As wildfires become increasingly destructive and expensive to control, effective management of active wildfires requires accurate, real-time fire spread predictions. To enhance the forecasting accuracy of active fires, data assimilation plays a vital role by integrating observations (such as remote-sensing data) and fire predictions generated from numerical models. This paper provides a comprehensive investigation on the application of a recently proposed diffusion-model-based filtering algorithm -- the Ensemble Score Filter (EnSF) -- to the data assimilation problem for real-time active wildfire spread predictions. Leveraging a score-based generative diffusion model, EnSF has been shown to have superior accuracy for high-dimensional nonlinear filtering problems, making it an ideal candidate for the filtering problems of wildfire spread models. Technical details are provided, and our numerical investigations demonstrate that EnSF provides superior accuracy, stability, and computational efficiency, establishing it as a robust and practical method for wildfire data assimilation. Our code has been made publicly available.

How can we assess the reliability of a dataset without access to ground truth? We introduce the problem of reliability scoring for datasets collected from potentially strategic sources. The true data are unobserved, but we see outcomes of an unknown statistical experiment that depends on them. To benchmark reliability, we define ground-truth-based orderings that capture how much reported data deviate from the truth. We then propose the Gram determinant score, which measures the volume spanned by vectors describing the empirical distribution of the observed data and experiment outcomes. We show that this score preserves several ground-truth based reliability orderings and, uniquely up to scaling, yields the same reliability ranking of datasets regardless of the experiment -- a property we term experiment agnosticism. Experiments on synthetic noise models, CIFAR-10 embeddings, and real employment data demonstrate that the Gram determinant score effectively captures data quality across diverse observation processes.

Regression with non-Euclidean responses -- e.g., probability distributions, networks, symmetric positive-definite matrices, and compositions -- has become increasingly important in modern applications. In this paper, we propose deep Fréchet neural networks (DFNNs), an end-to-end deep learning framework for predicting non-Euclidean responses -- which are considered as random objects in a metric space -- from Euclidean predictors. Our method leverages the representation-learning power of deep neural networks (DNNs) to the task of approximating conditional Fréchet means of the response given the predictors, the metric-space analogue of conditional expectations, by minimizing a Fréchet risk. The framework is highly flexible, accommodating diverse metrics and high-dimensional predictors. We establish a universal approximation theorem for DFNNs, advancing the state-of-the-art of neural network approximation theory to general metric-space-valued responses without making model assumptions or relying on local smoothing. Empirical studies on synthetic distributional and network-valued responses, as well as a real-world application to predicting employment occupational compositions, demonstrate that DFNNs consistently outperform existing methods.

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Government investigators are gathering data to confirm what exactly cracked the windshield of a 737 Max aircraft at above 30,000 feet. An United Airlines Boeing 737 MAX 9 airplane, similar to the one that was struck, approaches San Diego International Airport for a landing.Photograph: Kevin Carter; Getty Images Save this storyThe National Transportation Safety Board confirmed Sunday that it is investigating an airliner that was struck by an object in its windscreen, mid-flight, over Utah. "NTSB gathering radar, weather, flight recorder data," the federal agency said on the social media site X. "Windscreen being sent to NTSB laboratories for examination." The strike occurred Thursday, during a United Airlines flight from Denver to Los Angeles. Images shared on social media showed that one of the two large windows at the front of a 737 MAX aircraft was significantly cracked. Related images also reveal a pilot's arm that has been cut multiple times by what appear to be small shards of glass.

Employees at the company's Chelsea campus were told to stay home after exterminators found "credible evidence" of an infestation. Google's New York office is shown in lower Manhattan. Google employees working at the company's Chelsea campus in New York City received a notice on Sunday alerting them to a possible bed bug outbreak at the office. Exterminators arrived at the scene with a sniffer dog "and found credible evidence of their presence," according to an email obtained by WIRED. The email was sent to all Google employees in New York on behalf of the company's environmental, health, and safety team.

In just seven minutes, the thieves took off with crown jewels containing with thousands of diamonds along with other precious gems. Police stand outside the Louvre after a brazen theft. Could the French TV series have been prophetic? The show envisioned a heist at the Louvre, an event that became reality on the morning of October 19, when a group of professional thieves managed to break into the world-famous Paris museum . In just seven minutes, they stole a host of priceless French crown jewels.

Cybersecurity experts warn the AI companion app data leak could fuel blackmail and identity theft after millions of private chats were exposed.

Gabii was a once powerful city, but was largely abandoned by 50 CE. Breakthroughs, discoveries, and DIY tips sent every weekday. The city of Rome has been inhabited continuously for 14,000 years --long before it became known by its current name. While this makes the Italian capital city a world capital in terms of archaeology and ancient history, the centuries and centuries of construction make it difficult to study the city's ancient beginnings . Enter Gabii, an ancient Roman city 11 miles east of Rome you've probably never heard of unless you're an expert in the field.

When OpenAI released its AI video-generation app, Sora, in September, it promised that "you are in control of your likeness end-to-end." The app allows users to include themselves and their friends in videos through a feature called "cameos"--the app scans a user's face and performs a liveness check, providing data to generate a video of the user and to authenticate their consent for friends to use their likeness on the app. But Reality Defender, a company specializing in identifying deepfakes, says it was able to bypass Sora's anti-impersonation safeguards within 24 hours. Platforms such as Sora give a "plausible sense of security," says Reality Defender CEO Ben Colman, despite the fact that "anybody can use completely off-the-shelf tools" to pass authentication as someone else. Reality Defender's researchers used publicly available footage of notable individuals, including CEOs and entertainers, from earnings calls and media interviews.