Extinction is a part of nature. Of the five billion species that have existed on Earth, 99.9 per cent have vanished. The Triassic-Jurassic extinction, two hundred million years ago, finished off the crocodile-like phytosaur. Sixty-six million years ago, the end-Cretaceous extinction eliminated the Tyrannosaurus rex and the velociraptor; rapid climate change from an asteroid impact was the likely cause. The Neanderthals disappeared some forty thousand years ago. One day--whether from climate change, another asteroid, nuclear war, or something we can't yet imagine--humans will probably be wiped out, too.

Over the past three decades, Hicks has watched the Pentagon transform--politically, strategically, and technologically. She entered government in the 1990s at the tail end of the Cold War, when optimism and a belief in global cooperation still dominated US foreign policy. After 9/11, the focus shifted to counterterrorism and nonstate actors. Then came Russia's resurgence and China's growing assertiveness. Hicks took two previous breaks from government work--the first to complete a PhD at MIT and joining the think thank Center for Strategic and International Studies (CSIS), which she later rejoined to lead its International Security Program after her second tour. "By the time I returned in 2021," she says, "there was one actor--the PRC (People's Republic of China)--that had the capability and the will to really contest the international system as it's set up."

A "quiet crisis" is emerging at the U.S.-Canada border, as one expert proposes an unconventional solution to fight human smuggling: leveraging advanced technologies like artificial intelligence. While national attention is largely fixed on the southern border, an increasingly concerning situation is unfolding along the country's northern border, said Jon Brewton, the founder and CEO of Data2 and a U.S. Air Force Veteran. "U.S. Customs and Border Patrol has seen a fairly alarming increase in illegal crossings, drug trafficking, and even encountering individuals on the terrorist watch list," he told Fox News Digital. "And as difficult as securing the southern border has been, the northern border is twice as long." While the vast majority of illegal crossings happen at the southern border, officials have been warning for years that the northern line has seen an increase.

TL;DR: Give your old PC a new lease on life with a Microsoft Windows 11 Pro license, now only 11.63 (reg. Need a new laptop but don't have the budget to buy one? We've found the next best thing -- updating your operating system. If you've got an old PC that could use an upgrade, Microsoft Windows 11 Pro is now just 11.63 (reg. Curious what Windows 11 Pro brings to the table?

Russia's Defence Ministry said its air defence units "intercepted and destroyed" 11 Ukrainian drones over the country's Kursk and Belgorod regions, which border Ukraine, as well as the southern Rostov region overnight. Russia's Defence Ministry said Kyiv carried out seven attacks on Moscow's energy infrastructure facilities between April 5 and 6, despite a moratorium on energy strikes brokered by the United States. According to the ministry, the attacks targeted the Crimean region, which Russia annexed from Ukraine in 2014, as well as Russia's Bryansk, Rostov and Voronezh regions. The ministry also said that Kremlin forces launched an overnight strike using long-range precision weapons and drones against Ukraine's central artillery armament base and defence industry enterprises involved in drone production. According to the ministry, Moscow also gained control over the village of Basivka in Ukraine's Sumy region in a rare cross-border advance.

I know you've heard of gamification, but have you ever heard of festification? That's what Microsoft will be doing in April and May, with the Microsoft AI Skills Fest. It's a little odd, but it also looks like it might be a heck of a lot of fun. I've written a lot about Microsoft over the years. I've mocked its product naming.

Autonomous driving has made significant progress in both academia and industry, including performance improvements in perception task and the development of end-to-end autonomous driving systems. However, the safety and robustness assessment of autonomous driving has not received sufficient attention. Current evaluations of autonomous driving are typically conducted in natural driving scenarios. However, many accidents often occur in edge cases, also known as safety-critical scenarios. These safety-critical scenarios are difficult to collect, and there is currently no clear definition of what constitutes a safety-critical scenario. In this work, we explore the safety and robustness of autonomous driving in safety-critical scenarios. First, we provide a definition of safety-critical scenarios, including static traffic scenarios such as adversarial attack scenarios and natural distribution shifts, as well as dynamic traffic scenarios such as accident scenarios. Then, we develop an autonomous driving safety testing platform to comprehensively evaluate autonomous driving systems, encompassing not only the assessment of perception modules but also system-level evaluations. Our work systematically constructs a safety verification process for autonomous driving, providing technical support for the industry to establish standardized test framework and reduce risks in real-world road deployment.

Score estimation is the backbone of score-based generative models (SGMs), especially denoising diffusion probabilistic models (DDPMs). A key result in this area shows that with accurate score estimates, SGMs can efficiently generate samples from any realistic data distribution (Chen et al., ICLR'23; Lee et al., ALT'23). This distribution learning result, where the learned distribution is implicitly that of the sampler's output, does not explain how score estimation relates to classical tasks of parameter and density estimation. This paper introduces a framework that reduces score estimation to these two tasks, with various implications for statistical and computational learning theory: Parameter Estimation: Koehler et al. (ICLR'23) demonstrate that a score-matching variant is statistically inefficient for the parametric estimation of multimodal densities common in practice. In contrast, we show that under mild conditions, denoising score-matching in DDPMs is asymptotically efficient. Density Estimation: By linking generation to score estimation, we lift existing score estimation guarantees to $(\epsilon,\delta)$-PAC density estimation, i.e., a function approximating the target log-density within $\epsilon$ on all but a $\delta$-fraction of the space. We provide (i) minimax rates for density estimation over H\"older classes and (ii) a quasi-polynomial PAC density estimation algorithm for the classical Gaussian location mixture model, building on and addressing an open problem from Gatmiry et al. (arXiv'24). Lower Bounds for Score Estimation: Our framework offers the first principled method to prove computational lower bounds for score estimation across general distributions. As an application, we establish cryptographic lower bounds for score estimation in general Gaussian mixture models, conceptually recovering Song's (NeurIPS'24) result and advancing his key open problem.

Three-dimensional digital reconstruction of porous media presents a fundamental challenge in geoscience, requiring simultaneous resolution of fine-scale pore structures while capturing representative elementary volumes. We introduce a computational framework that addresses this challenge through latent diffusion models operating within the EDM framework. Our approach reduces dimensionality via a custom variational autoencoder trained in binary geological volumes, improving efficiency and also enabling the generation of larger volumes than previously possible with diffusion models. A key innovation is our controlled unconditional sampling methodology, which enhances distribution coverage by first sampling target statistics from their empirical distributions, then generating samples conditioned on these values. Extensive testing on four distinct rock types demonstrates that conditioning on porosity - a readily computable statistic - is sufficient to ensure a consistent representation of multiple complex properties, including permeability, two-point correlation functions, and pore size distributions. The framework achieves better generation quality than pixel-space diffusion while enabling significantly larger volume reconstruction (256-cube voxels) with substantially reduced computational requirements, establishing a new state-of-the-art for digital rock physics applications.

Text-to-image diffusion models excel at generating diverse portraits, but lack intuitive shadow control. Existing editing approaches, as post-processing, struggle to offer effective manipulation across diverse styles. Additionally, these methods either rely on expensive real-world light-stage data collection or require extensive computational resources for training. To address these limitations, we introduce Shadow Director, a method that extracts and manipulates hidden shadow attributes within well-trained diffusion models. Our approach uses a small estimation network that requires only a few thousand synthetic images and hours of training-no costly real-world light-stage data needed. Shadow Director enables parametric and intuitive control over shadow shape, placement, and intensity during portrait generation while preserving artistic integrity and identity across diverse styles. Despite training only on synthetic data built on real-world identities, it generalizes effectively to generated portraits with diverse styles, making it a more accessible and resource-friendly solution.