A spider-like moon rover heading to the lunar surface in 2021 is designed to explore the underground lava tubes in which astronauts might one day live.
Category: space – Page 605
InSight lander’s “mole” was unable to hammer through the Martian soil, and unusually dusty solar panels meant the robot was generating less power.
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.
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.
NASA has finally given up on heat probe instrument on it Mars surface lander. Maybe Mark Watney will eventually find a way to fix it!
NASA has finally given up on the heat probe instrument on its Mars surface lander.
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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# OnThisDay 1979: On the 10th anniversary of the first moon landing, James Burke went to NASA, to reflect on the heart-stopping minutes preceding “The eagle has landed.”
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 plasma, a gas-like flow of ions and electrons that comprises 99 percent of the visible universe, including the Earth’s ionosphere, interstellar space, the solar wind, 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.
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 concept 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.”
Scientists think they may have spied the universe’s “gravitational wave background” after more than a decade of searching.