China’s space station is almost complete after the country launched one out of the two modules left.
China’s space agency continues to make steady progress as the country aims to become one of the world’s leading space powers.
On Sunday, July 24, it launched one of the two laboratory modules required to complete its orbital space station, a press release reveals.
The monstrous people spiral of a nearby galaxy is just one of many Webb telescope images to come over the coming days, weeks, and years.
The new James Webb Space Telescope image shows NGC 628 as a swirling, dusty skeleton more like something from a Marvel movie than a spiral galaxy.
In an interview with The Independent, Gabriel Brammer, one of the researchers at the Cosmic Dawn Center at the Niels Bohr Institute at the University of Copenhagen, said the galaxy looks like our own Milky Way.
Most ancient astronomers have used tables and graphs that describe celestial bodies’ relative positions, depending on the time of year. The idea of describing the motion of planets in the form of a geometric line with the area under the curve equal to the distance traveled by a celestial body is truly innovative. This is essentially an idea that led to integral calculus.
The researcher of the five tablets knew that four of them involved astronomical calculations, but he wasn’t sure until he got a picture of the fifth. After reading them, it became clear that they contained instructions for predicting the motion of Jupiter using the geometric principle by constructing a trapezoidal figure. The finished “product” of their studies is what we now call the Babylonian Map of Jupiter.
The inscriptions on the five tablets show that the Babylonian astronomers measured the estimated daily speed of Jupiter, taking into account the position of the planet on different days. They then used speed and time to calculate the distance they would travel over a period of time, i.e., their calculations are equivalent to the geometric dependence of velocity on time and distance.
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The Moon isn’t necessarily there if you don’t look at it. So says quantum mechanics, which states that what exists depends on what you measure. Proving reality is like that usually involves the comparison of arcane probabilities, but physicists in China have made the point in a clearer way. They performed a matching game in which two players leverage quantum effects to win every time—which they can’t if measurements merely reveal reality as it already exists.
“To my knowledge this is the simplest [scenario] in which this happens,” says Adan Cabello, a theoretical physicist at the University of Seville who spelled out the game in 2001. Such quantum pseudotelepathy depends on correlations among particles that only exist in the quantum realm, says Anne Broadbent, a quantum information scientist at the University of Ottawa. “We’re observing something that has no classical equivalent.”
A quantum particle can exist in two mutually exclusive conditions at once. For example, a photon can be polarized so that the electric field in it wriggles vertically, horizontally, or both ways at the same time—at least until it’s measured. The two-way state then collapses randomly to either vertical or horizontal. Crucially, no matter how the two-way state collapses, an observer can’t assume the measurement merely reveals how the photon was already polarized. The polarization emerges only with the measurement.
The design of protein sequences that can precisely fold into pre-specified 3D structures is a challenging task. A recently proposed deep-learning algorithm improves such designs when compared with traditional, physics-based protein design approaches.
ABACUS-R is trained on the task of predicting the AA at a given residue, using information about that residue’s backbone structure, and the backbone and AA of neighboring residues in space. To do this, ABACUS-R uses the Transformer neural network architecture6, which offers flexibility in representing and integrating information between different residues. Although these aspects are similar to a previous network2, ABACUS-R adds auxiliary training tasks, such as predicting secondary structures, solvent exposure and sidechain torsion angles. These outputs aren’t needed during design but help with training and increase sequence recovery by about 6%. To design a protein sequence, ABACUS-R uses an iterative ‘denoising’ process (Fig.
