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Categories: Mathematics: Modeling, Physics: Quantum Computing

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A new artificial intelligence model can predict how different proteins may bind to DNA.

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Machine learning is a powerful tool in computational biology, enabling the analysis of a wide range of biomedical data such as genomic sequences and biological imaging. But when researchers use machine learning in computational biology, understanding model behavior remains crucial for uncovering the underlying biological mechanisms in health and disease. Researchers now propose guidelines that outline pitfalls and opportunities for using interpretable machine learning methods to tackle computational biology problems.

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Scientists at three research institutions capture the pulsing motion of atoms in diamond, uncovering the relationship between the diamond's strain and the behavior of the quantum information hosted within.

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To protect the Amazon and support the wellbeing of its people, its economy needs to shift from environmentally harmful production to a model built around the diversity of indigenous and rural communities, and standing forests.

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With GROVER, a new large language model trained on human DNA, researchers could now attempt to decode the complex information hidden in our genome. GROVER treats human DNA as a text, learning its rules and context to draw functional information about the DNA sequences.

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Thermometer, a new calibration technique tailored for large language models, can prevent LLMs from being overconfident or underconfident about their predictions. The technique aims to help users know when a model should be trusted.

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Scientists have hypothesized that moir excitons -- electron-hole pairs confined in moir interference fringes which overlap with slightly offset patterns -- may function as qubits in next-generation nano-semiconductors. However, due to diffraction limits, it has not been possible to focus light enough in measurements, causing optical interference from many moir excitons. To solve this, researchers have developed a new method of reducing these moir excitons to measure the quantum coherence time and realize quantum functionality.

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A new study places the origin of the Imazighen in the Epipaleolithic, more than twenty thousand years ago. The research concludes that the genetic origin of the current Arab population of north Africa is far more recent than previously believed, placing it in the seventh century AD. The team has designed an innovative demographic model that uses artificial intelligence to analyze the complete genomes of the two populations.

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Clean energy investments offer potential stability and growth, especially during volatile market conditions. A recent study explored the relationship between clean energy markets and global stock markets. Significant spillovers were observed from major indices like the SP500 to markets such as Japan's Nikkei225 and Global Clean Energy Index. These interactions suggest opportunities for optimizing investment portfolios and leveraging clean energy assets as hedging tools in volatile market environments.

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Researchers have designed a protocol for harnessing the power of quantum sensors. The protocol could give sensor designers the ability to fine-tune quantum systems to sense signals of interest, creating sensors that are vastly more sensitive than traditional sensors.

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A team developed a novel method to successfully visualise electron-hole crystals in an exotic quantum material. Their breakthrough could pave the way for new advancements in computing technologies, including in-memory and quantum computing.

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Researchers have found that training successive generations of generative artificial intelligence models on synthetic data gives rise to self-consuming feedback loops.

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Researchers have developed a means to realize cold-atom integrated nanophotonic circuits.

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A new generation of specialty optical fibers has been developed by physicists to cope with the challenges of data transfer expected to arise in the future age of quantum computing.

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Researchers argue that, in some situations where machine-learning models are used to allocate scarce resources or opportunities, randomizing decisions in a structured way may lead to fairer outcomes.

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Scientists have created the first-ever atomic movies showing how atoms rearrange locally within a quantum material as it transitions from an insulator to a metal. With the help of these movies, the researchers discovered a new material phase that settles a years-long scientific debate and could facilitate the design of new transitioning materials with commercial applications.

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The key to developing quantum electronics may have a few kinks. According to researchers, that's not a bad thing when it comes to the precise control needed to fabricate and operate such devices, including advanced sensors and lasers. The researchers fabricated a switch to turn on and off the presence of kink states, which are electrical conduction pathways at the edge of semiconducting materials.

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In a scientific breakthrough, an international research team has developed a quantum sensor capable of detecting minute magnetic fields at the atomic length scale. This pioneering work realizes a long-held dream of scientists: an MRI-like tool for quantum materials.

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Using two optically trapped glass nanoparticles, researchers observed a novel collective Non-Hermitian and nonlinear dynamic driven by nonreciprocal interactions. This contribution expands traditional optical levitation with tweezer arrays by incorporating the so called non-conservative interactions.

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Tackling a long-time mystery, scientists have found that the turbulent movements of air in clouds play a key role in the growth of water droplets and the initiation of rain. The research can improve computer model simulations of weather and climate and ultimately lead to better forecasts.