We present AROMA (Attentive Reduced Order Model with Attention), a framework designed to enhance the modeling of partial differential equations (PDEs) using local neural fields. Our flexible encoder-decoder architecture can obtain smooth latent representations of spatial physical fields from a variety of data types, including irregular-grid inputs and point clouds. This versatility eliminates the need for patching and allows efficient processing of diverse geometries. The sequential nature of our latent representation can be interpreted spatially and permits the use of a conditional transformer for modeling the temporal dynamics of PDEs. By employing a diffusion-based formulation, we achieve greater stability and enable longer rollouts compared to conventional MSE training. AROMA's superior performance in simulating 1D and 2D equations underscores the efficacy of our approach in capturing complex dynamical behaviors.

Looking Glass is transforming how we interact with 3D visuals. You can now gather around a screen and see digital objects come to life in true three dimensions; no headsets, no glasses, just your eyes and a shared experience with others. That's exactly what the new, 27-inch light-field display from Looking Glass offers. This innovative technology is transforming how we interact with 3D visuals, making immersive experiences more natural and accessible for businesses, educators and creators alike. Join the FREE "CyberGuy Report": Get my expert tech tips, critical security alerts and exclusive deals, plus instant access to my free "Ultimate Scam Survival Guide" when you sign up!

Kurt'CyberGuy' Knutsson joins'Fox & Friends' to warn about a disturbing new scam where criminals use AI to clone the voices of loved ones and trick victims into sending money. Charges have been filed against an Oklahoma woman who is being accused of laundering nearly 1.5 million in funds obtained through online romance scams, targeting elderly women. Attorney General Gentner Drummond announced that Christine Joan Echohawk, 53, was arrested Monday, and is accused of laundering money from out-of-state victims between Sept. 30, 2024, and Dec. 26, 2024. Officials said that all the victims were women between the ages of 64 and 79. The victims believed they were sending money to a male subject whom they thought they were in an online relationship with, according to a news release from Drummond's office.

I am a forty-five-year-old journalist who, for many years, didn't read the news. In high school, I knew about events like the O. J. Simpson trial and the Oklahoma City bombing, but not much else. In college, I was friends with geeky economics majors who read The Economist, but I'm pretty sure I never actually turned on CNN or bought a paper at the newsstand. I read novels, and magazines like Wired and Spin. If I went online, it wasn't to check the front page of the Times but to browse record reviews from College Music Journal. Somehow, during this time, I thought of myself as well informed.

Best subset selection in linear regression is well known to be nonconvex and computationally challenging to solve, as the number of possible subsets grows rapidly with increasing dimensionality of the problem. As a result, finding the global optimal solution via an exact optimization method for a problem with dimensions of 1000s may take an impractical amount of CPU time. This suggests the importance of finding suboptimal procedures that can provide good approximate solutions using much less computational effort than exact methods. In this work, we introduce a new procedure and compare it with other popular suboptimal algorithms to solve the best subset selection problem. Extensive computational experiments using synthetic and real data have been performed. The results provide insights into the performance of these methods in different data settings. The new procedure is observed to be a competitive suboptimal algorithm for solving the best subset selection problem for high-dimensional data.

While 3D Gaussians efficiently approximate an object's surface, they fail to capture the volumetric properties of subsurface scattering. We propose a framework for optimizing an object's shape together with the radiance transfer field given multiview OLAT (one light at a time) data. Our method decomposes the scene into an explicit surface represented as 3D Gaussians, with a spatially varying BRDF, and an implicit volumetric representation of the scattering component. A learned incident light field accounts for shadowing.

Generating controllable and photorealistic digital human avatars is a long-standing and important problem in Vision and Graphics. Recent methods have shown great progress in terms of either photorealism or inference speed while the combination of the two desired properties still remains unsolved. To this end, we propose a novel method, called DELIFFAS, which parameterizes the appearance of the human as a surface light field that is attached to a controllable and deforming human mesh model. At the core, we represent the light field around the human with a deformable two-surface parameterization, which enables fast and accurate inference of the human appearance. This allows perceptual supervision on the full image compared to previous approaches that could only supervise individual pixels or small patches due to their slow runtime. Our carefully designed human representation and supervision strategy leads to state-of-the-art synthesis results and inference time. The video results and code are available at https://vcai.

Motivated by decentralized approaches to machine learning, we propose a collaborative Bayesian learning algorithm taking the form of decentralized Langevin dynamics in a non-convex setting. Our analysis show that the initial KL-divergence between the Markov Chain and the target posterior distribution is exponentially decreasing while the error contributions to the overall KL-divergence from the additive noise is decreasing in polynomial time. We further show that the polynomial-term experiences speed-up with number of agents and provide sufficient conditions on the time-varying step-sizes to guarantee convergence to the desired distribution. The performance of the proposed algorithm is evaluated on a wide variety of machine learning tasks. The empirical results show that the performance of individual agents with locally available data is on par with the centralized setting with considerable improvement in the convergence rate.

At Fort Sill, service members from across the military are undergoing counter-drone training at the Joint C-sUAS (Counter small Unmanned Aircraft System) University (JCU), also known as "drone university." The program has become a critical part of the Military's efforts to combat the rapidly growing use of unmanned aerial systems (UAS) by adversaries. "It's the Army's premier Counter-Small UAS training institution," said Col. Moseph Sauda, the program's director. "Our mission is to prepare and train the joint force to counter the threat, to be able to understand that threat, how they operate, and how they attack us… We can then develop not only tactics, techniques, and procedures, but also the employment methodology that maximizes the capabilities of our existing systems." A 3D-printed drone flies above from Oklahoma's Fort Sill at the U.S. Army's Joint C-sUAS University.