrogate-based optimization approach generated useful results in a reasonable time. To understand the behavior of the algorithm and to get valuable insights into the fitness landscape, an in-depth sensitivity analysis was performed. The sensitivity analysis is crucial for the optimization process because it allows focusing the optimization on the most important parameters. We illustrate how this reduces the problem dimension without compromising the resulting accuracy. The presented approach is applicable to many other real-world problems, e.g., the development of new elevator systems to cover the last mile or simulation of student flow in academic study periods.

When it comes to life and death in an accident or health emergency, every minute can be crucial. For this to happen, both rescue vehicles and rescue workers must be on standby around the clock. Organizing all this, however, is not easy, especially in a city with over a million inhabitants. In Cologne, Germany, this task could become a little less complicated in the future thanks to artificial intelligence. To this end, Mark Schleider, a graduate of the master's program in computer science at the Technical University (TH) of Bingen, has developed a model in his final thesis that predicts the workload of rescue services.

Turns out, computers do too. When US and European researchers fed pictures of congressmembers to Google's cloud image recognition service, the service applied three times as many annotations related to physical appearance to photos of women as it did to men. The top labels applied to men were "official" and "businessperson;" for women they were "smile" and "chin." "It results in women receiving a lower status stereotype: That women are there to look pretty and men are business leaders," says Carsten Schwemmer, a postdoctoral researcher at GESIS Leibniz Institute for the Social Sciences in Köln, Germany. He worked on the study, published last week, with researchers from New York University, American University, University College Dublin, University of Michigan, and nonprofit California YIMBY.

Artificial intelligence (AI) isn't magic, it's just really complicated math, said Greg Mulholland, CEO and founder of Citrine Informatics (Redwood City, CA), at a press roundtable hosted by Lanxess (Cologne, Germany) at K 2019. But Mulholland's hosts seemed quite bedazzled by his AI-enabled platform, nonetheless. Lanxess is the first company to adopt Citrine's technology at scale, and Dr. Markus Eckert, Senior Vice President, Head of Business Unit Urethane Systems at Lanxess was eager to explain what it means for customers. Citrine is a Silicon Valley startup that couldn't be more niche: It has developed a platform that leverages data and AI specifically to accelerate the development of materials and chemicals. Citrine has been recognized for technology innovation by the World Economic Forum as a Tech Pioneer, and collaborates with world-class academic institutions such as Carnegie Mellon University in Pittsburgh and the University of California, Berkeley.