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.

Sample-wise learning curves plot performance versus training set size. They are useful for studying scaling laws and speeding up hyperparameter tuning and model selection. Learning curves are often assumed to be well-behaved: monotone (i.e. improving with more data) and convex. By constructing the Learning Curves Database 1.1 (LCDB 1.1), a large-scale database with high-resolution learning curves including more modern learners (CatBoost, TabNet, RealMLP and TabPFN), we show that learning curves are less often well-behaved than previously thought. Using statistically rigorous methods, we observe significant ill-behavior in approximately 15% of the learning curves, almost twice as much as in previous estimates. We also identify which learners are to blame and show that specific learners are more ill-behaved than others. Additionally, we demonstrate that different feature scalings rarely resolve ill-behavior. We evaluate the impact of ill-behavior on downstream tasks, such as learning curve fitting and model selection, and find it poses significant challenges, underscoring the relevance and potential of LCDB 1.1 as a challenging benchmark for future research.

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.

Accurately predicting conversion rates (CVR) for low-activity users remains a fundamental challenge in large-scale e-commerce recommender systems. Existing approaches face three critical limitations: (i) reliance on noisy and unreliable behavioral signals; (ii) insufficient user-level information due to the lack of diverse interaction data; and (iii) a systemic training bias toward high-activity users that overshadows the needs of low-activity users. To address these challenges, we propose ChoirRec, a novel framework that leverages the semantic capabilities of Large Language Models (LLMs) to construct semantic user groups and enhance CVR prediction for low-activity users. With a dual-channel architecture designed for robust cross-user knowledge transfer, ChoirRec comprises three components: (i) a Semantic Group Generation module that utilizes LLMs to form reliable, cross-activity user clusters, thereby filtering out noisy signals; (ii) a Group-aware Hierarchical Representation module that enriches sparse user embeddings with informative group-level priors to mitigate data insufficiency; and (iii) a Group-aware Multi-granularity Modual that employs a dual-channel architecture and adaptive fusion mechanism to ensure effective learning and utilization of group knowledge. We conduct extensive offline and online experiments on Taobao, a leading industrial-scale e-commerce platform. ChoirRec improves GAUC by 1.16\% in offline evaluations, while online A/B testing reveals a 7.24\% increase in order volume, highlighting its substantial practical value in real-world applications.

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.

Legal Article Prediction (LAP) is a critical task in legal text classification, leveraging natural language processing (NLP) techniques to automatically predict relevant legal articles based on the fact descriptions of cases. As a foundational step in legal decision-making, LAP plays a pivotal role in determining subsequent judgments, such as charges and penalties. Despite its importance, existing methods face significant challenges in addressing the complexities of LAP. Supervised classification models (SCMs), such as CNN and BERT, struggle to fully capture intricate fact patterns due to their inherent limitations. Conversely, large language models (LLMs), while excelling in generative tasks, perform suboptimally in predictive scenarios due to the abstract and ID-based nature of legal articles. Furthermore, the diversity of legal systems across jurisdictions exacerbates the issue, as most approaches are tailored to specific countries and lack broader applicability. To address these limitations, we propose Uni-LAP, a universal framework for legal article prediction that integrates the strengths of SCMs and LLMs through tight collaboration. Specifically, in Uni-LAP, the SCM is enhanced with a novel Top-K loss function to generate accurate candidate articles, while the LLM employs syllogism-inspired reasoning to refine the final predictions. We evaluated Uni-LAP on datasets from multiple jurisdictions, and empirical results demonstrate that our approach consistently outperforms existing baselines, showcasing its effectiveness and generalizability.