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Read more about A 107-year-old Einstein theory about how the universe began, Which is 100% correct.

The earliest dinosaurs included carnivorous, omnivorous and herbivorous species, according to a team of University of Bristol paleobiologists.

By looking at the tooth shapes of the earliest dinosaurs and simulating their tooth function with computational modeling, experts were able to compare them to living reptiles and their diets. Their findings, published December 16 in Science Advances, show that many groups of plant-eating dinosaurs were ancestrally omnivorous and that the ancestors of our famous long-necked herbivores, such as Diplodocus, ate meat. This ability to diversify their diets early in their evolution likely explains their evolutionary and ecological success.

The earliest dinosaurs are enigmatic: they were much smaller than their later relatives and for most of the Triassic they were in the shadow of the crocodile-like reptiles. It is unknown how diverse they were in terms of diets and ecology, but scientists know something must have happened in the Triassic that allowed dinosaurs to endure the Triassic–Jurassic mass extinction and adapt in its aftermath, becoming the dominant group for the rest of the Mesozoic.

In a major advance, scientists have found a new and groundbreaking way to force electrons to flow only in one direction in a superconductor.

The large-scale structure (LSS) of the Universe is obviously nonlinear and very complicated. However, the scale of onset of nonlinearity is well separated from the size of the Universe which makes a large portion of the structure formation modes accessible to perturbation theory (PT). The latter is itself complicated by the time dependence of the lambdaCDM background. The authors provide an exact all-order recursive solution for the PT kernels, which allows them to go beyond the Einstein-de Sitter approximation for the time dependence, and quantify the deviation at the two-loop level in the 10% range, a deviation detectible with upcoming observations.

Water has puzzled scientists for decades. For the last 30 years or so, they have theorized that when cooled down to a very low temperature like-100C, water might be able to separate into two liquid phases of different densities. Like oil and water, these phases don’t mix and may help explain some of water’s other strange behavior, like how it becomes less dense as it cools.

It’s almost impossible to study this phenomenon in a lab, though, because water crystallizes into ice so quickly at such low temperatures. Now, new research from the Georgia Institute of Technology uses machine learning models to better understand water’s phase changes, opening more avenues for a better theoretical understanding of various substances. With this technique, the researchers found strong computational evidence in support of water’s liquid-liquid transition that can be applied to real-world systems that use water to operate.

“We are doing this with very detailed quantum chemistry calculations that are trying to be as close as possible to the real physics and physical chemistry of water,” said Thomas Gartner, an assistant professor in the School of Chemical and Biomolecular Engineering at Georgia Tech. “This is the first time anyone has been able to study this transition with this level of accuracy.”

The relentless pursuit of success is valorised in our culture, but taking the long way around is often the best by Cody Delistraty + BIO.

In spin-based quantum processors, each quantum dot of a qubit is populated by exactly one electron, which requires careful tuning of each gate voltage such that it lies inside the charge-stability region (the “Coulomb diamond’‘) associated with the dot array. However, mapping the boundary of a multidimensional Coulomb diamond by traditional dense raster scanning would take years, so the authors develop a sparse acquisition technique that autonomously learns Coulomb-diamond boundaries from a small number of measurements. Here we have hardware-triggered line searches in the gate-voltage space of a silicon quadruple dot, with smart search directions proposed by an active-learning algorithm.

Astronomers searching for radio signals that could be signs of extraterrestrial life have just gained access to South Africa’s MeerKAT telescope.

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