NASA Conducts RS-25 Engine Test in Support of Future Artemis Missions - NASA (.gov)

A new report suggests that Helen Chiang, recently appointed as Xbox Chief Operations Officer, was terminated for "policy violations."

An exciting new MacBook is rumored to be announced in just one week. If the reports end up being true, it'll be the most significant change to the lineup in many years, introducing a touchscreen OLED MacBook Pro. As exciting as it sounds, however, it's also the perfect opportunity to buy up the previous generation's M5 Max MacBook Pro at its lowest price ever. That's especially significant considering the entire lineup got a price increase back in June. Right now, the 16-inch M5 Max MacBook Pro with 36 GB of RAM and 2 TB of storage is $3,499, which is 20 percent off the retail price and $400 cheaper than it was just a few days ago. And historically, this is $150 cheaper than this configuration has ever sold for. Photograph: Luke LarsenPhotograph: Luke LarsenPhotograph: Luke LarsenPhotograph: Luke LarsenAppleMacBook Pro (M5 Pro/Max)This absolutely stacked version of the MacBook Pro is one of the most powerful laptops you can buy. The M5 Max is a huge upgrade over both the M4 Max and the M5 Pro, offering GPU performance we've never seen on a MacBook before. For content creators, media professionals, engineers, and AI enthusiasts, this is nearly as good as it gets.

The radial basis function (RBF) kernel parameter and the support vector machine regularization parameter require extensive tuning. This tuning can mask the true representational power of parameterised quantum kernels (PQKs) induced by quantum feature maps, an area not yet thoroughly explored. Comparisons between untuned and tuned PQKs with the RBF baseline expose the structural capabilities and native inductive bias of PQKs. New quantum feature maps use two-qubit entanglement to capture multivariate correlations in IoT data. Results indicate that extensive tuning can yield strong performing architectures. Novel entanglement topologies achieved a key number of 56.5% nonoccupied class accuracy in principle component analysis, surpassing previous methods. These earlier approaches required extensive classical tuning to reach even moderate performance on similar tasks. This level of separation was previously unattainable without significant optimisation; now, structural alignment within the quantum design itself drives improved results.