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Categories: Computer Science: General, Space: Cosmology

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The central question in the ongoing hunt for dark matter is: what is it made of? One possible answer is that dark matter consists of particles known as axions. A team of astrophysicists has now shown that if dark matter consists of axions, it may reveal itself in the form of a subtle additional glow coming from pulsating stars.

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A team of computer scientists has created a neural network that can explain how it reaches its predictions. The work reveals what accounts for the functionality of neural networks--the engines that drive artificial intelligence and machine learning--thereby illuminating a process that has largely been concealed from users.   

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Whether you’re battling foes in a virtual arena or collaborating with colleagues across the globe, lag-induced disruptions can be a major hindrance to seamless communication and immersive experiences. That’s why researchers have developed new technology to make data transfer over optical fiber communication faster and more efficient.

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An international team of researchers has developed a new theoretical framework that bridges physics and biology to provide a unified approach for understanding how complexity and evolution emerge in nature. This new work on 'Assembly Theory' represents a major advance in our fundamental comprehension of biological evolution and how it is governed by the physical laws of the universe.

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Big data and artificial intelligence (AI) are being used to model hidden patterns in nature, not just for one bird species, but for entire ecological communities across continents. And the models follow each species’ full annual life cycle, from breeding to fall migration to non-breeding grounds, and back north again during spring migration.

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For reasons of food security and economic incentive, farmers continuously seek to maximize their marketable crop yields. As plants grow inconsistently, at the time of harvesting, there will inevitably be variations in quality and size of individual crops. Finding the optimal time to harvest is therefore a priority for farmers. A new approach making heavy use of drones and artificial intelligence demonstrably improves this estimation by carefully and accurately analyzing individual crops to assess their likely growth characteristics.

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Researchers developed the first AI to date that can intelligently design robots from scratch by compressing billions of years of evolution into mere seconds. It's not only fast but also runs on a lightweight computer and designs wholly novel structures from scratch — without human-labeled, bias-filled datasets.

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In the James Webb Space Telescope’s (JWST) first images of the universe’s earliest galaxies, the young galaxies appear too bright, too massive and too mature to have formed so soon after the Big Bang. Using new simulations, a team of astrophysicists now has discovered that these galaxies likely are not so massive after all. Although a galaxy’s brightness is typically determined by its mass, the new findings suggest that less massive galaxies can glow just as brightly from irregular, brilliant bursts of star formation.

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In recent years, astronomy has seen itself in a bit of crisis: Although we know that the Universe expands, and although we know approximately how fast, the two primary ways to measure this expansion do not agree. Now astrophysicists suggest a novel method which may help resolve this tension.

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At the core of uncovering extreme events such as floods is the physics of fluids – specifically turbulent flows. Researchers leveraged a computer-vision deep learning technique and adapted it for nonlinear analysis of extreme events in wall-bounded turbulent flows, which are pervasive in numerous physics and engineering applications and impact wind and hydrokinetic energy, among others. Results show the technique employed can be invaluable for accurately identifying the sources of extreme events in a completely data-driven manner.

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For the first time, in a unique laboratory experiment at CERN, researchers have observed individual atoms of antihydrogen fall under the effects of gravity. In confirming antimatter and regular matter are gravitationally attracted, the finding rules out gravitational repulsion as the reason why antimatter is largely missing from the observable universe.

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Scientists unveil exciting possibilities for the development of highly efficient quantum devices.

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Researchers have developed a novel superconducting qubit architecture that can perform operations between qubits with much higher accuracy than scientists have yet been able to achieve. This architecture, which utilizes a relatively new type of superconducting qubit called fluxonium, is scalable and could be used to someday build a large-scale quantum computer.

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Astronomers have found a striking link between the amount of dust surrounding a supermassive black hole and the strength of the radio emission produced in extremely bright galaxies.

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Boosting virtual screening with machine learning allowed for a 10-fold time reduction in the processing of 1.56 billion drug-like molecules. Researchers teamed up with industry and supercomputers to carry out one of the world's largest virtual drug screens.

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With the launch of the James Webb Space Telescope, astronomers are now able to peer so far back in time that we are approaching the epoch where we think that the first galaxies were created. Throughout most of the history of the Universe, galaxies seemingly tend to follow a tight relation between how many stars they have formed, and how many heavy elements they have formed. But for the first time we now see signs that this relation between the amount of stars and elements does not hold for the earliest galaxies. The reason is likely that these galaxies simply are in the process of being created, and have not yet had the time to create the heavy elements.

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Galaxies from the early Universe are more like our own Milky Way than previously thought, flipping the entire narrative of how scientists think about structure formation in the Universe, according to new research.

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New physics-based self-learning machines could replace the current artificial neural networks and save energy.

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Newly developed ultra-low radiation cables reduce background noise for neutrino and dark matter detectors.

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Scientists have shown that their steerable lung robot can autonomously maneuver the intricacies of the lung, while avoiding important lung structures.