Trump's ominous warning to Colombia as acting Venezuelan president issues message to world calling for'peace and dialogue, not war' Trump plans a military'quarantine' of Venezuela's oil to strong-arm Maduro's successor I got a GLP-1 drug with few questions asked... and never meeting a doctor face-to-face. But could that convenience have put my health at risk? Addicted, arrested and dead in a hotel corridor...Victoria Jones is the latest child of a famous parent to tragically spiral. So why ARE so many children of the rich and famous cursed? Marco Rubio'runs laps' around CBS reporter who asked why US commandos didn't nab Maduro associates in daring night time raid Prince Harry'desperately wants King Charles to come to Montecito and see Archie and Lilibet' Travis Kelce finally addresses possible retirement as Chiefs lose to NFL's worst team in what could be humiliating end to his iconic career State of Jennifer Garner and Jennifer Lopez's relationship revealed by insiders... as parents gossip about'less sociable' star at school play NASA's'queen of diamonds' EXPOSED: Genius is accused of treachery over top secret mission... as chilling details emerge Michael B. Jordan's unimpressed face sends fans wild as Timothee Chalamet cries on stage over Kylie Jenner North West, 12, sparks face piercing speculation after backlash over'risky' body modification'Out-of-touch' Gayle King slammed for complaining that her upper class seat doesn't have a window on her eight-hour flight'back to work' from Hawaii American family of seven stranded after Venezuela raids say they're trapped in a living hell... while oblivious influencers BOAST about getting stuck Ten people who spread false claims France's First Lady Brigitte Macron was born a man are found guilty of cyberbullying in Paris EXPOSED: The Air Force vet who let China steal America's nuclear secrets... and KEPT his $200K tax-funded salary From'sand theft auto' to space BABIES: The global innovations and trends set to shape 2026 From the rise of the humanoid robot to the weird world of AI girlfriends, 2025 had no shortage of strange and transformative inventions. Now, experts from the Nesta research foundation have revealed the global innovations and trends set to shape the world in 2026.

Why Maduro dated wife for TWENTY years before marrying in secret... as shamed first lady's incestuous party links laid bare Maduro's brazen gestures to onlookers as he arrives at rat-infested'hell hole' prison in Brooklyn Trump says Venezuelan opposition leader who beat him to Nobel prize'does not have support' to be president Inside the CIA's astonishing coup of Caracas: SETH HARP reveals the planning... the traitor... and how it's about to blow up in American faces Inside Operation'Absolute Resolve': The meticulous months-long plan to seize Maduro and the harrowing escape under fire Leonardo DiCaprio forced to skip film festival in California by Trump's Venezuela airstrikes as he accepts acting prize via video Inside the glamorous Dubai life of Daniel Kinahan, head of the notorious crime family and Europe's top cocaine kingpin... as GUY ADAMS reveals how net is finally closing in on the gangster after golden decade in the Middle East Explosive warning from America's house price Nostradamus: New forecast tracking over 300 markets reveals'rare' shift... the boomtowns set to sink... and surprising winners I thought I'd found the perfect boyfriend. Then I discovered what he really wanted... Wild moment NYC jeweler attacks'scammer' rival and claims he is impersonating him I'm 38 and hotter than ever after finally silencing food noise with my simple new method... you don't need drugs or diets Meghan McCain, 41, joyously announces birth of third child and reveals baby's unconventional name Female college football fan labeled a Karen for furious reaction to rival in the stands... but was she right all along? Snitch reveals the TRUTH about Tom Brady and Alix Earle... after handsy video leaked out of St Barts The'ideal man' is now a 5'7 architect, dating app analysis reveals READ MORE: Dating apps are designed to keep singles'swiping and spending' The saying goes that'beauty is in the eye of the beholder'. But have you ever wondered what the'perfect' man looks like? Now, just in time for'Dating Sunday' - the busiest day of the year on dating apps - happn has uncovered what the most popular men on its dating app in 2025 look like.

