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Categories: Geoscience: Geomagnetic Storms, Mathematics: Modeling

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The origin of heavy elements in our universe is theorized to be the result of neutron star collisions, which produce conditions hot and dense enough for free neutrons to merge with atomic nuclei and form new elements in a split-second window of time. Testing this theory and answering other astrophysical questions requires predictions for a vast range of masses of atomic nuclei. Scientists are using machine learning algorithms to successfully model the atomic masses of the entire nuclide chart -- the combination of all possible protons and neutrons that defines elements and their isotopes.

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Existing ship control systems using Model Predictive Control for Maritime Autonomous Surface Ships (MASS) do not consider the various forces acting on ships in real sea conditions. Addressing this gap, researchers developed a novel time-optimal control method, that accounts for the real wave loads acting on a ship, enabling effective planning and control of MASS at sea.

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Neural networks have been powering breakthroughs in artificial intelligence, including the large language models that are now being used in a wide range of applications, from finance, to human resources to healthcare. But these networks remain a black box whose inner workings engineers and scientists struggle to understand. Now, a team has given neural networks the equivalent of an X-ray to uncover how they actually learn.

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When you teach a child how to solve puzzles, you can either let them figure it out through trial and error, or you can guide them with some basic rules and tips. Similarly, incorporating rules and tips into AI training -- such as the laws of physics --could make them more efficient and more reflective of the real world. However, helping the AI assess the value of different rules can be a tricky task.

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A new safety-check technique can prove with 100 percent accuracy that a planned robot motion will not result in a collision. The method can generate a proof in seconds and does so in a way that can be easily verified by a human.

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A new mathematical model has shown, with great precision, the impact that physiological and psychological parameters have on running performance and provides tips for optimized training.

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Researchers have developed an artificial intelligence model that can accurately identify tumors and diseases in medical images. The tool draws a map to explain each diagnosis, helping doctors follow its line of reasoning, check for accuracy, and explain the results to patients.

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Human coexistence depends on cooperation. Individuals have different motivations and reasons to collaborate, resulting in social dilemmas, such as the well-known prisoner's dilemma. Scientists now present a new mathematical principle that helps to understand the cooperation of individuals with different characteristics.

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Medical summarization, a process that uses artificial intelligence (AI) to condense complex patient information, is currently used in health care settings for tasks such as creating electronic health records and simplifying medical text for insurance claims processing. While the practice is intended to create efficiencies, it can be labor-intensive, according researchers who created a new method to streamline the way AI creates these summaries, efficiently producing more reliable results.

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Fashioned from the same element found in sand and covered by intricate patterns, microchips power smartphones, augment appliances and aid the operation of cars and airplanes. Now, scientists are developing computer simulation codes that will outperform current simulation techniques and aid the production of microchips using plasma, the electrically charged state of matter also used in fusion research. These codes could help increase the efficiency of the manufacturing process and potentially stimulate the renaissance of the chip industry in the United States.

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Single-molecule magnets (SMMs) are exciting materials. In a recent breakthrough, researchers have used deep learning to predict SMMs from 20,000 metal complexes. The predictions were made solely based on the crystal structures of these metal complexes, thus eliminating the need for time-consuming experiments and complex simulations. As a result, this method is expected to accelerate the development of functional materials, especially for high-density memory and quantum computing devices.

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Researchers have developed a powerful new artificial intelligence model that can distinguish between male and female brains.

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Engineers have developed a new chip that uses light waves, rather than electricity, to perform the complex math essential to training AI. The chip has the potential to radically accelerate the processing speed of computers while also reducing their energy consumption.

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Engineers have studied how insects navigate, for the purpose of developing energy-efficient robots.

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In an image, estimating the distance between objects and the camera by using the blur in the images as clue, also known as depth from focus/defocus, is essential in computer vision. However, model-based methods fail when texture-less surfaces are present, and learning-based methods require the same camera settings during training and testing. Now, researchers have come up with an innovative strategy for depth estimation that combines the best of both the worlds to solve these limitations, extending the applicability of depth from focus/defocus.

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The past year has seen major advances in Large Language Models (LLMs) such as ChatGPT. The ability of these models to interpret and produce human text sources (and other sequence data) has implications for people in many areas of human activity. A new perspective paper argues that like many professionals, neuroscientists can either benefit from partnering with these powerful tools or risk being left behind.

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Researchers have developed a machine learning approach to compressing image data with greater accuracy than learning-free computation methods, with applications for retinal implants and other sensory prostheses.

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Scientists have developed a new approach to machine learning -- a form of artificial intelligence -- to identify drugs that help minimize harmful scarring after a heart attack or other injuries.

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The springtime emergence of vast swarms of cicadas can be explained by a mathematical model of collective decision-making with similarities to models describing stock market crashes.

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For decades scientists have been trying to solve Feynman's Sprinkler Problem: How does a sprinkler running in reverse work? Through a series of experiments, a team of mathematicians has figured out how flowing fluids exert forces and move structures, thereby revealing the answer to this long-standing mystery.