Eva Shockey joins'Tomi Lahren Is Fearless,' discusses hunting and eating bears: 'Not my favorite' Alaska moose hunter miraculously survives ambush brown bear attack, says he'blacked out' Ella Langley's viral performance with Muscadine Bloodline at Penn State takes over the internet Lioness season 3 episode 7 delivers Joe's rescue in a gunfight fans won't stop talking about Viral video shows bystanders warning elderly man who has no clue he's being followed by a huge black bear Henry Winkler's wife says the actor tends to exaggerate about the size of his ... fish Zoe Saldaña shares behind the scenes Instagram posts from brutal'Lioness' torture scene Bear tries to steal one too many fish off of fisherman's line, gets charged at with pole in hand Riley Green drops acoustic'My Way' video as fans await'That's Just Me' album release Trump Butler rally shooting could be sign of a'larger conspiracy': Former deputy assistant AG Sheriff decries Democrat's'disgusting display' at House hearing on sanctuary laws Sheriff decries Democrat's'disgusting display' at House hearing on sanctuary laws Sheriff calls Democrat's questioning at House hearing a'disgusting display' It's urgent for Congress to step in and protect college sports: Sen Ted Cruz Mike Pence warns this Trump move would send a'deafening' message to Putin'COMPLETE WASTE OF MONEY': Real estate celebrity calls out $200,000 housing mistake Rand Paul slams Congress over AI regulation: 'I wouldn't put Congress in charge of a McDonald's' Rand Paul slams Congress over AI regulation: 'I wouldn't put Congress in charge of a McDonald's' Ed Sheeran responds to Macklemore backlash amid'Free Palestine' controversy NASCAR star Denny Hamlin tells OutKick Outdoors how fishing with professional angler Chris Zaldain has helped accelerate his learning curve and improve his skills on the water. A man from Westerly, Rhode Island was out on a family hike in Colorado a couple of weeks ago when he, his wife and his son came face-to-face with a momma moose and her baby. Dean and Rosemary Felicetti were in Colorado visiting their son Gabriel when they decided to go on a hike, reports WJAR . They visited a lake and had their feet in the water before heading back. Dense stands of spruce and fir climb the lower slopes while aspen and tall grass line the near shoreline on a cloudy summer day.

A hacker collective pulled down a Flock camera and dumped its data. The files included thousands of videos and logs showing that the device captured 1.6 million images of 50,000 vehicles in 21 days. Hackers ripped down a Flock camera above a roadway, made a near-complete copy of the data stored inside it, and shared the files with 404 Media and WIRED, revealing in new detail how exactly Flock Safety's cameras track the movements of both vehicles and people . The hackers say they are also publishing details on how they managed to obtain the software, in the hopes that other people may copy them. The breach provides an unprecedented look inside a system that Flock has described as protected by on-device encryption .

Security News This Week: Your Expired Visa Card Could Be'Zombified' to Make Contactless Payments Plus: Apple sends out an "unprecedented" number of spyware warnings, Ukraine hits a Russian ecommerce giant with cyber and drone attacks, and more. As the controversial vehicle surveillance giant Flock Safety continues to expand, WIRED got the code for the company's new AI policing tool and reconstructed the software to show that its capabilities go far beyond reading license plates and tracking vehicles. We also published the story this week of a Rhode Island police officer who was subjected to five internal affairs investigations in less than two years after he publicly questioned his department's use of Flock cameras . Following incidents of high-profile rogue activity by some of its AI agents, OpenAI said this week that it is halting model training runs and overhauling internal safety protocols. The company said that its upcoming Astra model may represent a turning point of "critical" cyber capabilities.

Flock Has a Powerful New AI Tool for Police. Flock's surveillance cameras have already sparked outrage. WIRED reconstructed its next-generation AI system, already in use by some police, to confirm it goes much further than tracking license plates. Vehicle surveillance giant Flock Safety has told the public for years that its technology "cannot recognize, identify, or track individuals." It has now built a system that does both, an artificial intelligence tool for police that can identify drivers and track vehicles by their patterns of movement alone, WIRED has learned. Drawing on a network of cameras that logs the movements of drivers in more than 6,000 communities, the tool can pick out potential witnesses by how often their cars pass through a neighborhood, or surface a driver's "associates" from the cameras they pass together. Because the system also reaches police case files, 911 dispatch logs, and commercial identity records, those plates can be turned into names, home addresses, and relatives.

