Neural networks are learning algorithms that approximate the solution to a task by training with available data. However, it is usually unclear how exactly they accomplish this. Two young Basel physicists have now derived mathematical expressions that allow one to calculate the optimal solution without training a network. Their results not only give insight into how those learning algorithms work, but could also help to detect unknown phase transitions in physical systems in the future.

Neural networks are based on the principle of operation of the brain. Such computer algorithms learn to solve problems through repeated training and can, for example, distinguish objects or process spoken language.

For several years now, physicists have been trying to use neural networks to detect phase transitions as well. Phase transitions are familiar to us from everyday experience, for instance when water freezes to ice, but they also occur in more complex form between different phases of magnetic materials or quantum systems, where they are often difficult to detect.

Researchers have used a novel near-infrared light imaging technique to capture the first cross-sectional images of carbon dioxide in the exhaust plume of a commercial jet engine. This new state-of-the-art technology could help accelerate turbine combustion research aimed at developing engines and aviation fuels that are more environmentally friendly.

“This approach, which we call chemical species tomography, provides real-time spatially resolved information for carbon dioxide emissions from a large-scale commercial engine,” said research team leader Michael Lengden from the University of Strathclyde in the U.K. “This information has not been available before at this industrial scale and is a big improvement over the current industry-standard emissions measurement, which involves taking gas from the exhaust to a gas analyzer system in a different location.”

The researchers report the new research in Applied Optics. Chemical species tomography works much like the X-ray-based CT scans used in medicine, except that it uses near-infrared laser light tuned to the absorption wavelength of a target molecule and requires very fast imaging speeds to capture the dynamic processes of combustion.

In the world’s first planetary defense technology demonstration, NASA’s Double Asteroid Redirection Test (DART) made history as it successfully slammed into an asteroid target on Monday. The first ‘attempt’ to move an asteroid in space, the probe tested a way to protect our planet from future hazards and potential impacts.

Massachusetts Institute of Technology (MIT) engineers have developed a battery-free, wireless underwater camera that’s powered by sound waves.

Scientists estimate that more than 95 percent of Earth’s oceans have never been observed, which means we have seen less of our planet’s ocean than we have the far side of the moon or the surface of Mars.

The high cost of powering an underwater camera for a long time, by tethering it to a research vessel or sending a ship to recharge its batteries, is a steep challenge preventing widespread undersea exploration.

MIT researchers have taken a major step to overcome this problem by developing a battery-free, wireless underwater camera that is about 100,000 times more energy-efficient than other undersea cameras. The device takes color photos, even in dark underwater environments, and transmits image data wirelessly through the water.

Researchers at the Department of Energy’s Oak Ridge National Laboratory and the University of Tennessee, Knoxville, discovered a key material needed for fast-charging lithium-ion batteries. The commercially relevant approach opens a potential pathway to improve charging speeds for electric vehicles.

Lithium-ion batteries, or LIBs, play an essential role in the nation’s portfolio of clean energy technologies. Most hybrid electric and all–electric vehicles use LIBs. These rechargeable batteries offer advantages in reliability and efficiency because they can store more energy, charge faster and last longer than traditional lead-acid batteries. However, the technology is still developing, and fundamental advances are needed to meet priorities to improve the cost, range and charge time of electric-vehicle batteries.

“Overcoming these challenges will require advances in materials that are more efficient and synthesis methods that are scalable to industry,” said ORNL Corporate Fellow and corresponding author Sheng Dai.

China’s drive for “common prosperity” is called a ‘’profound revolution’’ by state media. It has unnerved investors.

Scientists from the National Synchrotron Light Source II (NSLS-II) and Computational Science Initiative (CSI) at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory have helped to solve a common problem in synchrotron X-ray experiments: reducing the noise, or meaningless information, present in data. Their work aims to improve the efficiency and accuracy of X-ray studies at NSLS-II, with the goal of enhancing scientists’ overall research experience at the facility.

NSLS-II, a DOE Office of Science user facility, produces X-ray beams for the study of a huge variety of samples, from potential new battery materials to plants that can remediate contaminated soil. Researchers from across the nation and around the globe come to NSLS-II to investigate their samples using X-rays, collecting huge amounts of data in the process. One of the many X-ray techniques available at NSLS-II to visiting researchers is X-ray photon correlation spectroscopy (XPCS). XPCS is typically used to study material behaviors that are time-dependent and take place at the nanoscale and below, such as the dynamics between and within structural features, like tiny grains. XPCS has been used, for example, to study magnetism in advanced computing materials and structural changes in polymers (plastics).

While XPCS is a powerful technique for gathering information, the quality of the data collected and range of materials that can be studied is limited by the “flux” of the XPCS X-ray beam. Flux is a measure of the number of X-rays passing through a given area at a point in time, and high flux can lead to too much “noise” in the data, masking the signal the scientists are seeking. Efforts to reduce this noise have been successful for certain experimental setups. But for some types of XPCS experiments, achieving a more reasonable signal-to-noise ratio is a big challenge.

Can robots adapt their own working methods to solve complex tasks? Researchers at Chalmers University of Technology, Sweden, have developed a new form of AI, which, by observing human behavior, can adapt to perform its tasks in a changeable environment. The hope is that robots that can be flexible in this way will be able to work alongside humans to a much greater degree.

“Robots that work in human environments need to be adaptable to the fact that humans are unique, and that we might all solve the same task in a different way. An important area in robot development, therefore, is to teach robots how to work alongside humans in dynamic environments,” says Maximilian Diehl, Doctoral Student at the Department of Electrical Engineering at Chalmers University of Technology and main researcher behind the project.

When humans carry out a simple task, such as setting a table, we might approach the challenge in several different ways, depending on the conditions. If a chair unexpectedly stands in the way, we could choose to move it or walk around it. We alternate between using our right and left hands, we take pauses, and perform any number of unplanned actions.

With this month’s release of the F-150 Lightning, Ford hopes to electrify new and traditional truck buyers alike, and—eventually—to replace its industry-defining gas-powered line.

A team of researchers at Stanford University, working with a colleague at the Chinese Academy of Sciences, has built an AI-based filtration system to remove noise from seismic sensor data in urban areas. In their paper published in the journal Science Advances, the group describes training their application and testing it against real data from a prior seismic event.

In order to provide advance warning when an earthquake is detected, scientists have placed seismometers in earthquake-prone areas, including urban areas where quakes do the most damage and harm or kill the most people. But seismologists have found it troublesome to sort out seismic data related to natural ground movements from data related to city life. They note that human activities in cities, such as vehicles and trains, produce a lot of seismic noise. In this new effort, the researchers developed a deep learning application that determines which seismic data is natural and which is man-made and filters out those that are non-natural.

The researchers call their new application UrbanDenoiser. It was built using a deep-learning application and trained on 80,000 samples of urban seismic noise along with 33,751 samples from recorded natural seismic activity. The team applied their filtering system to seismic data recorded in Long Beach, California, to see how well it worked. They found it improved the level of desired signals compared to background noise by approximately 15 decibels. Satisfied with the results, they used UrbanDenoiser to analyze data from an earthquake that struck a nearby area in 2014. They found the application was able to detect four times the amount of data compared to the sensors without the filtering.