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Categories: Computer Science: General, Physics: Quantum Physics

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Scientists predict the existence of flat electronic bands at the Fermi level, a finding that could enable new forms of quantum computing and electronic devices.

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Computers have come so far in terms of their power and potential, rivaling and even eclipsing human brains in their ability to store and crunch data, make predictions and communicate. But there is one domain where human brains continue to dominate: energy efficiency.

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Researchers developed an artificial intelligence (AI) model that could help electrical grids prevent power outages by automatically rerouting electricity in milliseconds. The approach is an early example of 'self-healing grid' technology, which uses AI to detect and repair problems such as outages autonomously and without human intervention when issues occur, such as storm-damaged power lines.

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The international collaboration presented their first results with new data in four years, featuring a new low-energy sample of electron neutrinos and a dataset doubled in size.

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Looking under the microscope, a group of cells slowly moves forward in a line, like a train on the tracks. The cells navigate through complex environments. A new approach now shows how they do this and how they interact with each other.

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A new technique can model an entire 3D scene, including areas hidden from view, from just one camera image. The method relies on image shadows, which provide information about the geometry and location of hidden objects.

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The potential of quantum computers is currently thwarted by a trade-off problem. Quantum systems that can carry out complex operations are less tolerant to errors and noise, while systems that are more protected against noise are harder and slower to compute with. Now a research team has created a unique system that combats the dilemma, thus paving the way for longer computation time and more robust quantum computers.

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A new way to teach artificial intelligence (AI) to understand human line drawings -- even from non-artists -- has been developed.

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A recently developed wirelessly powered 5G relay could accelerate the development of smart factories, report scientists. By adopting a lower operating frequency for wireless power transfer, the proposed relay design solves many of the current limitations, including range and efficiency. In turn, this allows for a more versatile and widespread arrangement of sensors and transceivers in industrial settings.

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Scientists have proposed a new AI method in which systems of habitual and goal-directed behaviors learn to help each other. Through computer simulations that mimicked the exploration of a maze, the method quickly adapts to changing environments and also reproduced the behavior of humans and animals after they had been accustomed to a certain environment for a long time. The study not only paves the way for the development of systems that adapt quickly and reliably in the burgeoning field of AI, but also provides clues to how we make decisions in the fields of neuroscience and psychology.

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Researchers have developed innovative, eco-friendly quantum materials that can drive the transformation of methanol into ethylene glycol. This discovery opens up new possibilities for using eco-friendly materials in photocatalysis, paving the way for sustainable chemical production.

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Using computer-assisted neural networks, researchers have been able to accurately identify affective states from the body language of tennis players during games. For the first time, they trained a model based on artificial intelligence (AI) with data from actual games. Their study demonstrates that AI can assess body language and emotions with accuracy similar to that of humans. However, it also points to ethical concerns.

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Understanding organismal traits and learning how they evolve and adapt to different environments is crucial for biologists and the battle against biodiversity loss. To be truly efficient, however, the researchers need to use huge amounts of data, including physical traits and DNA. Furthermore, those different data types need to be accurately linked to each other, so that computers and next-age AI technology can correctly process it. Currently, this process of accurate linking is extremely difficult and largely inefficient. To solve this problem, researchers created a brand new computer language called Phenoscript.

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Researchers developed software that can simulate the growth of field crops. To do this, they fed thousands of photos from field experiments into a learning algorithm. This enabled the algorithm to learn how to visualize the future development of cultivated plants based on a single initial image. Using the images created during this process, parameters such as leaf area or yield can be estimated accurately.

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Researchers carried out a pioneering experiment where they measured the effect of the rotation of Earth on quantum entangled photons. The work represents a significant achievement that pushes the boundaries of rotation sensitivity in entanglement-based sensors, potentially setting the stage for further exploration at the intersection between quantum mechanics and general relativity.

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A technique can plan a trajectory for a robot using only language-based inputs. While it can't outperform vision-based approaches, it could be useful in settings that lack visual data to use for training.

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Spontaneous parametric down-conversion (SPDC), as a source of entangled photons, is of great interest for quantum physics and quantum technology, but so far it could be only implemented in solids. Researchers have demonstrated, for the first time, SPDC in a liquid crystal. The results open a path to a new generation of quantum sources: efficient and electric-field tunable.

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Researchers have made a significant breakthrough in how to enable and exploit ultra-fast spin behavior in ferromagnets. The research paves the way for ultra-high frequency applications.

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Researchers have experimentally generated high-density relativistic electron-positron pair-plasma beams by producing two to three orders of magnitude more pairs than previously reported.

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Researchers have developed a technique that allows artificial intelligence (AI) programs to better map three-dimensional spaces using two-dimensional images captured by multiple cameras. Because the technique works effectively with limited computational resources, it holds promise for improving the navigation of autonomous vehicles.