Into the two big foreign policy arenas sucking up much of the Trump administration's time and effort come two major challenges in less than 24 hours. Israel's air raid on the offices of Hamas in Doha and a Russian drone incursion deep into Polish airspace represent two massive headaches for the White House. After all, these are conflicts - Ukraine and Gaza - US President Donald Trump said he would deal with swiftly and decisively. In each case, a leader he sees as a natural, if problematic ally - Russian President Vladimir Putin and Israeli Prime Minister Benjamin Netanyahu - has thrown a massive spanner in the wheels of White House peace-making. The Doha raid came just two days after the Trump administration delivered its latest proposals to end the war in Gaza.

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.

Accurate quantification of uncertainty in neural network predictions remains a central challenge for scientific applications involving high-dimensional, correlated data. While existing methods capture either aleatoric or epistemic uncertainty, few offer closed-form, multidimensional distributions that preserve spatial correlation while remaining computationally tractable. In this work, we present a framework for training neural networks with a multidimensional Gaussian loss, generating closed-form predictive distributions over outputs with non-identically distributed and heteroscedastic structure. Our approach captures aleatoric uncertainty by iteratively estimating the means and covariance matrices, and is demonstrated on a super-resolution example. We leverage a Fourier representation of the covariance matrix to stabilize network training and preserve spatial correlation. We introduce a novel regularization strategy -- referred to as information sharing -- that interpolates between image-specific and global covariance estimates, enabling convergence of the super-resolution downscaling network trained on image-specific distributional loss functions. This framework allows for efficient sampling, explicit correlation modeling, and extensions to more complex distribution families all without disrupting prediction performance. We demonstrate the method on a surface wind speed downscaling task and discuss its broader applicability to uncertainty-aware prediction in scientific models.

Finally, we illustrate how our framework can be used for exploratory data analysis.

We introduce chefs' random tables (CRTs), a new class of non-trigonometric random features (RFs) to approximate Gaussian and softmax-kernels.

Dataset and all its scripts can be found at https://github.com/spcl/ens10 .