Orbital missions will unlock the next phase of Starship's development, providing better data on the performance of the spacecraft's heat shield and allowing for tests of in-orbit refueling, which will be essential for missions to Mars. Save this storyIt has been two weeks since SpaceX's last Starship test flight, and engineers have diagnosed issues with its heat shield, identified improvements, and developed a preliminary plan for the next time the ship heads into space. Bill Gerstenmaier, a SpaceX executive in charge of build and flight reliability, presented the findings Monday at the American Astronautical Society's Glenn Space Technology Symposium in Cleveland. The rocket lifted off on August 26 from SpaceX's launch pad in Starbase, Texas, just north of the US-Mexico border. It was the 10th full-scale test flight of SpaceX's Super Heavy booster and Starship upper stage, combining to form the world's largest rocket. There were a couple of overarching objectives on the August 26 test flight.

Breakthroughs, discoveries, and DIY tips sent every weekday. The smelly, brown seaweed can put a damper on a day at the beach at best and hinder baby turtles on their way to the ocean at worst. Only about one in 1,000 sea turtle hatchlings survive to adulthood, and might be added to their already long list of challenges . The new findings detailed in a study published in the explores the role that this brown seaweed plays on vulnerable sea turtle populations. "For sea turtle hatchlings, reaching the ocean is already a race against time - and survival. Now, increasingly large mats of sargassum are adding new challenges to this critical journey," study co-author and Florida Atlantic University biologist Sarah Milton, said in a statement .

Fox News chief political analyst Brit Hume explains why President Donald Trump should not remove himself from the peace negotiations between Russia and Ukraine and more on'Special Report.' When Russia invaded Ukraine in February 2022, many experts predicted Kyiv's quick fall. When Ukraine pushed back overextended Russian forces, the same experts confidently said that Russia's mass -- a population almost four times larger than Ukraine -- would certainly grind Ukraine down. Triumph for Putin was inevitable. But, an odd thing happened on the way to Russia's victory parade: Ukraine is outfighting Russia.

Accurate ocean forecasting is essential for supporting a wide range of marine applications. Recent advances in artificial intelligence have highlighted the potential of data-driven models to outperform traditional numerical approaches, particularly in atmospheric weather forecasting. However, extending these methods to ocean systems remains challenging due to their inherently slower dynamics and complex boundary conditions. In this work, we present MedFormer, a fully data-driven deep learning model specifically designed for medium-range ocean forecasting in the Mediterranean Sea. MedFormer is based on a U-Net architecture augmented with 3D attention mechanisms and operates at a high horizontal resolution of 1/24°. The model is trained on 20 years of daily ocean reanalysis data and fine-tuned with high-resolution operational analyses. It generates 9-day forecasts using an autoregressive strategy. The model leverages both historical ocean states and atmospheric forcings, making it well-suited for operational use. We benchmark MedFormer against the state-of-the-art Mediterranean Forecasting System (MedFS), developed at Euro-Mediterranean Center on Climate Change (CMCC), using both analysis data and independent observations. The forecast skills, evaluated with the Root Mean Squared Difference and the Anomaly Correlation Coefficient, indicate that MedFormer consistently outperforms MedFS across key 3D ocean variables. These findings underscore the potential of data-driven approaches like MedFormer to complement, or even surpass, traditional numerical ocean forecasting systems in both accuracy and computational efficiency.

Onion CEO Ben Collins Hasn't Given Up on Print--or Buying Infowars A year after relaunching The Onion as a newspaper, Collins visits to talk about why "going into something and not ruining it is bravery." Ben Collins made a big bet. A year ago, just a few months after he'd been named CEO of The Onion, he relaunched its print edition. Once a favorite on university campuses, The Onion hadn't published a physical issue since 2013 . Common wisdom said that readership, and advertising dollars, just weren't there for newspapers. But Collins, a fan of the satirical paper since childhood, thought "that's dumb." Readers celebrated The Onion's relaunch and the ability to read all of its bitingly funny headlines on a single broadsheet. Collins wouldn't give exact numbers on how many people are currently subscribed to the print edition but did say they should be enough to keep its writers' room humming (a few weeks after we taped this episode, the Wall Street Journal reported that The Onion now boasts more than 53,000 paying subscribers). On this episode of, I spoke with Collins about his hopes for The Onion, the future of journalism, and his Balatro addiction. KATIE DRUMMOND: Do you have a recent favorite Onion headline? Can I look it up for you? "Ghislaine Maxwell Can't Help but Notice Interview Room Covered in Plastic Sheeting." The staff churns out like 15 a day that are great. I sit there, and I still don't know how they do it. When I say they throw away eight or nine of the best sentences I would ever write every day, I mean that sincerely.

While diffusion-based models have shown remarkable generative capabilities in static settings, their extension to continual learning (CL) scenarios remains fundamentally constrained by Generative Catastrophic Forgetting (GCF). We observe that even with a rehearsal buffer, new generative skills often overwrite previous ones, degrading performance on earlier tasks. Although some initial efforts have explored this space, most rely on heuristics borrowed from continual classification methods or use trained diffusion models as ad hoc replay generators, lacking a principled, unified solution to mitigating GCF and often conducting experiments under fragmented and inconsistent settings. To address this gap, we introduce the Continual Diffusion Generation (CDG), a structured pipeline that redefines how diffusion models are implemented under CL and enables systematic evaluation of GCF. Beyond the empirical pipeline, we propose the first theoretical foundation for CDG, grounded in a cross-task analysis of diffusion-specific generative dynamics. Our theoretical investigation identifies three fundamental consistency principles essential for preserving knowledge in the rehearsal buffer over time: inter-task knowledge consistency, unconditional knowledge consistency, and prior knowledge consistency. These criteria expose the latent mechanisms through which generative forgetting manifests across sequential tasks. Motivated by these insights, we further propose \textit{Continual Consistency Diffusion} (CCD), a principled training framework that enforces these consistency objectives via hierarchical loss functions: $\mathcal{L}_{IKC}$, $\mathcal{L}_{UKC}$, and $\mathcal{L}_{PKC}$. Extensive experiments show that CCD achieves SOTA performance across various benchmarks, especially improving generative metrics in overlapping-task scenarios.

Comprised of GRD and SLC images from Sentinel-1; AIS used to create labels.

Identifying vessels that do not show up in conventional monitoring systems--known as "dark vessels"--is key to managing and securing the health

One key failure case was answers in the form of "the answer must be something that is ." Q: What might someone get from learning about science? Answer Choices: (a) headache (b) see things differently (c) increased knowledge (d) accidents (e) appreciation of nature A: The answer must be something that someone would get from learning about science. Learning about science would increase knowledge. Therefore, the answer is increased knowledge (c). These rationales, while perhaps useful to the model, read to us as opaque and unexplanatory. Sometimes the model will imply the answer that it has chosen in its question. Q: The christian rock band was on a large stage, the singer's foot got caught on a wire and he had a small what? Answer Choices: (a) commit sin (b) fail (c) disobey (d) stumble (e) boo boo A: The answer must be something that would cause the singer to stumble. The singer's foot got caught on a wire, so he would stumble.