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The world of technology is rapidly shifting from flat media viewed in the third person to immersive media experienced in the first person. Recently dubbed “the metaverse,” this major transition in mainstream computing has ignited a new wave of excitement over the core technologies of virtual and augmented reality. But there is a third technology area known as telepresence that is often overlooked but will become an important part of the metaverse.

While virtual reality brings users into simulated worlds, telepresence (also called telerobotics) uses remote robots to bring users to distant places, giving them the ability to look around and perform complex tasks. This concept goes back to science fiction of the 1940s and a seminal short story by Robert A. Heinlein entitled Waldo. If we combine that concept with another classic sci-fi tale, Fantastic Voyage (1966), we can imagine tiny robotic vessels that go inside the body and swim around under the control of doctors who diagnose patients from the inside, and even perform surgical tasks.

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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.

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At the time climate change was only beginning to be talked about in the scientific community as well as behind the scenes among researchers working for fossil fuel companies.

Climate change fit the EPA’s mandate. And unlike an oil or chemical spill, no reputable scientist would see climate change as equivalent to “the crisis of the day.” But this phrase appears in Chief Justice John Roberts’ opinion justifying the decision in West Virginia v. EPA to deny the Agency its power to regulate carbon emissions from coal-fired power plants which based on the mandated powers described above is its purview (see points 3, 4, and 5).

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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.

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