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Categories: Computer Science: General, Physics: General

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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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An international research team has succeeded in developing a new type of material in the rather young research field of covalent organic frameworks. The new two-dimensional polymer is characterized by the fact that its properties can be controlled in a targeted and reversible manner. This has brought the researchers a step closer to the goal of realizing switchable quantum states.

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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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Scientists address decades-long problem in the field of single-molecule fluorescence resonance energy transfer, paving the way for more accurate experiments.

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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.

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Data assimilation is an important mathematical discipline in earth sciences, particularly in numerical weather prediction (NWP). However, conventional data assimilation methods require significant computational resources. To address this, researchers developed a novel method to solve data assimilation on quantum computers, significantly reducing the computation time. The findings of the study have the potential to advance NWP systems and will inspire practical applications of quantum computers for advancing data assimilation.

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A team has developed printable, highly efficient light-emitting metasurfaces.

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To understand the unique physical phenomena associated with the properties of magnetic hedgehogs and antihedgehogs, which behave as virtual magnetic monopoles and antimonopoles respectively, it is essential to study their intrinsic excitations. In a new study, researchers revealed the dynamical nature of collective excitation modes in hedgehog lattices in itinerant chiral magnets. Their findings serve as the foundation for studying the dynamics of emergent magnetic monopoles in magnets.

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Scientists have developed an artificial intelligence (AI)-powered system to track tiny devices that monitor markers of disease in the gut. Devices using the novel system may help at-risk individuals monitor their gastrointestinal (GI) tract health at home, without the need for invasive tests in hospital settings.

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Researchers have demonstrated a new method that leverages artificial intelligence (AI) and computer simulations to train robotic exoskeletons to autonomously help users save energy while walking, running and climbing stairs.

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In the largest study yet of working-from-home professionals, economists reveal that employees who work from home two days a week are just as productive, likely to get promoted, and far less prone to quit.

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The exhaust heat generated by a fusing plasma in a commercial-scale reactor may not be as damaging to the vessel's innards as once thought, according to new research about escaping plasma particles.