Get ready for summer with discounts on robot pool cleaners from Beatbot. National Pool Opening Day is tomorrow, April 25, and summer is almost here, which means pool owners everywhere are getting ready to unveil the horrors of whatever happened during the off-season. Most of the Beatbot lineup is on sale at Amazon and Beatbot's own storefront, with prices starting at $499. Beatbot makes many of the best pool-cleaning robots we've tested, and we've highlighted our top picks below. Note that the discounts are scheduled to end on April 26, though items may sell out sooner.

A piece of the UFC White House event's setup is sitting in Pennsylvania Amish country Viral Ottawa Senators fan blamed for team's 0-2 playoff start banished to Taiwan 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 Trump weighs in on Iran's internal power struggle and Strait of Hormuz control 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 Silver says owner Robert Pera has no interest in relocating but wants the Grizz to be'Tennessee's team' Rumors of the Memphis Grizzlies potentially leaving the Bluff City are nothing new, but they've gotten louder in recent months on the heels of the franchise's worst season in nearly a decade. NBA commissioner Adam Silver, however, recently explained that Memphians have nothing to worry about, but did offer up a suggestion for the team that some fans may be hesitant to commit to. Silver recently joined the Pardon My Take podcast and, for the most part, delivered the Memphis-friendly message. NBA Commissioner Adam Silver held a press conference at Chase Center in San Francisco, Calif., on Feb. 15, 2025, during NBA All-Star weekend. There's no reason why the Memphis Grizzlies can't be successful.

Do the Dodgers get an unfair advantage with'bizarre' rule impacting Shohei Ohtani? Florida's Thomas Haugh ditches Draft to return to school amid swirling Todd Golden rumors Mamdani takes'Curse of the Mambino' on the chin as Mets' 11-game skid sets franchise record Cubs' co-owner pushes back on woke backlash Matt Shaw received for attending Charlie Kirk's memorial'Zig-Zag Theory': Houston Rockets will cover and even series vs. Los Angeles Lakers in Game 2 Stephen A. Smith says he believes Dianna Russini and Mike Vrabel are innocent -- for now Wisconsin teen's turkey hunt takes a wild turn when a bobcat lunges and grabs his arm on camera Matt Fitzpatrick is the king of Harbour Town, lame USA chants, and LIV Golf's telling announcement Dana Perino: Economic pressure on Iranian regime is'excruciating' IRGC's'extreme commanders' abused Iranian people for decades: Ret Lt Col Chuck DeVore Missing scientists probe was reportedly sparked after'UFO General' disappeared Iranian leaders say they don't negotiate'under the shadow of threats' VP Vance's Pakistan trip suspended as Trump weighs diplomacy vs force on Iran Former Virginia governor condemns Dem redistricting plan as'illegal power grab' California Dems warned of'blue Armageddon' in governor race This is how the US can'pressure' the Iranian regime: Former leading CENTCOM official Democratic Senate candidate criticized for remarks about JD Vance's family A stretcher crew stood by but there wasn't much left to carry off after the robot's wild self-destruction A humanoid robot, 'Lightning,' shattered the Beijing half-marathon world record this weekend, completing the race in just 50 minutes and 26 seconds, 13 miles faster than any human. Cyber expert Kurt Knutsson warns this massive leap in artificial intelligence, along with Tesla's Optimus robot, necessitates a universal'off button' and stronger guardrails to ensure safety and prevent future human replacement. I'm equally excited and terrified by it. On one hand, I'd love to have a robot around the house so it could fold my laundry and make me feel like George Jetson.

Japanese scientist Makoto Kuroda faces felony charges after allegedly poisoning a colleague's water bottle with toxic chemicals at UW-Madison's Influenza Research Institute.

Taylor Sheridan's new war movie gets major update, legendary director attached LPGA star Nelly Korda sizzles on the beach, Dems won't stop dancing & Gia Duddy whips up a bikini lunch Paige Spiranac provides an update on'Great Cans' saga, fan's still MIA but others have picked up the slack Ivanka Trump has the angry libs on high alert as she slides into an amazing dress, Waffle House chaos & MEAT! Donald Trump makes odd'hair' comment to Danica Patrick at TPUSA event Islamabad enters'red zone' lockdown ahead of expected US-Iran peace talks Holocaust survivor known as'Crossing Guard Diva' goes viral for glam style House Ethics Committee weighs action against Rep. Cherfilus-McCormick'Sinister' links suspected in mysterious deaths of scientists Welcome to the numerous new Screencaps readers - trust me, you have to give this column two weeks to understand what's going on If you are one of the hundreds of thousands of new Screencaps readers who found this column on Monday, welcome back. You're about to become hooked. Just go ahead and clear your daily schedule at 9 a.m. for America's Best Daily Column, as named by the readers who've been with me for years. In some cases, readers have been with me for over a decade. This column is their talk radio.

