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Category: space

Not all appears as it would seem in the Whirlpool galaxy. One of the best-studied spiral galaxies and a delight to amateur astronomers, Messier 51, as it’s officially named, is influenced by powerful, invisible forces.

Located 31 million light-years away in the constellation Canes Venatici, the galaxy’s arms are strikingly visible as they reach out along the central spine structure, displaying swirling clouds of gas and dust that are massive star-making factories. But new observations by NASA ’s Stratospheric Observatory for Infrared Astronomy, or SOFIA, presented at this week’s 237th meeting of the American Astronomical Society, shows a more complicated picture.

Radio telescopes previously detected neatly-drawn magnetic fields throughout the length of the galaxy’s massive arms. But under SOFIA’s infrared gaze for the first time those lines give way to a chaotic scene in the outer spiral arms. Using a far-infrared camera and imaging polarimeter instrument called the High-Resolution Airborne Wideband Camera, or HAWC+, researchers found that the magnetic fields in the outskirts of the galaxy no longer follow the spiral structure and are instead distorted.

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InSight lander’s “mole” was unable to hammer through the Martian soil, and unusually dusty solar panels meant the robot was generating less power.

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Remember the Hubble Deep Field? And its successor the Hubble Ultra Deep Field? We sure do here at Universe Today. How could we forget them?

Well, just as the Hubble Space Telescope has successors, so do two of its most famous images. And those successors will come from one of Hubble’s successors, NASA’s Roman Space Telescope.

The Hubble Deep Field and Ultra Deep Field showed a generation of people how expansive and wondrous the Universe is. They showed that even empty-looking patches of sky are, in fact, full of galaxies. All sizes, shapes, and ages of galaxies.

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The Chinese capital may be the most well-positioned to help China’s satellite internet ambitions. Ultimate Blue Nebula’s Lan said private satellite manufacturers and space transport providers based in Beijing could make up as much as 80 per cent of the nascent Chinese satellite internet industry’s overall supply chain.

The Chinese capital is already home to a comprehensive supply chain for satellite manufacturers and space transport providers.

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Circa 2019 😃

The La Moto Volante from French company Lazareth demonstrated its first stable hover. NASA’s helicopter that will fly on Mars has passed its flight tests. And Boston Dynamics’ upgraded Handle robot is a champ at warehouse Tetris.

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On the path to writing his Ph.D. dissertation, Lucio Milanese made a discovery—one that refocused his research, and will now likely dominate his thesis.

Milanese studies , a gas-like flow of ions and electrons that comprises 99 percent of the visible universe, including the Earth’s ionosphere, interstellar space, the , and the environment of stars. Plasmas, like other fluids, are often found in a turbulent state characterized by chaotic, unpredictable motion, providing multiple challenges to researchers who seek to understand the cosmic universe or hope to harness burning plasmas for fusion energy.

Milanese is interested in what physicist Richard Feynman called “the most important unsolved problem of classical physics”—turbulence. In this case, the focus is plasma turbulence, its nature and structure.

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Over the past decade or so, deep neural networks have achieved very promising results on a variety of tasks, including image recognition tasks. Despite their advantages, these networks are very complex and sophisticated, which makes interpreting what they learned and determining the processes behind their predictions difficult or sometimes impossible. This lack of interpretability makes deep neural networks somewhat untrustworthy and unreliable.

Researchers from the Prediction Analysis Lab at Duke University, led by Professor Cynthia Rudin, have recently devised a technique that could improve the interpretability of deep neural networks. This approach, called whitening (CW), was first introduced in a paper published in Nature Machine Intelligence.

“Rather than conducting a post hoc analysis to see inside the hidden layers of NNs, we directly alter the NN to disentangle the latent space so that the axes are aligned with known concepts,” Zhi Chen, one of the researchers who carried out the study, told Tech Xplore. “Such disentanglement can provide us with a much clearer understanding of how the network gradually learns concepts over layers. It also focuses all the information about one concept (e.g., “lamp,” “bed,” or “person”) to go through only one neuron; this is what is meant by disentanglement.”

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