Control of the 6D (x,y,z,p x,p y,p z) phase space distribution of beams is important for all particle accelerators over a wide span of applications including beam - based imaging for material science, accelerator - based light sources, and plasma wakefield - based accelerators. At all large high intensity beam particle accelerators, such as the Los Alamos Neutron Science Center (LAN - SCE) [1] or the Spallation Neutron Source (SNS) [2], the beam's phase distribution must be controlled for proper acceleration and to prevent beam lo ss by matching to the accelerator's magnetic focusing lattice. At particle accelerator - based free electron laser (FEL) light sources, such as the Linac Coherent Light Source [3], the European X - ray FEL [4], and the Swiss FEL [5] the beam's phase space defi nes the properties of the generated light and must be adjusted between different experiments.

'Oppenheimer' swept away the competition at the 2024 Oscars, receiving seven awards including earning renowned director Christopher Nolan his first golden man statuette. While this is the filmmaker's first major award-winning film, he has been producing movies since 1998 when he made Following - and has made 10 more since. We asked ChatGPT to rank his other 11 films dating back to 26 years to the'Following' and 2010 film'Inception' up through his his 2012 film'The Dark Knight Rises' and his 2020 film'Tenet.' Renowned director Christopher Nolan took home his first Oscar for his critically acclaimed film, ' Oppenheimer.' The historic film starred Cillian Murphy as J Robert Oppenheimer, the director of the Los Alamos lab that designed and built the world's first atomic bomb during World War II - he is often known as the'father of the atomic bomb' Oppenheimer swept the box office when it was released on July 21, 2023, reeling in a whopping 82.4 million in opening weekend, winning Nolan Best Picture and Best Director during Sunday's award show.

Structural Health Monitoring (SHM) is vital for evaluating structural condition, aiming to detect damage through sensor data analysis. It aligns with predictive maintenance in modern industry, minimizing downtime and costs by addressing potential structural issues. Various machine learning techniques have been used to extract valuable information from vibration data, often relying on prior structural knowledge. This research introduces an innovative approach to structural damage detection, utilizing a new Convolutional Neural Network (CNN) algorithm. In order to extract deep spatial features from time series data, CNNs are taught to recognize long-term temporal connections. This methodology combines spatial and temporal features, enhancing discrimination capabilities when compared to methods solely reliant on deep spatial features. Time series data are divided into two categories using the proposed neural network: undamaged and damaged. To validate its efficacy, the method's accuracy was tested using a benchmark dataset derived from a three-floor structure at Los Alamos National Laboratory (LANL). The outcomes show that the new CNN algorithm is very accurate in spotting structural degradation in the examined structure.

On 16th July 1945 the world changed forever. The Manhattan Project's'Trinity' test, directed by Robert Oppenheimer, endowed humanity for the first time with the ability to wipe itself out: an atomic bomb had been successfully detonated 210 miles south of Los Alamos, New Mexico. On 6th August 1945 the bomb was dropped on Hiroshima and three days later, Nagasaki-- unleashing unprecedented destructive power. The end of World War II brought a fragile peace, overshadowed by this new, existential threat. While nuclear technology promised an era of abundant energy, it also launched us into a future where nuclear war could lead to the end of our civilization.

Malware is one of the most dangerous and costly cyber threats to national security and a crucial factor in modern cyber-space. However, the adoption of machine learning (ML) based solutions against malware threats has been relatively slow. Shortcomings in the existing ML approaches are likely contributing to this problem. The majority of current ML approaches ignore real-world challenges such as the detection of novel malware. In addition, proposed ML approaches are often designed either for malware/benign-ware classification or malware family classification. Here we introduce and showcase preliminary capabilities of a new method that can perform precise identification of novel malware families, while also unifying the capability for malware/benign-ware classification and malware family classification into a single framework.

IN2018, ON A TRIP to his ancestral homeland, Alexandr Wang listened as China's brightest engineers gave impressive presentations on artificial intelligence. He found it odd that the researchers conspicuously avoided any mention of how AI might be used. Wang, whose immigrant parents were nuclear physicists at Los Alamos National Laboratory, where the first atomic bombs were designed, was unsettled. "They were really dodgy on what the use cases were. You could tell it was for no good," recalls Wang, the cofounder of Scale AI, who has no second "e" in his first name so that it has eight characters, a number associated with good fortune in Chinese culture. Scale was then an up-and-coming startup providing data services primarily to self-driving auto-makers.

A team of scientists at Los Alamos National Laboratory is applying machine-learning algorithms to subsurface imaging that will impact a variety of applications, including energy exploration, carbon capture and sequestration and estimating pathways of subsurface contaminant transport, according to new research published in IEEE Signal Processing Magazine. "The subsurface is extremely complex and full of uncertainty, and knowledge of its physical properties is vital for a variety of applications," said Youzuo Lin of Los Alamos' Energy and Earth System Science group and lead author of the paper. "This paper is the first systematic survey on physics-guided machine-learning techniques for computational wave imaging." The authors reviewed more than a 100 research articles, organizing them within a structured framework that highlights the most significant recent innovations in this area. These insights will be of value not only for subsurface imaging, but also for other computational wave imaging problems such as medical ultrasound imaging and acoustic sensing for materials science. The process of obtaining subsurface data from surface measurements is called seismic inversion.

Melinda Snodgrass is the novelist and screenwriter best known for her classic Star Trek: The Next Generation script "The Measure of a Man." Her latest novel, Lucifer's War, pits an unlikely band of heroes against a horde of Lovecraftian monsters that have been spreading fear and ignorance throughout human history. "It's unbelievable now, the kind of nonsense people are accepting, that's being pushed on them by social media," Snodgrass says in Episode 529 of the Geek's Guide to the Galaxy podcast. "I really wanted to make a stand for science and rationality, as opposed to magic and superstition." The book is set in Snodgrass' home state of New Mexico, a place where science and superstition clash in a particularly striking way. "It's a very weird place, where you have Los Alamos laboratory, Sandia laboratories, high-tech, high-energy centers," Snodgrass says, "Some of the finest scientific minds in the world come here to lecture and study and commune with each other, and then on the other side you have people who will balance your aura and sell you a crystal to deal with your cancer."

The new approach allows scientists to better understand neural network behavior. Los Alamos National Laboratory researchers have developed a novel method for comparing neural networks that looks into the "black box" of artificial intelligence to help researchers comprehend neural network behavior. Neural networks identify patterns in datasets and are utilized in applications as diverse as virtual assistants, facial recognition systems, and self-driving vehicles. "The artificial intelligence research community doesn't necessarily have a complete understanding of what neural networks are doing; they give us good results, but we don't know how or why," said Haydn Jones, a researcher in the Advanced Research in Cyber Systems group at Los Alamos. "Our new method does a better job of comparing neural networks, which is a crucial step toward better understanding the mathematics behind AI." Researchers at Los Alamos are looking at new ways to compare neural networks.

"The artificial intelligence research community doesn't necessarily have a complete understanding of what neural networks are doing; they give us good results, but we don't know how or why," said Haydn Jones, a researcher in the Advanced Research in Cyber Systems group at Los Alamos. "Our new method does a better job of comparing neural networks, which is a crucial step toward better understanding the mathematics behind AI." Jones is the lead author of the paper "If You've Trained One You've Trained Them All: Inter-Architecture Similarity Increases With Robustness," which was presented recently at the Conference on Uncertainty in Artificial Intelligence. In addition to studying network similarity, the paper is a crucial step toward characterizing the behavior of robust neural networks. Neural networks are high performance, but fragile. For example, self-driving cars use neural networks to detect signs.