The GOP's Attacks on James Talarico Are Straight Out of the Incel Handbook Claims about low testosterone and false accusations of veganism might play well to the online far right, but will they win an election? Democratic US Senate candidate James Talarico speaks in Houston, Texas. On Tuesday, with Donald Trump's endorsement and the backing of the MAGA faithful, scandal-ridden Texas attorney general Ken Paxton defeated incumbent US senator John Cornyn in a runoff primary to claim the Republican nomination for that seat. He then quickly set about painting his general-election opponent, Democratic Texas state representative James Talarico, as insufficiently masculine. "My opponent is the most extreme radical that Democrats have ever nominated," Paxton said in his victory speech.

AI Tools Are Transforming Muslim Worship. Tarique Kazi used to recite the Quran to his mother. Kazi is a 32-year-old Houston-based Muslim and teacher of hifdh--the Islamic practice of memorizing the Quran in order to deepen faith. For Kazi, the hours he spent with his mother studying the sacred text were among his most cherished. "It was the most beautiful thing that I always looked forward to my mom giving me feedback, telling me how I did," he tells TIME.

The 285-pound pachyderm was up and running right after birth. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Sam Houston is the fourth generation of an elephant family at the Fort Worth Zoo. Breakthroughs, discoveries, and DIY tips sent six days a week. On April 1, Texas' Fort Worth Zoo welcomed a 285-pound baby into the world.

We introduce a novel framework for constructing scalable and flexible covariance kernels for Gaussian processes (GPs) by directly learning the covariance structure under a regression-type parameterization induced by Vecchia approximations, using deep neural architectures. Specifically, we model kriging coefficients and conditional standard deviations, deterministic quantities that uniquely characterize the covariance, providing stable and informative learning targets. Exploiting the permutation-equivariant structure of conditioning sets in the Vecchia factorization, we derive a universal representation for permutation-preserving functions and design neural architectures that respect this symmetry, leading to improved training stability and data efficiency. The proposed approach enables expressive, non-stationary kernel learning while maintaining computational scalability, thereby bridging classical GP methodology with modern deep learning.

Kevin Durant is expected to sit out Game 4 with a sprained left ankle and bone bruise, as the Rockets face elimination against the Lakers on Sunday night.

Edward Cabrera's strikeout prop is the play as struggling Phillies face surging Cubs today Nuggets vs Timberwolves Game 3 pick hinges on Jaden McDaniels calling out Denver's entire defense Charles Barkley was disgusted by Magic's highly questionable pregame handshake ChatGPT predicted the first round of the NFL Draft and here's what it said Curt Cignetti was so focused this offseason, he turned down all external requests: 'I'm 95% football' Former MLB owner claims'despicable' San Francisco Giants are the reason the A's left Oakland Longtime NASCAR crew chief tells wild story about one of the sport's biggest characters WNBA finally embraces Caitlin Clark's stardom with unprecedented national TV schedule Why are the Mets so bad? Flyers mascot Gritty pens letter to fans ahead of first playoff game... eight years after he debuted Hasan Piker justifies'social murder' of CEO Fox News celebrates'Bring Your Kids to Work Day' Trump says there's'no time frame' to secure Iran deal Iranian activist praises Trump's intervention after female protesters saved from execution Steve Hilton praised for'offering solutions' in CA gubernatorial debate Middle East tensions escalate over US blockade, Iran's actions We had a homer land on top of the foul pole, and a line drive land in a pitcher's shirt Jo Adell just pulled off something you may NEVER see again -- robbing THREE home runs in a single game vs the Mariners. Is this the greatest defensive performance in MLB history? Ricky Cobb reacts like only the Super 70s Sports Guy can .... All eyes are on today's NFL Draft, but I doubt it'll produce anything like what Major League Baseball gave us Wednesday night.

Send the pool guy packing. One of these robotic buddies can maintain your water quality instead. Cleaning swimming pools is not fun. I learned this simple logic as a kid growing up in and around pools--it's the only way to survive summer in Houston, Texas. Four years ago, I became a pool owner myself, and I found that the rule still holds. Jumping into the pool on a hot day remains a rare treat, but if the pool is filled with leaves and dirt, that treat becomes a lot less delightful. And when the thermometer is reading over 100 degrees Fahrenheit, the thought of laboring on the pool deck, scooping out debris with a net, is downright cruel.

Transformer-based scientific foundation models are increasingly deployed in high-stakes settings, but current architectures give deterministic outputs and provide limited support for calibrated predictive uncertainty. We propose Stochastic Attention, a lightweight inference-time modification that randomizes attention by replacing softmax weights with normalized multinomial samples controlled by a single concentration parameter, and produces predictive ensembles without retraining. To set this parameter, we introduce a calibration objective that matches the stochastic attention output with the target, yielding an efficient univariate post-hoc tuning problem. We evaluate this mechanism on two scientific foundation models for weather and timeseries forecasting along with an additional regression task. Across benchmarks against uncertainty-aware baselines, we find that Stochastic Attention achieves the strongest native calibration and the sharpest prediction intervals at comparable coverage, while requiring only minutes of post-hoc tuning versus days of retraining for competitive baselines.

Sam Altman speaks during the BlackRock infrastructure summit on 11 March in Washington DC. Sam Altman speaks during the BlackRock infrastructure summit on 11 March in Washington DC. How a fiery attack on Sam Altman's home unfolded Molotov cocktail attack on OpenAI CEO's home comes amid growing discontent against artificial intelligence I n the early hours of 10 April, a man approached the gate of OpenAI CEO Sam Altman's house in San Francisco and hurled a molotov cocktail at the building before fleeing. Federal and California state authorities have charged Moreno-Gama with a range of crimes including attempted arson and attempted murder. His parents issued a statement this week saying that their son had recently suffered a mental health crisis.

Fréchet regression, or conditional Barycenters, is a flexible framework for modeling relationships between covariates (usually Euclidean) and response variables on general metric spaces, e.g., probability distributions or positive definite matrices. However, in contrast to classical barycenter problems, computing conditional counterparts in many non-Euclidean spaces remains an open challenge, as they yield non-convex optimization problems with an affine structure. In this work, we study the existence and computation of conditional barycenters, specifically in the space of positive-definite matrices with the Bures-Wasserstein metric. We provide a sufficient condition for the existence of a minimizer of the conditional barycenter problem that characterizes the regression range of extrapolation. Moreover, we further characterize the optimization landscape, proving that under this condition, the objective is free of local maxima. Additionally, we develop a projection-free and provably correct algorithm for the approximate computation of first-order stationary points. Finally, we provide a stochastic reformulation that enables the use of off-the-shelf stochastic Riemannian optimization methods for large-scale setups. Numerical experiments validate the performance of the proposed methods on regression problems of real-world biological networks and on large-scale synthetic Diffusion Tensor Imaging problems.