The pasta-sauce company has partnered with the nonprofit StoryCorps on a device designed to record family conversations around the table and save them for all time. Prego, the pasta sauce company, is getting into hardware with a device that sits on your table and records dinner conversations. The Connection Keeper is a round puck that houses two microphones for recording around the table. The recorder was developed in partnership with StoryCorps, the 23-year-old nonprofit that has recorded conversations with more than 720,000 people about their lives. The Connection Keeper is more of a publicity stunt than a readily available product.

The Bose QuietComfort Ultra 2 have the best active noise cancellation on the market, and they very rarely get cheaper. Bose's QuietComfort Ultra 2 earbuds are the best noise-canceling earbuds you can buy. Right now, they're $50 off, which matches the best price we tend to see outside of special events like Black Friday and Cyber Monday. If you want to wait until November, they might hit $200 again, but otherwise $250 is a very fair deal--especially since they pop back up to $300 regularly. The discounted price applies to all five color options, including Black, Deep Plum, Desert Gold, Midnight Violet, and White Smoke (another rarity, as usually only the vivid colors go on sale).

Mark Zuckerberg and Jack Dorsey have different visions for how to use AI for management purposes, but both imagine a system of heightened control. Silicon Valley moguls have lately complained that too many people are too negative about artificial intelligence. They're likewise frustrated by stalled AI adoption among major corporations that aren't seeing the lucrative efficiencies promised by Big Tech. But if consumers and corporations are proving resistant to AI's acceleration, it hasn't stopped billionaire CEOs from charging ahead with their personal fantasies of what the technology can do. On April 13, the Financial Times reported that Meta is working up a photorealistic, three-dimensional AI avatar of chief exec Mark Zuckerberg, according to several people at the company.

Things to Do in L.A. Tap to enable a layout that focuses on the article. The Dodgers of esports: How L.A.'s Liquid Guild won the attention of over 100,000 people The top "WoW" guilds around the world, including Team Liquid, race to be the first to defeat highest-difficulty bosses. This is read by an automated voice. Please report any issues or inconsistencies here . Los Angeles-based Team Liquid won the "World of Warcraft" world championship for the fourth consecutive time, defeating Germany's Echo guild in a monthlong competition watched by more than 100,000 viewers.

Ritzy Bay Area town torn apart after teacher's daughter, 16, was behind wheel when four friends died in high-speed crash... then she posted a TikTok video that poured fuel on the flames Two CIA officers killed in Mexico when their car skidded off ravine and exploded after meeting about bust of'largest ever drug lab' Nancy Guthrie sheriff's appalling past revealed: Beat handcuffed suspect so badly he needed intensive care, used VILE language about woman and lied in sworn statement Trump confronts Xi as US forces seize Chinese ship carrying mysterious'gift' to Iran New'Hollywood dose' pill: A-listers hooked on'youth elixir' that dermatologists say is anti-ageing, shrinks pores, smooths wrinkles... and even banishes rosacea Days after we got engaged, the love of my life told me he'd killed a man and buried him in a bog. I reported him to police... but then I made this irreversible mistake Ark of the Covenant's final resting place pinpointed by archaeologists as fresh search begins Fury as murderer marries pen pal behind bars... as teenage victim's mom says: 'I'm serving a life sentence without my son' Insiders claim failed AI rollout could be to blame for Tim Cook's departure from Apple - as one says'the AI era requires a different kind of leadership' Life-threatening cantaloupe recall in four states upgraded to FDA's highest risk level... 'reasonable probability of death' AMANDA PLATELL: Why Sarah Ferguson - with the ghost of Princess Diana at her side - is ready to sensationally blow up the Royal Family. She knows ALL their secrets... Team USA Olympics star Noah Lyles slammed for'horrible' reaction to his wife's wedding dress reveal In honour of the Queen's (purple!) reign: Kate mirrors late monarch's colourful wardrobe and wears her pearl earrings and necklace US troops board second tanker as Iran is accused of breaking ceasefire'numerous times' How to lose weight when perimenopause sabotages your metabolism: I'm a trainer but when I hit 46, I piled on the pounds overnight. The new'posh' drug that's easier to order than Uber Eats - and why all my middle-class friends have ditched booze and cocaine for it: JANA HOCKING Autistic woman, 24, worked hard to build independent life for herself... now she's PARALYZED thanks to selfishness of stranger READ MORE: McDonald's is testing humanoid ROBOTS in Shanghai During last year's shambolic Beijing robot half marathon, humanoid machines tripped, shuffled, and occasionally shattered into pieces as they collapsed under the strain. But 12 months later, supporters looked on in awe as a new generation of speedy robotic racers left the human athletes in the dust.

A longitudinal study found that loneliness is closely linked to lapses in immediate and delayed recall. Neuroscientists know that there is a link between loneliness and cognitive decline in older adults, although it is still difficult to understand the exact magnitude of the link. A new longitudinal study provides evidence that a proportion of people who feel lonely end up having more memory impairment, though this doesn't necessarily mean that their brains age faster. The report, published in Aging & Mental Health, shows that older adults with higher levels of loneliness scored lower on tests of immediate and delayed recall. Even so, the rate at which their memory declined over six years was virtually identical to those who were not lonely.

