By design, COLP is a parsimonious classifier, which gives rise to a provably identifiable causal model.

It took just minutes for a new Greek-made anti-drone system to show what it is capable of. On its first test run with a European Union patrol in the Red Sea a year ago, the Centauros system detected and swiftly brought down two aerial drones launched by Yemen's Houthis, who have been attacking merchant vessels in the busy shipping lane. Another two drones swiftly retreated: Centauros had jammed their electronics, said Kyriakos Enotiadis, electronics director at state-run Hellenic Aerospace Industry (HAI), which produces the anti-drone system.

The sea floor is home to all sorts of weird and wonderful creatures. But one in particular has become an online sensation, thanks to its impressive'buttocks'. A big–bottomed sea star has been spotted more than 1,000 metres (3,280ft) below the waves. And it appears to have a backside that will make even the most avid gymgoer jealous. This has led many baffled viewers to compare the creature to Patrick from the animated series Spongebob Squarepants.

Breakthroughs, discoveries, and DIY tips sent every weekday. Fine artist Laura Shape uses quite an unexpected medium in her visual artwork. It lends striking patterns to her abstract canvases, while helping restore rivers, reefs, and wetlands. Shape uses the leather of invasive species--specifically lionfish, carp, and Burmese pythons. "I use those materials to make vibrant, textured, abstract acrylic pieces," she tells Popular Science via video call.

Adm. Daryl Caudle joins'America's Newsroom' to discuss rising tensions with China's navy, the use of AI in US defense, and a powerful Memorial Day re-enlistment ceremony at Ground Zero. The U.S. Navy has called off a search for a sailor assigned to the USS George Washington amid reports that he possibly went overboard while the ship was sailing north of Australia. The sailor was reported overboard on the aircraft carrier on Monday as the ship was transiting the Timor Sea, the Navy said. US DEFENSE OFFICIAL REACTS TO IRAN'S CLAIMS ABOUT ENCOUNTER WITH WARSHIP This photo shows a general view of U.S. aircraft carrier USS George Washington shortly after berthing at Manila Bay in Manila on July 3. (TED ALJIBE/AFP via Getty Images) The search effort involving the George Washington, its carrier strike group, as well as the Australian Defence (sic) Force and Australian Border Force, concluded at 12:40 p.m. Wednesday. "USS George Washington expresses sincere condolences to those impacted by this loss and is actively engaged with the crew to make services available to tend to their needs during this challenging time," Lt. Cmdr.

Though the underlying fields associated with vector-valued environmental data are continuous, observations themselves are discrete. For example, climate models typically output grid-based representations of wind fields or ocean currents, and these are often downscaled to a discrete set of points. By treating the area of interest as a two-dimensional manifold that can be represented as a triangular mesh and embedded in Euclidean space, this work shows that discrete intrinsic Gaussian processes for vector-valued data can be developed from discrete differential operators defined with respect to a mesh. These Gaussian processes account for the geometry and curvature of the manifold whilst also providing a flexible and practical formulation that can be readily applied to any two-dimensional mesh. We show that these models can capture harmonic flows, incorporate boundary conditions, and model non-stationary data. Finally, we apply these models to downscaling stationary and non-stationary gridded wind data on the globe, and to inference of ocean currents from sparse observations in bounded domains.

Group or cluster structure on explanatory variables in machine learning problems is a very general phenomenon, which has attracted broad interest from practitioners and theoreticians alike. In this work we contribute an approach to sparse learning under such group structure, that does not require prior information on the group identities. Our paradigm is motivated by the Laplacian geometry of an underlying network with a related community structure, and proceeds by directly incorporating this into a penalty that is effectively computed via a heat-flow-based local network dynamics. The proposed penalty interpolates between the lasso and the group lasso penalties, the runtime of the heat-flow dynamics being the interpolating parameter. As such it can automatically default to lasso when the group structure reflected in the Laplacian is weak. In fact, we demonstrate a data-driven procedure to construct such a network based on the available data. Notably, we dispense with computationally intensive pre-processing involving clustering of variables, spectral or otherwise. Our technique is underpinned by rigorous theorems that guarantee its effective performance and provide bounds on its sample complexity. In particular, in a wide range of settings, it provably suffices to run the diffusion for time that is only logarithmic in the problem dimensions. We explore in detail the interfaces of our approach with key statistical physics models in network science, such as the Gaussian Free Field and the Stochastic Block Model. Our work raises the possibility of applying similar diffusion-based techniques to classical learning tasks, exploiting the interplay between geometric, dynamical and stochastic structures underlying the data.

Welcome back to In the Loop, TIME's new twice-weekly newsletter about the world of AI. If you're reading this in your browser, you can subscribe to have the next one delivered straight to your inbox. Let's say, sometime in the next few years, artificial intelligence automates most of the jobs that humans currently do. If that happens, how can we avoid societal collapse? This question, once the stuff of science fiction, is now very real.

Data factorization methods have met with considerable success in discovering latent features of the signals encountered in wide-ranging applications. In this way, the representation bases, which make up the columns of the basis matrix or dictionary, are learned from the available samples of the target environment. An example is the sparse representation (SR) in which the dictionary is intended to best represent the data with a small number of atoms, much smaller than the dimension of the signal space. It has been shown that, in addition to a more informative representation of signals, imposing sparsity constraints on the representation coefficients can improve the generalization performance and the computational efficiency [1, 2, 3]. Furthermore, the sparse representation is more robust to noise, redundancy, and missing data. These features are mainly attributed to the fact that the intrinsic dimension of natural signals is usually much smaller than their apparent dimension and hence SR in an appropriate dictionary can extract these intrinsic features more efficiently. SR has been a successful strategy and has received considerable attention and achieved state-of-the-art results in many applications, e.g.