Bayesian experimental design (BED) for complex physical systems is often limited by the nested inference required to estimate the expected information gain (EIG) or its gradients. Each outer sample induces a different posterior, creating a large and heterogeneous set of inference targets. Existing methods have to sacrifice either accuracy or efficiency: they either perform per-outer-sample posterior inference, which yields higher fidelity but at prohibitive computational cost, or amortize the inner inference across all outer samples for computational reuse, at the risk of degraded accuracy under posterior heterogeneity. To improve accuracy and maintain cost at the amortized level, we propose a grouped geometric pooled posterior framework that partitions outer samples into groups and constructs a pooled proposal for each group. While such grouping strategy would normally require generating separate proposal samples for different groups, our tailored ensemble Kalman inversion (EKI) formulation generates these samples without extra forward-model evaluation cost. We also introduce a conservative diagnostic to assess importance-sampling quality to guide grouping. This grouping strategy improves within-group proposal-target alignment, yielding more accurate and stable estimators while keeping the cost comparable to amortized approaches. We evaluate the performance of our method on both Gaussian-linear and high-dimensional network-based model discrepancy calibration problems.

In this work, we revisit the problem of active sequential prediction-powered mean estimation, where at each round one must decide the query probability of the ground-truth label upon observing the covariates of a sample. Furthermore, if the label is not queried, the prediction from a machine learning model is used instead. Prior work proposed an elegant scheme that determines the query probability by combining an uncertainty-based suggestion with a constant probability that encodes a soft constraint on the query probability. We explored different values of the mixing parameter and observed an intriguing empirical pattern: the smallest confidence width tends to occur when the weight on the constant probability is close to one, thereby reducing the influence of the uncertainty-based component. Motivated by this observation, we develop a non-asymptotic analysis of the estimator and establish a data-dependent bound on its confidence interval. Our analysis further suggests that when a no-regret learning approach is used to determine the query probability and control this bound, the query probability converges to the constraint of the max value of the query probability when it is chosen obliviously to the current covariates. We also conduct simulations that corroborate these theoretical findings.

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' 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' 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 Life-threatening cantaloupe recall in four states upgraded to FDA's highest risk level... 'reasonable probability of death' Fury as murderer marries pen pal behind bars... as teenage victim's mom says: 'I'm serving a life sentence without my son' Kate and William join Charles and Camilla in celebrating British centenarians at Buckingham Palace as Royal Family marks the late Queen's 100th birthday US troops board second tanker as Trump accuses Iran of violating ceasefire'numerous times' - Live updates 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 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'You who have come from Europe are not going to return - I will sacrifice you': Chilling rant to tourists by Mexican pyramid gunman before he killed one and wounded seven others Autistic woman, 24, worked hard to build independent life for herself... now she's PARALYZED thanks to selfishness of stranger Even Cameron Diaz admits she's a dirty mess. I'll get hate for saying it, but we're all thinking the same thing about THAT wrinkled forehead: CAROLINE BULLOCK I tested a Marvel-style exoskeleton - so, can it really turn me into an athlete?

Feature selection is a classical problem in statistics and machine learning, and it continues to remain an extremely challenging problem especially in the context of unknown non-linear relationships with dependent features. On the other hand, Shapley values are a classic solution concept from cooperative game theory that is widely used for feature attribution in general non-linear models with highly-dependent features. However, Shapley values are not naturally suited for feature selection since they tend to capture both direct effects from each feature to the response and indirect effects through other features. In this paper, we combine the advantages of Shapley values and adapt them to feature selection by proposing \emph{MinShap}, a modification of the Shapley value framework along with a suite of other related algorithms. In particular for MinShap, instead of taking the average marginal contributions over permutations of features, considers the minimum marginal contribution across permutations. We provide a theoretical foundation motivated by the faithfulness assumption in DAG (directed acyclic graphical models), a guarantee for the Type I error of MinShap, and show through numerical simulations and real data experiments that MinShap tends to outperform state-of-the-art feature selection algorithms such as LOCO, GCM and Lasso in terms of both accuracy and stability. We also introduce a suite of algorithms related to MinShap by using the multiple testing/p-value perspective that improves performance in lower-sample settings and provide supporting theoretical guarantees.

Clinical decision-making often involves selecting tests that are costly, invasive, or time-consuming, motivating individualized, sequential strategies for what to measure and when to stop ascertaining. We study the problem of learning cost-optimal sequential decision policies from retrospective data, where test availability depends on prior results, inducing informative missingness. Under a sequential missing-at-random mechanism, we develop a doubly robust Q-learning framework for estimating optimal policies. The method introduces path-specific inverse probability weights that account for heterogeneous test trajectories and satisfy a normalization property conditional on the observed history. By combining these weights with auxiliary contrast models, we construct orthogonal pseudo-outcomes that enable unbiased policy learning when either the acquisition model or the contrast model is correctly specified. We establish oracle inequalities for the stage-wise contrast estimators, along with convergence rates, regret bounds, and misclassification rates for the learned policy. Simulations demonstrate improved cost-adjusted performance over weighted and complete-case baselines, and an application to a prostate cancer cohort study illustrates how the method reduces testing cost without compromising predictive accuracy.