Take the Portland Trail Blazers +2.5 in Game 3 Shocker! Kyle Brandt-Seth Rollins on-set spat was staged Tigers look to exploit Reds' struggles at home as Framber Valdez takes the mound in Cincinnati Watch as Eagles steal Makai Lemon with wild phone call: 'Why is Philly calling me?' Giants' draft pick has intense Jaxson Dart message: 'I'm ready to die for you' Donald Trump uses Pete Rose to justify soldier's alleged shady Maduro bet, and he's not wrong Ex-Michigan football coach Sherrone Moore's mistress reveals he got her pregnant during relationship Giants' bizarre draft decisions leave star player frustrated as true needs go unfulfilled in first round Rueben Bain's short arms and tragic car accident history contributed to his NFL Draft slide Sherrone Moore accuser Paige Shiver speaks out in new interview: he'had complete control over me' Megan Rapinoe calls on traditional WNBA media to be replaced with those who'understand queer culture' The NFL Draft continues to be one of the worst'sporting events' of the year'Fox & Friends' hosts learn country line dancing in Houston Veterans cheer Trump's order on psychedelic drugs to treat PTSD'Fox & Friends' hosts'get their Texas on' with Tecovas boots'Fox & Friends' kicks off the Fox News America 250 Tour in Houston Country artist Rich O'Toole joins'Fox & Friends' in Houston IDF finds'ambulance used by Hezbollah to conceal weapons' Hegseth shuts down reporter's EXTREME question OutKick 49ers GM John Lynch skeptical of Rams' decision to draft QB Ty Simpson with No. 13 overall pick Lynch called Simpson'a good football player' but noted the pick'surprised everybody' The San Francisco 49ers traded out of the NFL Draft's first round on Thursday, so general manager John Lynch didn't have a player to discuss when he met with reporters. No problem, because he started talking players a couple of division rivals drafted. Lynch commented on what the Arizona Cardinals and Los Angeles Rams did. San Francisco 49ers general manager John Lynch speaks at the NFL Scouting Combine at the Indiana Convention Center on Feb. 24, 2026.

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 NFL Draft prospect Rueben Bain Jr. mum about 2024 crash when publicly asked about it for first time Troy Aikman is selling'fire suites,' which are exactly what they sound like Fernando Mendoza's first pitch at Marlins game draws harsh reviews Steve Hilton praised for'offering solutions' in CA gubernatorial debate Middle East tensions escalate over US blockade, Iran's actions Michael Easter and Gary Brecka discuss the'choice' to live to be 100 Sen Ted Cruz calls new deadline with Iran'really consequential' RFK Jr confronted over'raccoon parts' on Capitol Hill Our democracy is not'in crisis,' Sen John Fetterman says The DOJ is'on the offense' here, Andrew Kolvet says OutKick ChatGPT predicted the first round of the NFL Draft and here's what it said Ultimate human vs. machine showdown as OutKick's Dan Z. takes on ChatGPT in a mock draft battle Where Is The Value In This NFL Draft? Jonathan Hutton & Chad Withrow ask Armando Salguero what position has the most value in this year's NFL draft I'm not sure why I do these things to myself, but I decided to go head-to-head with ChatGPT in a mock draft competition. I recently released my final mock draft, and then I asked ChatGPT to predict the entire first round. Below, you will see where we are the same and where we are different.

A nd so Fortnite has done it again. Over the past five years, developer Epic Games maintained the relevance and awareness of its ageing online shooter by churning out pop culture collaborations, from Marvel to John Wick to Sabrina Carpenter. For limited periods, players get to take part in the game as their favourite movie characters and music artists, an arrangement that provides refreshed audience numbers for the game - and a tidy revenue stream for the brands. This month, the Fortnite island has become a miniature Springfield, complete with popular characters and well-known locations. If you want to play as Homer and shoot up Moe's Tavern, you can.

Last week, an American-Israeli company that claims it's developed proprietary technology to cool the planet announced it had raised $60 million, by far the largest known venture capital round to date for a solar geoengineering startup. The company, Stardust, says the funding will enable it to develop a system that could be deployed by the start of the next decade, according to Heatmap, which broke the story. As scientists who have worked on the science of solar geoengineering for decades, we have grown increasingly concerned about emerging efforts to start and fund private companies to deploy technologies that could alter the climate of the planet. We also strongly dispute some of the technical claims that certain companies have made about their offerings. This story is part of Heat Exchange, MIT Technology Review's guest opinion series offering expert commentary on legal, political and regulatory issues related to climate change and clean energy. Can "The Simpsons" really predict the future?