Democrats talk a lot about affordability. Could a message of prosperity be more powerful? The Washington Roundtable's Evan Osnos interviews the former Commerce Secretary and Rhode Island governor Gina Raimondo about a problem she's been working on since she left the Biden Administration: how to make sure that the A.I. revolution does not leave American workers behind. Drawing on her father's experience of losing his factory job in the wake of outsourcing to China, Raimondo warns against repeating the mistakes of the past and argues that the U.S. needs a plan to help workers navigate the A.I. transition. "We can't put the toothpaste back in the bottle. A.I. is out there," Raimondo says.

Despite the remarkable empirical success of generative models, the available theory on their statistical accuracy in scientific computing remains largely pessimistic. This paper develops a theoretical framework for understanding the regularity of transport maps and the generalization properties of one-step Wasserstein-guided generative models for PDE-induced probability measures. We consider normalized target densities associated with linear elliptic and parabolic equations on bounded domains, as well as diffusion and Fokker--Planck equations on the torus. Under standard structural assumptions, we prove that these target measures satisfy doubling conditions. By combining this fact with regularity theory for optimal transport between doubling measures, we show that the optimal transport map from a uniform source measure to the target measure is Hölder continuous. This regularity yields an approximation-theoretic justification for one-step generative models that learn PDE-induced distributions via a single pushforward map. As a representative instance, we study DeepParticle and derive excess-risk bounds characterizing the discrepancy between the learned map and the population-optimal map. We also establish a robustness estimate under target shift and illustrate the theory with experiments which support the derived rates.

Recent work has shown that parameterizing and optimizing coordinate transformations using normalizing flows, i.e., invertible neural networks, can significantly accelerate the convergence of spectral approximations. We present the first error estimates for approximating functions using Hermite expansions composed with adaptive coordinate transformations. Our analysis establishes an equivalence principle: approximating a function $f$ in the span of the transformed basis is equivalent to approximating the pullback of $f$ in the span of Hermite functions. This allows us to leverage the classical approximation theory of Hermite expansions to derive error estimates in transformed coordinates in terms of the regularity of the pullback. We present an example demonstrating how a nonlinear coordinate transformation can enhance the convergence of Hermite expansions. Focusing on smooth functions decaying along the real axis, we construct a monotone transport map that aligns the decay of the target function with the Hermite basis. This guarantees spectral convergence rates for the corresponding Hermite expansion. Our analysis provides theoretical insight into the convergence behavior of adaptive Hermite approximations based on normalizing flows, as recently explored in the computational quantum physics literature.

Variational inference (VI) is a central tool in modern machine learning, used to approximate an intractable target density by optimising over a tractable family of distributions. As the variational family cannot typically represent the target exactly, guarantees on the quality of the resulting approximation are crucial for understanding which of its properties VI can faithfully capture. Recent work has identified instances in which symmetries of the target and the variational family enable the recovery of certain statistics, even under model misspecification. However, these guarantees are inherently problem-specific and offer little insight into the fundamental mechanism by which symmetry forces statistic recovery. In this paper, we overcome this limitation by developing a general theory of symmetry-induced statistic recovery in variational inference. First, we characterise when variational minimisers inherit the symmetries of the target and establish conditions under which these pin down identifiable statistics. Second, we unify existing results by showing that previously known statistic recovery guarantees in location-scale families arise as special cases of our theory. Third, we apply our framework to distributions on the sphere to obtain novel guarantees for directional statistics in von Mises-Fisher families. Together, these results provide a modular blueprint for deriving new recovery guarantees for VI in a broad range of symmetry settings.

Under general assumptions on the target distribution $p^\star$, we establish a sharp Lipschitz regularity theory for flow-matching vector fields and diffusion-model scores, with optimal dependence on time and dimension. As applications, we obtain Wasserstein discretization bounds for Euler-type samplers in dimension $d$: with $N$ discretization steps, the error achieves the optimal rate $\sqrt{d}/N$ up to logarithmic factors. Moreover, the constants do not deteriorate exponentially with the spatial extent of $p^\star$. We also show that the one-sided Lipschitz control yields a globally Lipschitz transport map from the standard Gaussian to $p^\star$, which implies Poincaré and log-Sobolev inequalities for a broad class of probability measures.