Leaked recording reveals Campbell's exec's sickening remarks about iconic soup's ingredients How Lauren Sanchez would REALLY look if she'd never had rumored plastic surgery Trump's losing control... MAGA's imploding... and White House insiders tell me why they're REALLY worried: ANDREW NEIL Billionaire family posts VERY unusual obituary after heir, 40, met violent end at $2.8m hunting lodge following marriage scandal These women have lost as much as nine stone WITHOUT jabs: Now they reveal secret to their stunning success, the extraordinary event that brought them together and how it's changed their lives... Judge throws out Comey and James cases as Trump's beauty queen prosecutor is humiliated Her moving videos about the handsome boyfriend who ghosted her went viral and catapulted her to overnight fame. Kate Gosselin's ex Jon is seen at his splashy wedding for the first time as son Collin weighs in on his siblings not attending Fugitive'Slender Man' stabber Morgan Geyser snapped'just Google me' when asked for ID by cops who found her with MUCH older lover It all seems to be falling apart now! Pete Hegseth drops hammer on Democrat senator in'sedition' storm as court martial looms after Trump's execution threat Sabrina Carpenter looks unrecognisable in throwback snap from seven years ago as fans call her rebranding'wild' Neuralink's'Patient 4' feared missing months after getting revolutionary brain chip... now his wife tells the REAL heartbreaking story NFL's first transgender cheerleader makes explosive allegation against Carolina Panthers Slash your cholesterol by a third in just a month... hundreds of thousands are on a new diet that's transforming lives. Inside the world's longest underwater cave: Subterranean water'web' in Mexico extends at least 325 MILES Beneath the idyllic resort towns of Mexico's Yucatan Peninsula, daring explorers have uncovered a hidden world of grand chambers and twisting tunnels. The Ox Bel Ha, Mayan for'Three Paths of Water', is a sprawling water'web' that makes up the world's longest underwater cave system.

Ix Ch'ak Ch'een was one of at least four women who oversaw the city of Cobá. Breakthroughs, discoveries, and DIY tips sent every weekday. Ongoing analysis of an ancient monument among the Mayan ruins at Cobá has revealed the identity of one of the sprawling city's previously unknown rulers. According to archaeologists with Mexico's National Institute of Anthropology and History (INAH), the king referenced multiple times in the historical accounts described on the city's Foundation Rock wasn't a king at all. She was a queen named Ix Ch'ak Ch'een.

Disney superfan, 31, vanishes from her Midwest home months after announcing pregnancy... then horrific discovery is made at Walt Disney World Pete Hegseth's jet makes emergency landing in Britain after high-stakes NATO summit on Russia-Ukraine war Doctor's husband'was watching X-rated videos in his house while daughter, two, died in roasting car outside' Bella Hadid's health battle takes dark turn: Loved ones reveal hellish new details about'missing' model... as ominous texts emerge Trump hails'beautiful black women' strutting Chicago in MAGA hats Trump says he'll go to the Supreme Court to watch tariff arguments Charlie Kirk suspect invokes Bryan Kohberger as he makes clothing demand to seem'more human' America's saddest lost soul can no longer SPEAK and spends days hitting herself'after years of unspeakable abuse by gangs of men' Virginia Giuffre calls Prince Andrew'entitled' and claims duke saw having sex with her as his'birthright' in autobiography released after her death'You will DIE if you do not remove your breasts', doctors screamed at me. I refused and tried a new experimental therapy instead... now I'm cancer-free Warning over'life-threatening' storm brewing in Atlantic that could hit US Will Trump's Gaza peace deal fail? Policy expert MARK DUBOWITZ breaks down all the forces at play... and how the president can actually pull this off America's nuclear bombers spotted on mission over Venezuela as conflict escalates Astonishing interactive map lays bare where MILLIONS of homes will be submerged by water within a few years... are YOU at risk? The View's Joy Behar reveals the TRUTH behind her ageless appearance aged 83 Trump ORDERS troops to be paid as'hatchet man' floats 10,000 job cuts amid government shutdown America's nuclear bombers spotted on mission over Venezuela as conflict escalates READ MORE: Trump strikes'narco-terrorist' boat killing six as Venezuela warns of full-scale US invasion A trio of US B-52H Stratofortress bombers was spotted flying near Venezuelan airspace in what some analysts are calling a bold display of military power. Flight tracking data shows all three bombers departed from Louisiana's Barksdale Air Force Base in Shreveport, starting at 2:50am ET.

Despite the remarkable capabilities demonstrated by Graph Neural Networks (GNNs) in graph-related tasks, recent research has revealed the fairness vulnerabilities in GNNs when facing malicious adversarial attacks. However, all existing fairness attacks require manipulating the connectivity between existing nodes, which may be prohibited in reality. To this end, we introduce a N ode I njection-based F airness A ttack (NIFA), exploring the vulnerabilities of GNN fairness in such a more realistic setting. In detail, NIFA first designs two insightful principles for node injection operations, namely the uncertainty-maximization principle and homophily-increase principle, and then optimizes injected nodes' feature matrix to further ensure the effectiveness of fairness attacks. Comprehensive experiments on three real-world datasets consistently demonstrate that NIFA can significantly undermine the fairness of mainstream GNNs, even including fairness-aware GNNs, by injecting merely 1% of nodes. We sincerely hope that our work can stimulate increasing attention from researchers on the vulnerability of GNN fairness, and encourage the development of corresponding defense mechanisms.

Training recommender systems for next-item recommendation often requires unique embeddings to be learned for each item, which may take up most of the trainable parameters for a model. Shared embeddings, such as using content information, can reduce the number of distinct embeddings to be stored in memory. This allows for a more lightweight model; correspondingly, model complexity can be increased due to having fewer embeddings to store in memory. We show the benefit of using shared content-based features ('semantic IDs') in improving recommendation accuracy and diversity, while reducing model size, for two music recommendation datasets, including an online A/B test on a music streaming service.

Opinion dynamics models such as the bounded confidence models (BCMs) describe how a population can reach consensus, fragmentation, or polarization, depending on a few parameters. Connecting such models to real-world data could help understanding such phenomena, testing model assumptions. To this end, estimation of model parameters is a key aspect, and maximum likelihood estimation provides a principled way to tackle it. Here, our goal is to outline the properties of statistical estimators of the two key BCM parameters: the confidence bound and the convergence rate. We find that their maximum likelihood estimators present different characteristics: the one for the confidence bound presents a small-sample bias but is consistent, while the estimator of the convergence rate shows a persistent bias. Moreover, the joint parameter estimation is affected by identifiability issues for specific regions of the parameter space, as several local maxima are present in the likelihood function. Our results show how the analysis of the likelihood function is a fruitful approach for better understanding the pitfalls and possibilities of estimating the parameters of opinion dynamics models, and more in general, agent-based models, and for offering formal guarantees for their calibration.

Large Language Models (LLMs) encapsulate a surprising amount of factual world knowledge. However, their performance on temporal questions and historical knowledge is limited because they often cannot understand temporal scope and orientation or neglect the temporal aspect altogether. In this study, we aim to measure precisely how robust LLMs are for question answering based on their ability to process temporal information and perform tasks requiring temporal reasoning and temporal factual knowledge. Specifically, we design eight time-sensitive robustness tests for factual information to check the sensitivity of six popular LLMs in the zero-shot setting. Overall, we find LLMs lacking temporal robustness, especially to temporal reformulations and the use of different granularities of temporal references. We show how a selection of these eight tests can be used automatically to judge a model's temporal robustness for user questions on the fly. Finally, we apply the findings of this study to improve the temporal QA performance by up to 55 percent.