For the first time, researchers have found evidence that even microquasars with low-mass stars can efficiently accelerate particles.
Einstein’s theories support the science behind this time-travel loophole. But could it actually work?
OpenAI on Thursday said the U.S. National Laboratories will be using its latest artificial intelligence models for scientific research and nuclear weapons security.
Under the agreement, up to 15,000 scientists working at the National Laboratories may be able to access OpenAI’s reasoning-focused o1 series. OpenAI will also work with Microsoft, its lead investor, to deploy one of its models on Venado, the supercomputer at Los Alamos National Laboratory, according to a release. Venado is powered by technology from Nvidia and Hewlett-Packard Enterprise.
Constraining the origin of Earth’s building blocks requires knowledge of the chemical and isotopic characteristics of the source region(s) where these materials accreted. The siderophile elements Mo and Ru are well suited to investigating the mass-independent nucleosynthetic (i.e., “genetic”) signatures of material that contributed to the latter stages of Earth’s formation. Studies contrasting the Mo and Ru isotopic compositions of the bulk silicate Earth (BSE) to genetic signatures of meteorites, however, have reported conflicting estimates of the proportions of the non-carbonaceous type or NC (presumptive inner Solar System origin) and carbonaceous chondrite type or CC (presumptive outer Solar System origin) materials delivered to Earth during late-stage accretion (likely including the Moon-forming event and onwards).
A nonclassical quantum correlation with unprecedented strength is proposed and observed in a scalable optical platform.
Identifying driver regulators in cell stateions is key to decoding cellular function. Here, the authors present regX, an interpretable AI framework to prioritise potential driver TFs and cCREs from single-cell multiomics data, showing potential for understanding and manipulating cell states.
Identifying and characterizing secreted virulence proteins are fundamental for deciphering microbial pathogenicity. Here, the authors introduce a practical training framework to improve protein language model representations by integrating biological features and prior information through contrastive learning.
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