Determining whether a candidate crystalline material is thermodynamically stable depends on identifying its true ground-state structure, a central challenge in computational materials science. We introduce CrystalGRW, a diffusion-based generative model on Riemannian manifolds that proposes novel crystal configurations and can predict stable phases validated by density functional theory. The crystal properties, such as fractional coordinates, atomic types, and lattice matrices, are represented on suitable Riemannian manifolds, ensuring that new predictions generated through the diffusion process preserve the periodicity of crystal structures. We incorporate an equivariant graph neural network to also account for rotational and translational symmetries during the generation process. CrystalGRW demonstrates the ability to generate realistic crystal structures that are close to their ground states with accuracy comparable to existing models, while also enabling conditional control, such as specifying a desired crystallographic point group. These features help accelerate materials discovery and inverse design by offering stable, symmetry-consistent crystal candidates for experimental validation.

Urban air mobility (UAM) is a transformative system that operates various small aerial vehicles in urban environments to reshape urban transportation. However, integrating UAM into existing urban environments presents a variety of complex challenges. Recent analyses of UAM's operational constraints highlight aircraft noise and system safety as key hurdles to UAM system implementation. Future UAM air traffic management schemes must ensure that the system is both quiet and safe. We propose a multi-agent reinforcement learning approach to manage UAM traffic, aiming at both vertical separation assurance and noise mitigation. Through extensive training, the reinforcement learning agent learns to balance the two primary objectives by employing altitude adjustments in a multi-layer UAM network. The results reveal the tradeoffs among noise impact, traffic congestion, and separation. Overall, our findings demonstrate the potential of reinforcement learning in mitigating UAM's noise impact while maintaining safe separation using altitude adjustments

Ensuring safe separation between aircraft is a critical challenge in air traffic management, particularly in urban air mobility (UAM) environments where high traffic density and low altitudes require precise control. In these environments, conflicts often arise at the intersections of flight corridors, posing significant risks. We propose a tactical separation approach leveraging shared scheduling protocols, originally designed for Ethernet networks and operating systems, to coordinate access to these intersections. Using a decentralized Markov decision process framework, the proposed approach enables aircraft to autonomously adjust their speed and timing as they navigate these critical areas, maintaining safe separation without a central controller. We evaluate the effectiveness of this approach in simulated UAM scenarios, demonstrating its ability to reduce separation violations to zero while acknowledging trade-offs in flight times as traffic density increases. Additionally, we explore the impact of non-compliant aircraft, showing that while shared scheduling protocols can no longer guarantee safe separation, they still provide significant improvements over systems without scheduling protocols.

Recently there have been many shared tasks targeting the detection of generated text from Large Language Models (LLMs). However, these shared tasks tend to focus either on cases where text is limited to one particular domain or cases where text can be from many domains, some of which may not be seen during test time. In this shared task, using the newly released RAID benchmark, we aim to answer whether or not models can detect generated text from a large, yet fixed, number of domains and LLMs, all of which are seen during training. Over the course of three months, our task was attempted by 9 teams with 23 detector submissions. We find that multiple participants were able to obtain accuracies of over 99% on machine-generated text from RAID while maintaining a 5% False Positive Rate -- suggesting that detectors are able to robustly detect text from many domains and models simultaneously. We discuss potential interpretations of this result and provide directions for future research.

Despite achieving remarkable performance, deep graph learning models, such as node classification and network embedding, suffer from harassment caused by small adversarial perturbations. However, the vulnerability analysis of graph matching under adversarial attacks has not been fully investigated yet. This paper proposes an adversarial attack model with two novel attack techniques to perturb the graph structure and degrade the quality of deep graph matching: (1) a kernel density estimation approach is utilized to estimate and maximize node densities to derive imperceptible perturbations, by pushing attacked nodes to dense regions in two graphs, such that they are indistinguishable from many neighbors; and (2) a meta learning-based projected gradient descent method is developed to well choose attack starting points and to improve the search performance for producing effective perturbations. We evaluate the effectiveness of the attack model on real datasets and validate that the attacks can be transferable to other graph learning models.

Fox News chief national security correspondent Jennifer Griffin reports on what the United States and Israel are doing to stay ahead of adversaries in A.I. on'Special Report.' President Biden on Tuesday signed an ambitious executive order that he says will keep both national security and climate change in mind while fast-tracking large-scale artificial intelligence (AI) infrastructure in the United States. Biden said in a statement released by the White House that the executive order will "accelerate the speed at which we build the next generation of AI infrastructure here in America, in a way that enhances economic competitiveness, national security, AI safety, and clean energy." It directs the Department of Defense and the Department of Energy "to lease federal sites where the private sector can build frontier AI infrastructure at speed and scale." "The United States leads the world at the frontier of artificial intelligence (AI). Cutting-edge AI will have profound implications for national security and enormous potential to improve Americans' lives if harnessed responsibly, from helping cure disease to keeping communities safe by mitigating the effects of climate change. However, we cannot take our lead for granted," Biden said.

Fox News' senior national correspondent William La Jeunesse joins'America's Newsroom' to discuss Congress' history of killing pushes for cost-cutting. FIRST ON FOX: The Congressional Department of Government Efficiency (DOGE) Caucus is holding its second-ever meeting on Wednesday, where its leaders are expected to unveil a set of "principles" to guide the group in its mission to cut government waste. They outlined eight goals, some practical while others more symbolic, in a bid to ensure the caucus is in sync with the DOGE advisory panel set up by President-elect Donald Trump. "The federal government must serve the interests of taxpayers, and taxpayers are best served by a lean, efficient, transparent, and accountable bureaucracy," the first principle read, according to a draft memo obtained by Fox News Digital. The document also suggested both lofty and smaller-scale goals.

We consider the blackbox transfer-based targeted adversarial attack threat model in the realm of deep neural network (DNN) image classifiers. Rather than focusing on crossing decision boundaries at the output layer of the source model, our method perturbs representations throughout the extracted feature hierarchy to resemble other classes. We design a flexible attack framework that allows for multi-layer perturbations and demonstrates state-of-the-art targeted transfer performance between ImageNet DNNs. We also show the superiority of our feature space methods under a relaxation of the common assumption that the source and target models are trained on the same dataset and label space, in some instances achieving a 10\times increase in targeted success rate relative to other blackbox transfer methods. Finally, we analyze why the proposed methods outperform existing attack strategies and show an extension of the method in the case when limited queries to the blackbox model are allowed.

Far above your head, constellations of satellites are working constantly to provide the positioning, navigation, and timing systems that quietly run modern life. Known as the global navigation satellite system, or GNSS, signals from these satellites provide the foundation for mobile networks, energy grids, the internet, and GPS. And increasingly, their dependability is under threat. GPS signals can be jammed--deliberately drowned out with other powerful radio signals--and spoofed, where erroneous signals are released to fool positioning systems. GPS interference has been documented in Ukraine, the Middle East, and the South China Sea.

Attacks on underwater cables running through strategically significant bodies of water in both the Baltic Sea and the South China Sea by Russia and China, respectively, in recent months has top officials concerned they are not "mere coincidence." Maritime sabotage efforts in both regions of the world appear to have been on the rise over the last several years, with a notable spike in recent months after at least three separate attacks occurred in as many months, beginning in November, and the top suspects are Russia and China. "The Kremlin has been running a hybrid campaign against Europe for years, ranging from spreading disinformation and cyberattacks to weaponizing energy supplies. Since Russia's full-scale invasion of Ukraine, these efforts have intensified dramatically," EU High Representative Kaja Kallas told Fox News Digital. "However, Russia is not the only challenge we face."