In variety testing, multi-environment trials (MET) are essential for evaluating the genotypic performance of crop plants. A persistent challenge in the statistical analysis of MET data is the estimation of variance components, which are often still inaccurately estimated or shrunk to exactly zero when using residual (restricted) maximum likelihood (REML) approaches. At the same time, institutions conducting MET typically possess extensive historical data that can, in principle, be leveraged to improve variance component estimation. However, these data are rarely incorporated sufficiently. The purpose of this paper is to address this gap by proposing a Bayesian framework that systematically integrates historical information to stabilize variance component estimation and better quantify uncertainty. Our Bayesian linear mixed model (BLMM) reformulation uses priors and Markov chain Monte Carlo (MCMC) methods to maintain the variance components as positive, yielding more realistic distributional estimates. Furthermore, our model incorporates historical prior information by managing MET data in successive historical data windows. Variance component prior and posterior distributions are shown to be conjugate and belong to the inverse gamma and inverse Wishart families. While Bayesian methodology is increasingly being used for analyzing MET data, to the best of our knowledge, this study comprises one of the first serious attempts to objectively inform priors in the context of MET data. This refers to the proposed Bayesian updating approach. To demonstrate the framework, we consider an application where posterior variance component samples are plugged into an A-optimality experimental design criterion to determine the average optimal allocations of trials to agro-ecological zones in a sub-divided target population of environments (TPE).

EVEN supervised learning is subject to the famous NoFree Lunch Theorems [1]-[3], which say that, in combinatorial optimization, there is no universal algorithm that works better than its competitors for every objective function [4]-[6]. Indeed, David Wolpert has recently proven that, on average, cross-validation performs as well as anti-crossvalidation (choosing among a set of candidate algorithms based on which has the worst out-of-sample behavior) for supervised learning. Still, he acknowledges that "it is hard to imagine any scientist who would not prefer to use [crossvalidation] to using anti-cross-validation" [7]. On the other hand, unsupervised learning has seldom been studied from the perspective of the NFLTs. This may be because the adjective "unsupervised" suggests that no human input is needed, which is misleading as many unsupervised tasks are combinatorial optimization problems that depend on the choice of the objective function. For instance, it is well known that, among the eigenvectors of the covariance matrix, Principal Components Analysis selects those with the largest variances [8]. However, mode-hunting techniques that rely on spectral manipulation aim at the opposite objective: selecting the eigenvectors of the covariance matrix with the smallest variances [9], [10]. Therefore, unlike in supervised learning, where it is difficult to identify reasons to optimize with respect to anti-cross-validation, in unsupervised learning there are strong reasons to reduce dimensionality for variance minimization. D. A. D ıaz-Pach on and T. Liu are with the Division of Biostatistics, University of Miami, Miami, FL, 33136 USA (e-mail: ddiaz3@miami.edu,

Physics-Informed Neural Networks (PINNs) often suffer from slow convergence, training instability, and reduced accuracy on challenging partial differential equations due to the anisotropic and rapidly varying geometry of their loss landscapes. We propose a lightweight curvature-aware optimization framework that augments existing first-order optimizers with an adaptive predictive correction based on secant information. Consecutive gradient differences are used as a cheap proxy for local geometric change, together with a step-normalized secant curvature indicator to control the correction strength. The framework is plug-and-play, computationally efficient, and broadly compatible with existing optimizers, without explicitly forming second-order matrices. Experiments on diverse PDE benchmarks show consistent improvements in convergence speed, training stability, and solution accuracy over standard optimizers and strong baselines, including on the high-dimensional heat equation, Gray--Scott system, Belousov--Zhabotinsky system, and 2D Kuramoto--Sivashinsky system.

In finite-width deep neural networks, the empirical kernel $G$ evolves stochastically across layers. We develop a collective kernel effective field theory (EFT) for pre-activation ResNets based on a $G$-only closure hierarchy and diagnose its finite validity window. Exploiting the exact conditional Gaussianity of residual increments, we derive an exact stochastic recursion for $G$. Applying Gaussian approximations systematically yields a continuous-depth ODE system for the mean kernel $K_0$, the kernel covariance $V_4$, and the $1/n$ mean correction $K_{1,\mathrm{EFT}}$, which emerges diagrammatically as a one-loop tadpole correction. Numerically, $K_0$ remains accurate at all depths. However, the $V_4$ equation residual accumulates to an $O(1)$ error at finite time, primarily driven by approximation errors in the $G$-only transport term. Furthermore, $K_{1,\mathrm{EFT}}$ fails due to the breakdown of the source closure, which exhibits a systematic mismatch even at initialization. These findings highlight the limitations of $G$-only state-space reduction and suggest extending the state space to incorporate the sigma-kernel.