WNBA superstar Caitlin Clark will be the grand marshal of this year's Indianapolis 500 Victor Wembanyama's historic game one performance was personal, Spurs star reveals in postgame interview Dana White says gnats at Trump's White House Rose Garden dinner raised concerns for outdoor UFC events High school athlete slams CIF's shared podium rule as humiliating response that fails female competitors Kuwaiti Muslim jiu-jitsu champion refuses Israeli athlete's handshake: 'We do not respect them at all' Caitlin Clark's fiery Fever teammate tells WNBA haters to relax with perfect three-word response Red Sox legend Jason Varitek's wife appears to take massive swipe at team after ugly ouster Taiwan warns US about China's regional ambitions as Trump weighs arms deal Nate Bargatze takes clean comedy to big screen with'The Breadwinner' Retired vice admiral on Iran standoff: Trump has'time on his hands' Jury dismisses Elon Musk's lawsuit against OpenAI and Sam Altman Strikes must resume if Iran fails to negotiate'in good faith': Brig Gen John Teichert Trace Gallagher: What does liberal America want? 'Rededicate 250' faith event draws thousands to DC OutKick-Sports Brendan Sorsby's lawsuit against NCAA could set a dangerous precedent in college sports moving forward Chris Fallica weighs in on the Brendan Sorby sports betting incident. Fallica is skeptical on if Sorsby will even play college sports again after checking himself into a betting rehab. Brendan Sorsby's college football career should likely be over, according to rules put in place, after the gambling revelations detailed this week in a lawsuit filed against the NCAA by his own attorneys. At a time when athletes are suing the NCAA over nearly every restriction tied to earning opportunities, this case feels far more straightforward.

Estimating the Most Important Person (MIP) in any social event setup is a challenging problem mainly due to contextual complexity and scarcity of labeled data. Moreover, the causality aspects of MIP estimation are quite subjective and diverse. To this end, we aim to address the problem by annotating a large-scale `in-the-wild' dataset for identifying human perceptions about the `Most Important Person (MIP)' in an image. The paper provides a thorough description of our proposed Multimodal Large Language Model (MLLM) based data annotation strategy, and a thorough data quality analysis. Further, we perform a comprehensive benchmarking of the proposed dataset utilizing state-of-the-art MIP localization methods, indicating a significant drop in performance compared to existing datasets. The performance drop shows that the existing MIP localization algorithms must be more robust with respect to `in-the-wild' situations. We believe the proposed dataset will play a vital role in building the next-generation social situation understanding methods. The code and data is available at https://github.com/surbhimadan92/MIP-GAF.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The NCAA's proposal to permit athletes to earn money from endorsements would stand in the way of players' names, images and likenesses being used in EA Sports' new college football video game. Until that changes, Notre Dame doesn't want to be in the game. The Fighting Irish are not alone among major college football programs passing on inclusion in the rebooted game until players can get paid to be in it, too.

Nonnegative matrix factorization (NMF) is a widely used linear dimensionality reduction technique for nonnegative data. NMF requires that each data point is approximated by a convex combination of basis elements. Archetypal analysis (AA), also referred to as convex NMF, is a well-known NMF variant imposing that the basis elements are themselves convex combinations of the data points. AA has the advantage to be more interpretable than NMF because the basis elements are directly constructed from the data points. However, it usually suffers from a high data fitting error because the basis elements are constrained to be contained in the convex cone of the data points. In this letter, we introduce near-convex archetypal analysis (NCAA) which combines the advantages of both AA and NMF. As for AA, the basis vectors are required to be linear combinations of the data points and hence are easily interpretable. As for NMF, the additional flexibility in choosing the basis elements allows NCAA to have a low data fitting error. We show that NCAA compares favorably with a state-of-the-art minimum-volume NMF method on synthetic datasets and on a real-world hyperspectral image.

This year, 47 million Americans will spend an estimated $8.5 billion betting on the outcome of the NCAA basketball championships, a cultural ritual appropriately known as March Madness. Before the tournament starts, anyone who wants to place a bet must fill out a bracket, which holds their predictions for each of the 63 championship games. The winner of a betting pool is the one whose bracket most closely mirrors the results of the championship. For most people, making a bracket is a way to flex their knowledge of collegiate basketball and maybe make a few bucks by outguessing their colleagues in the office betting pool. But for the mathematically inclined, accurately predicting March Madness brackets is a technical problem in search of a solution.

SportsPulse: Trysta Krick runs through surprises, snubs and other highlights from Selection Sunday as March Madness officially begins. About one in five Americans will fill out brackets for March Madness, most novices at evaluating men's college hoops. Adobe lent a virtual hand as it launched #HackTheBracket on Monday to give visitors (at no cost) a chance to delve deep into each matchup or just use the technology giant's artificial intelligence-powered engine to predict the winner. "Analytics have been, mostly, for hardcore data heads," Trevor Paulsen, senior product manager for Adobe Analytics Cloud, told USA TODAY Sports. "For the last five of six years, it's been a hobby for some of the engineers here. We built predictive models to see how they'd do internally."