Albert Hsiao, M.D., Ph.D., and his colleagues at the University of California San Diego (UCSD) health system had been working for 18 months on an artificial intelligence program designed to help doctors identify pneumonia on a chest X-ray. When the coronavirus hit the U.S., they decided to see what it could do. The researchers quickly deployed the application, which dots X-ray images with spots of color where there may be lung damage or other signs of pneumonia. It has now been applied to more than 6,000 chest X-rays, and it's providing some value in diagnosis, said Hsiao, director of UCSD's augmented imaging and artificial intelligence data analytics laboratory. His team is one of several around the country that has pushed AI programs developed in a calmer time into the COVID-19 crisis to perform tasks like deciding which patients face the greatest risk of complications and which can be safely channeled into lower-intensity care.

As societies around the world take increasingly drastic measures to confront the COVID-19 pandemic, the collective response is having an unforeseen impact on the environment. For example, with factories closed and transportation restricted, China saw a 25% decline in carbon dioxide emissions over a four-week period. Similarly, New York City, with fewer restrictions than China, has still seen emissions fall by 5%-10%. It's as if by confronting one crisis, humankind has shown that it could also -- with the proper motivation -- confront another. Of course, like the pandemic itself, the drop in emissions will eventually come to an end, rebounding once restrictions on movement and economic activity ease.

Air power has played an increasingly important role in the Libyan conflict. The relatively flat featureless desert terrain of the north and coast means that ground units are easily spotted, with few places to hide. The air forces of both the United Nations-recognised Government of National Accord (GNA) and eastern-based commander Khalifa Haftar and his self-styled Libyan National Army (LNA) use French and Soviet-era fighter jets, antiquated and poorly maintained. While manned fighter aircraft have been used, for the most part the air war has been fought by unmanned aerial vehicles (UAVs) or drones. With nearly 1,000 air strikes conducted by UAVs, UN Special Representative to Libya Ghassan Salame called the conflict "the largest drone war in the world".

Twitter users stretch words such as'yes', 'dude' and'hey' to modify their meaning, according to researchers who analysed 100 billion tweets. The US linguist experts say stretched words that convey a different meaning than the original are common feature of social media, but are rare in formal writing. For instance, 'suuuuure' can imply sarcasm, 'duuuuude' can be a sign of incredulity, 'yeeessss' may indicate excitement and'heellllp' may be a sign of desperation. Researchers say they've developed new tools that could be used in future research of stretchable words, such as investigations of mistypings and misspellings. These could also be applied to improve natural language processing for software and search engines and Twitter's spam filters, or even have applications in genetics.

A team of researchers have developed a new algorithm that could help search and rescue teams locate people lost at sea using ocean currents, wind speed, and wave direction. The project was a joint effort from scientists at MIT, the Swiss Federal Institute of Technology (ETH), the Woods Hole Oceanographic Institution (WHOI), and Virginia Tech, who tested their method using human manikins in the ocean off the coast of Martha's Vineyard. Unlike current search and rescue models--which also use data about ocean currents and wind to calculate the likely location of a missing person by simulating one single linear path--the team's new system is focused on identifying multiple points of'attraction' in the ocean, which can sometimes change dramatically over time. Using a system they called Transient Attracting Profiles (TRAPS), the team tracks these attraction points, which they behave like'moving magnets' pulling people in the water toward them. Instead of mapping out a single, linear path, the TRAPS model identifies many different attraction points, or'traps,' in the ocean that will likely have pulled a person in multiple directions as they drift through the waters.

NASA has shared new details about the sensors used on the Perseverance rover as it travels the surface of Mars in search for signs of past microbial life. The instruments, a high powered camera and an ultraviolet laser, will work in tandem to take readings of the soil to help determine its chemical and mineral makeup. The main instrument, called SHERLOC (or Scanning Habitable Environments with Raman & Luminescence for Organics & Chemicals), will be mounted on the end of one of the rover's robotic arms. NASA's Persevernce rover will travel across Mars using an ultraviolet laser to determine what minerals and compounds are present in the soil, based on the way the light scatters SHERLOC will emit a quarter-sized ultraviolet laser at the ground, and scientists will measure the way the light scatters when it hits the ground to infer what kind of minerals and chemical compounds it's made of. The technique will also be used to identify the unique spectral'fingerprint' that certain organic material might give off in the hopes of tracking down potential signs of past life.

Facebook is no stranger to releasing experimental apps, but Collab, the latest effort from its NPE team, may end up its most creative yet. The software allows you to create a clip of original music and then add up to two other community-created compositions to build a more complex arrangement. You can also mix and match videos exclusively created by other people. Whatever direction you take, the three videos play in sync to create a song. You can find clips by swiping through the interface -- though you can't remix the underlying music someone else created.

A group of bipartisan, bicameral politicians have drafted a bill that would "dramatically" increase investment in tech research and development. The Endless Frontiers Act would commit $100 billion over five years toward research in artificial intelligence, high-performance computing, robotics, automation and more. Another $10 billion would go towards creating regional technology hubs across the US. Senate Democractic Leader Chuck Schumer, Senator Todd Young and Congressmen Ro Khanna and Mike Gallagher unveiled the legislation today. They say it's partly in response to the coronavirus, which they claim "magnified weakness from decades of US underinvestment in scientific research," and competition from countries like China.

Apple is continuing its string of AI startup acquisitions, this time to improve Siri's performance. The company has confirmed to Bloomberg that it recently acquired Inductiv, a Waterloo, Ontario, Canada-based company that uses AI to correct data -- which, in turn, improves machine learning. The company didn't elaborate on its plans and relied on its standard response that it "buys smaller technology companies from time to time," but Siri appears to be the focus. The iPhone maker appears to be focused on improving its voice assistant's understanding as of late, most recently acquiring Voysis to boost natural language comprehension. Cleaner data would go a long way toward that goal by reducing the chances that garbage information confuses Siri.

In an ever-increasing interest for Machine Learning (ML) and a favorable data development context, we here propose an original methodology for data-based prediction of two-dimensional physical fields. Polynomial Chaos Expansion (PCE), widely used in the Uncertainty Quantification community (UQ), has recently shown promising prediction characteristics for one-dimensional problems, with advantages that are inherent to the method such as its explicitness and adaptability to small training sets, in addition to the associated probabilistic framework. Simultaneously, Dimensionality Reduction (DR) techniques are increasingly used for pattern recognition and data compression and have gained interest due to improved data quality. In this study, the interest of Proper Orthogonal Decomposition (POD) for the construction of a statistical predictive model is demonstrated. Both POD and PCE have widely proved their worth in their respective frameworks. The goal of the present paper was to combine them for a field-measurement-based forecasting. The described steps are also useful to analyze the data. Some challenging issues encountered when using multidimensional field measurements are addressed, for example when dealing with few data. The POD-PCE coupling methodology is presented, with particular focus on input data characteristics and training-set choice. A simple methodology for evaluating the importance of each physical parameter is proposed for the PCE model and extended to the POD-PCE coupling.