Minnesota DHS whistleblower Faye Bernstein said she faced a "smear campaign" for reporting fraud concerns since 2019. She was called "racist" and banned from state property.

Noisy ships and industry are impacting critically endangered Kemp's ridley sea turtles. Breakthroughs, discoveries, and DIY tips sent six days a week. For the world's smallest sea turtles, life in the ocean is getting pretty noisy. These relatively little turtles (on average they're still 75 to 100 pounds) mostly found in the Gulf of Mexico already face fishing gear accidents, seacraft collisions, plastic pollution, and habitat deterioration, and now excess noise may be harming the critically endangered and rare Kemp's ridley sea turtles (). We say because even though these sea turtles share waters with extremely busy shipping lanes, scientists know very little about their underwater hearing.

Motion forecasting is an essential task for autonomous driving, and utilizing information from infrastructure and other vehicles can enhance forecasting capabilities.

Bayesian optimization (BO) is a popular framework for optimizing black-box functions. In many applications, the objective function can be evaluated at multiple fidelities toenableatrade-offbetween thecostandaccuracy.

Reconstructing a continuous surface from a raw 3D point cloud is a challenging task.

Deep Gaussian Processes learn probabilistic data representations for supervised learning by cascading multiple Gaussian Processes. While this model family promises flexible predictive distributions, exact inference is not tractable. Approximate inference techniques trade off the ability to closely resemble the posterior distribution against speed of convergence and computational efficiency. We propose a novel Gaussian variational family that allows for retaining covariances between latent processes while achieving fast convergence by marginalising out all global latent variables. After providing a proof of how this marginalisation can be done for general covariances, we restrict them to the ones we empirically found to be most important in order to also achieve computational efficiency. We provide an efficient implementation of our new approach and apply it to several benchmark datasets. It yields excellent results and strikes a better balance between accuracy and calibrated uncertainty estimates than its state-of-the-art alternatives.

For a given set of points, define a value called "weight" for each point in (a),