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A team of DESY scientists has built a miniature double particle accelerator that can recycle some of the laser energy fed into the system to boost the energy of the accelerated electrons a second time. The device uses narrowband terahertz radiation which lies between infrared and radio frequencies in the electromagnetic spectrum, and a single accelerating tube is just 1.5 centimetres long and 0.79 millimetres in diameter. Dongfang Zhang and his colleagues from the Center for Free-Electron laser Science (CFEL) at DESY present their experimental accelerator in the journal Physical Review X.

The miniature size of the device is possible due to the short wavelength of terahertz radiation. “Terahertz-based accelerators have emerged as promising candidates for next-generation compact electron sources,” explains Franz Kärtner, Lead Scientist at DESY and head of the CFEL group that built the device. Scientists have successfully experimented with terahertz accelerators before, which could enable applications where large particle accelerators are just not feasible or necessary. “However, the technique is still in an early stage, and the performance of experimental terahertz accelerators has been limited by the relatively short section of interaction between the terahertz pulse and the electrons,” says Kärtner.

For the new device, the team used a longer pulse comprising many cycles of terahertz waves. This multicycle pulse significantly extends the interaction section with the particles. “We feed the multicycle terahertz pulse into a waveguide that is lined with a dielectric material”, says Zhang. Within the waveguide, the pulse’s speed is reduced. A bunch of electrons is shot into the central part of the waveguide just in time to travel along with the pulse. “This scheme increases the interaction region between the terahertz pulse and the electron bunch to the centimetre range—compared to a few millimetres in earlier experiments,” reports Zhang.

A sailor aboard a U.S. Navy ship has returned “presumptive positive” test results for coronavirus, in what is the first instance of a coronavirus case for a sailor aboard one of the service’s ships.

After a prolonged winter, artificial intelligence is experiencing a scorching summer mainly thanks to advances in deep learning and artificial neural networks. To be more precise, the renewed interest in deep learning is largely due to the success of convolutional neural networks (CNNs), a neural network structure that is especially good at dealing with visual data.

But what if I told you that CNNs are fundamentally flawed? That was what Geoffrey Hinton, one of the pioneers of deep learning, talked about in his keynote speech at the AAAI conference, one of the main yearly AI conferences.

Kimberlite rock samples are a mainstay of diamond exploration. Formed millions of years ago at depths of 150 to 400 kilometres, kimberlites are brought to the surface by geological and chemical forces. Sometimes, the igneous rocks carry diamonds embedded within them.

“For researchers, kimberlites are subterranean rockets that pick up passengers on their way to the surface,” explains University of British Columbia geologist Maya Kopylova. “The passengers are solid chunks of wall rocks that carry a wealth of details on conditions far beneath the surface of our planet over time.”

But when Kopylova and colleagues began analyzing samples from a De Beers Chidliak Kimberlite Province property in southern Baffin Island, it became clear the wall rocks were very special. They bore a mineral signature that matched other portions of the North Atlantic craton—an ancient part of Earth’s continental crust that stretches from Scotland to Labrador.

Two retired professors are sharing the mathematics version of the Nobel Prize for their lifelong contributions to the changing nature of math in the computing age. Both Hillel Furstenberg and Gregory Margulis spent decades applying ideas from probability theory to different kinds of discrete mathematics in order to shake loose new ways to solve seemingly intractable problems. The Abel Prize, awarded since just 2003, honors career mathematical accomplishments with a prize of about $700,000.

Wait—there’s not a Nobel Prize for mathematics? It’s true, and although you may have heard a lascivious story to explain why, no one really knows for sure.

Obstacle avoidance is a crucial piece of technology for drones, but commercially-available systems just aren’t fast enough for some situations. Now, engineers at the University of Zurich have developed a new system that gives drones such fast reflexes that they can play – and win at – dodgeball.

According to the researchers, most current obstacle avoidance systems take about 20 to 40 milliseconds to process changes in their surroundings. That’s fine for a drone gently approaching a building and finding its way inside, but it’s no match for fast-moving obstacles like birds or other drones. That makes navigation a problem in certain situations, like when there are a lot of drones together or in dynamic environments like disaster zones, or when a drone just needs to move fast.

Continue reading “Drone plays dodgeball to demo fast new obstacle detection system” »

An injection of magma under Norris Geyser Basin may be why the region is five inches higher today than it was 20 years ago.

STAT1-deficient mice are more susceptible to infection with severe acute respiratory syndrome coronavirus (SARS-CoV) than type I interferon (IFN) receptor-deficient mice. We used mice lacking functional receptors for both type I and type III IFN (double knockout, dKO) to evaluate the possibility that type III IFN plays a decisive role in SARS-CoV protection. We found that viral peak titres in lungs of dKO and STAT1-deficient mice were similar, but significantly higher than in wild-type mice. The kinetics of viral clearance from the lung were also comparable in dKO and STAT1-deficient mice. Surprisingly, however, infected dKO mice remained healthy, whereas infected STAT1-deficient mice developed liver pathology and eventually succumbed to neurological disease. Our data suggest that the failure of STAT1-deficient mice to control initial SARS-CoV replication efficiently in the lung is due to impaired type I and type III IFN signalling, whereas the failure to control subsequent systemic viral spread is due to unrelated defects in STAT1-deficient mice.

Seeking a shortcut to treatment for the novel coronavirus pandemic, Sanofi and Regeneron spied promising results in severe patients with their shared arthritis med Kevzara. Now, they’re hustling the med into immediate clinical trials to put that promise to the test.

Sanofi and Regeneron are ready to enroll a phase 2/3 clinical program studying arthritis med Kevzara as a therapy for patients hospitalized with severe COVID-19, Sanofi said Monday.

In a two-part U.S. arm of the Kevzara program, the drugmakers will evaluate the drug as an add-on to supportive care in around 400 patients across 16 states. The first segment of the trial will study Kevzara’s impact on fever and patients’ need for supplemental oxygen while a second segment will focus on longer-term outcomes, including preventing death and cutting the need for supportive care such as mechanical ventilation, supplemental oxygen and/or hospitalization, the partners said.