Distinguishing correlation from causation is a fundamental challenge in machine intelligence, often representing a critical barrier to building robust and trustworthy systems. While Pearl's $\mathcal{DO}$-calculus provides a rigorous framework for causal inference, a parallel challenge lies in its physical implementation. Here, we apply and experimentally validate a quantum algorithmic framework for performing causal interventions. Our approach maps causal networks onto quantum circuits where probabilistic links are encoded by controlled-rotation gates, and interventions are realized by a structural remodeling of the circuit -- a physical analogue to Pearl's ``graph surgery''. We demonstrate the method's efficacy by resolving Simpson's Paradox in a 3-qubit model, and show its scalability by quantifying confounding bias in a 10-qubit healthcare simulation. Critically, we provide a proof-of-principle experimental validation on an IonQ Aria quantum computer, successfully reproducing the paradox and its resolution in the presence of real-world noise. This work establishes a practical pathway for quantum causal inference, offering a new computational tool to address deep-rooted challenges in algorithmic fairness and explainable AI (XAI).

AI Expert Marva Bailer explains to Fox News Digital Hank Azaria's opinion piece about humanity and AI matters. "The Simpsons" star Hank Azaria has voiced his fears over artificial intelligence in a new opinion piece. The actor, who has been with the show since 1989, wrote an opinion essay for The New York Times, worrying AI "will be able to recreate the sounds of the more than 100 voices I created for characters on'The Simpsons.'" He continued, "It makes me sad to think about it. Not to mention, it seems just plain wrong to steal my likeness or sound -- or anyone else's."

Federated learning (FL) is a promising technology for data privacy and distributed optimization, but it suffers from data imbalance and heterogeneity among clients. Existing FL methods try to solve the problems by aligning client with server model or by correcting client model with control variables. These methods excel on IID and general Non-IID data but perform mediocrely in Simpson's Paradox scenarios. Simpson's Paradox refers to the phenomenon that the trend observed on the global dataset disappears or reverses on a subset, which may lead to the fact that global model obtained through aggregation in FL does not accurately reflect the distribution of global data. Thus, we propose FedCFA, a novel FL framework employing counterfactual learning to generate counterfactual samples by replacing local data critical factors with global average data, aligning local data distributions with the global and mitigating Simpson's Paradox effects. In addition, to improve the quality of counterfactual samples, we introduce factor decorrelation (FDC) loss to reduce the correlation among features and thus improve the independence of extracted factors. We conduct extensive experiments on six datasets and verify that our method outperforms other FL methods in terms of efficiency and global model accuracy under limited communication rounds.

The aim of this work is to learn models of population dynamics of physical systems that feature stochastic and mean-field effects and that depend on physics parameters. The learned models can act as surrogates of classical numerical models to efficiently predict the system behavior over the physics parameters. Building on the Benamou-Brenier formula from optimal transport and action matching, we use a variational problem to infer parameter- and time-dependent gradient fields that represent approximations of the population dynamics. The inferred gradient fields can then be used to rapidly generate sample trajectories that mimic the dynamics of the physical system on a population level over varying physics parameters. We show that combining Monte Carlo sampling with higher-order quadrature rules is critical for accurately estimating the training objective from sample data and for stabilizing the training process. We demonstrate on Vlasov-Poisson instabilities as well as on high-dimensional particle and chaotic systems that our approach accurately predicts population dynamics over a wide range of parameters and outperforms state-of-the-art diffusion-based and flow-based modeling that simply condition on time and physics parameters.

Understanding the creation, evolution, and dissemination of scientific knowledge is crucial for bridging diverse subject areas and addressing complex global challenges such as pandemics, climate change, and ethical AI. Scientometrics, the quantitative and qualitative study of scientific literature, provides valuable insights into these processes. We introduce Scito2M, a longitudinal scientometric dataset with over two million academic publications, providing comprehensive contents information and citation graphs to support cross-disciplinary analyses. Using Scito2M, we conduct a temporal study spanning over 30 years to explore key questions in scientometrics: the evolution of academic terminology, citation patterns, and interdisciplinary knowledge exchange. Our findings reveal critical insights, such as disparities in epistemic cultures, knowledge production modes, and citation practices. For example, rapidly developing, application-driven fields like LLMs exhibit significantly shorter citation age (2.48 years) compared to traditional theoretical disciplines like oral history (9.71 years).

In this paper we demonstrate the possibility of a trend reversal in binary classification tasks between the dataset and a classification score obtained from a trained model. This trend reversal occurs for certain choices of the regularization parameter for model training, namely, if the parameter is contained in what we call the pathological regularization regime. For ridge regression, we give necessary and sufficient algebraic conditions on the dataset for the existence of a pathological regularization regime. Moreover, our results provide a data science practitioner with a hands-on tool to avoid hyperparameter choices suffering from trend reversal. We furthermore present numerical results on pathological regularization regimes for logistic regression. Finally, we draw connections to datasets exhibiting Simpson's paradox, providing a natural source of pathological datasets.