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Categories: Mathematics: Modeling, Physics: General

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Researchers introduce a new tool to measure bias in text-to-image AI generation models, which they have used to quantify bias in the state-of-the-art model Stable Diffusion.

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Scientists working on Fermilab's Muon g-2 experiment released the world's most precise measurement yet of the magnetic moment of the muon, bringing particle physics closer to the ultimate showdown between theory and experiment that may uncover new particles or forces.

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Bringing ultrafast physics to structural biology has revealed the dance of molecular 'coherence' in unprecedented clarity.

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Random telegraph noise (RTN) in semiconductors is typically caused by two-state defects. Two-dimensional (2D) van der Waals (vdW) layered magnetic materials are expected to exhibit large fluctuations due to long-range Coulomb interaction; importantly, which could be controlled by a voltage compared to 3D counterparts having large charge screening. Researchers reported electrically tunable magnetic fluctuations and RTN signal in multilayered vanadium-doped tungsten diselenide (WSe2) by using vertical magnetic tunneling junction devices. They identified bistable magnetic states in the 1/f2 RTNs in noise spectroscopy, which can be further utilized for switching devices via voltage polarity.

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Developing new materials requires significant time and labor, but some chemists are now hopeful that artificial intelligence (AI) could one day shoulder much of this burden. In a new study, a team prompted a popular AI model, ChatGPT, to perform one particularly time-consuming task: searching scientific literature. With that data, they built a second tool, a model to predict experimental results.

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Researchers have developed a new explainable artificial intelligence (AI) model to reduce bias and enhance trust and accuracy in machine learning-generated decision-making and knowledge organization.

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Researchers have unveiled an artificial intelligence-based model for computational imaging and microscopy without training with experimental objects or real data. The team introduced a self-supervised AI model nicknamed GedankenNet that learns from physics laws and thought experiments. Informed only by the laws of physics that universally govern the propagation of electromagnetic waves in space, the researchers taught their AI model to reconstruct microscopic images using only random artificial holograms -- synthesized solely from 'imagination' without relying on any real-world experiments, actual sample resemblances or real data.

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New study identifies concerning gaps between how human radiologists score the accuracy of AI-generated radiology reports and how automated systems score them. Researchers designed two novel scoring systems that outperform current automated systems that evaluate the accuracy of AI narrative reports. Reliable scoring systems that accurately gauge the performance of AI models are critical for ensuring that AI continues to improve and that clinicians can trust them.

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Deep learning methods have been used to augment existing energy-based physical models in 'do novo' or from-scratch computational protein design, resulting in a 10-fold increase in success rates verified in the lab for binding a designed protein with its target protein. The results will help scientists design better drugs against diseases like cancer and COVID-19.

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A new technique incorporates many different building blocks of cellular metamaterials into one unified graph-based representation. This can be used to make a user-friendly interface that can quickly and easily model metamaterials, edit the structures, and simulate their properties.

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An interdisciplinary team of mathematicians, engineers, physicists, and medical scientists has uncovered an unexpected link between pure mathematics and genetics, that reveals key insights into the structure of neutral mutations and the evolution of organisms.

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Scientists have trained a machine learning model in outer space, on board a satellite. This achievement could revolutionize the capabilities of remote-sensing satellites by enabling real-time monitoring and decision making for a range of applications.

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A new machine-learning technique can efficiently learn to control a robot, leading to better performance with fewer data.

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Enzymes play a key role in cellular metabolic processes. To enable the quantitative assessment of these processes, researchers need to know the so-called 'turnover number' (for short: kcat) of the enzymes. A team of bioinformaticians now describes a tool for predicting this parameter for various enzymes using AI methods.

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A team has combined economic theory with biology to understand how natural systems respond to change. The researchers noticed a similarity between consumers' shopping behavior and the behavior of metabolic systems, which convert food into energy in our bodies. The team focused on predicting how different metabolic systems might respond to environmental change by using an economic tool called the Slutsky equation. Their calculations indicated that very different metabolic systems actually share previously unknown universal properties, and can be understood using tools from other academic fields. Metabolic processes are used in drug development, bioengineering, food production and other industries, so being able to predict how such systems will respond to change can offer many benefits.

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Using Japan's most powerful supercomputer, researchers reproduced cases of clear air turbulence around Tokyo. They simulated the fine vortices responsible for this dangerous phenomenon. The usefulness of the simulation in predicting turbulence was confirmed by comparing simulation data with data from aircraft recordings. This research should improve the forecasting of turbulence.

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Structural engineers are familiar with seventeenth-century scientist Robert Hooke's theory that a hanging chain will mirror the shape of an upstanding rigid arch. However, new research now shows that this common-held belief is incorrect because, regardless of the similarities, the hanging chain and the arch are two incompatible mechanical systems.

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Board games based on numbers, like Monopoly, Othello and Chutes and Ladders, make young children better at math, according to a comprehensive review of research published on the topic over the last 23 years.

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The arrangement of electrons in matter, known as the electronic structure, plays a crucial role in fundamental but also applied research such as drug design and energy storage. However, the lack of a simulation technique that offers both high fidelity and scalability across different time and length scales has long been a roadblock for the progress of these technologies. Researchers have now pioneered a machine learning-based simulation method that supersedes traditional electronic structure simulation techniques. Their Materials Learning Algorithms (MALA) software stack enables access to previously unattainable length scales.

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A solution for temporal asymmetry -- or entropy production -- in thermodynamics has been developed to further our understanding of the behavior of biological systems, machine learning, and AI tools. The researchers worked on the time-irreversible Ising model dynamics caused by asymmetric connections between neurons.