Why Maduro dated wife for TWENTY years before marrying in secret... as shamed first lady's incestuous party links laid bare Maduro's brazen gestures to onlookers as he arrives at rat-infested'hell hole' prison in Brooklyn Brutal Venezuelan dictator Nicolás Maduro dramatically'seized by US as he tried to flee to panic room' Inside the CIA's astonishing coup of Caracas: SETH HARP reveals the planning... the traitor... and how it's about to blow up in American faces Thousands trapped in paradise INDEFINITELY as Trump's Maduro arrest halts all flights to and from 19 Caribbean islands Eagle eyed viewers spot odd detail in background of Trump's Mar-a-Lago war room during Maduro strike in Venezuela Inside the glamorous Dubai life of Daniel Kinahan, head of the notorious crime family and Europe's top cocaine kingpin... as GUY ADAMS reveals how net is finally closing in on the gangster after golden decade in the Middle East Explosive warning from America's house price Nostradamus: New forecast tracking over 300 markets reveals'rare' shift... the boomtowns set to sink... and surprising winners I thought I'd found the perfect boyfriend. Then I discovered what he really wanted... Emergency exit was'always locked', bartender claims amid probe into deadly Swiss ski resort inferno I'm 38 and hotter than ever after finally silencing food noise with my simple new method... you don't need drugs or diets Female college football fan labeled a Karen for furious reaction to rival in the stands... but was she right all along? Meghan McCain, 41, joyously announces birth of third child and reveals baby's unconventional name Snitch reveals the TRUTH about Tom Brady and Alix Earle... after handsy video leaked out of St Barts NFL fans go crazy as Buccaneers player Elijah Roberts' mom crushes national anthem before huge Panthers game Taylor Swift's wedding dress at BFF Este Haim's nuptials has fans spotting secret Easter Egg Today is the busiest day of the YEAR for dating apps - here's the best time to go online If you're hoping to find love in 2026, today could be a good day to get started. January 4 is the busiest day of the year for dating app activity, with the highest number of messages and likes sent. This is largely driven by New Year's resolutions and a desire for fresh starts, leading to huge spikes in users swiping, matching and messaging.

Accurate atmospheric wind field information is crucial for various applications, including weather forecasting, aviation safety, and disaster risk reduction. However, obtaining high spatiotemporal resolution wind data remains challenging due to limitations in traditional in-situ observations and remote sensing techniques, as well as the computational expense and biases of numerical weather prediction (NWP) models. This paper introduces G-WindCast, a novel deep learning framework that leverages signal strength variations from 5G Global Navigation Satellite System (GNSS) signals to forecast three-dimensional (3D) atmospheric wind fields. The framework utilizes Forward Neural Networks (FNN) and Transformer networks to capture complex, nonlinear, and spatiotemporal relationships between GNSS-derived features and wind dynamics. Our preliminary results demonstrate promising accuracy in real-time wind forecasts (up to 30 minutes lead time). The model exhibits robustness across forecast horizons and different pressure levels, and its predictions for wind fields show superior agreement with ground-based radar wind profiler compared to concurrent European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5). Furthermore, we show that the system can maintain excellent performance for localized forecasting even with a significantly reduced number of GNSS stations (e.g., around 100), highlighting its cost-effectiveness and scalability. This interdisciplinary approach underscores the transformative potential of exploiting non-traditional data sources and deep learning for advanced environmental monitoring and real-time atmospheric applications.

Monitoring coral reefs at large spatial scales remains an open challenge, essential for assessing ecosystem health and informing conservation efforts. While drone-based aerial imagery offers broad spatial coverage, its limited resolution makes it difficult to reliably distinguish fine-scale classes, such as coral morphotypes. At the same time, obtaining pixel-level annotations over large spatial extents is costly and labor-intensive, limiting the scalability of deep learning-based segmentation methods for aerial imagery. W e present a multi-scale weakly supervised semantic segmentation framework that addresses this challenge by transferring fine-scale ecological information from underwater imagery to aerial data. Our method enables large-scale coral reef mapping from drone imagery with minimal manual annotation, combining classification-based supervision, spatial interpolation and self-distillation techniques. W e demonstrate the efficacy of the approach, enabling large-area segmentation of coral morphotypes and demonstrating flexibility for integrating new classes. This study presents a scalable, cost-effective methodology for high-resolution reef monitoring, combining low-cost data collection, weakly supervised deep learning and multi-scale remote sensing.

Background. Dengue outbreaks are a major public health issue, with Brazil reporting 71% of global cases in 2024. Purpose. This study aims to describe the profile of severe dengue patients admitted to Brazilian Intensive Care units (ICUs) (2012-2024), assess trends over time, describe new onset complications while in ICU and determine the risk factors at admission to develop complications during ICU stay. Methods. We performed a prospective study of dengue patients from 253 ICUs across 56 hospitals. We used descriptive statistics to describe the dengue ICU population, logistic regression to identify risk factors for complications during the ICU stay, and a machine learning framework to predict the risk of evolving to complications. Visualisations were generated using ISARIC VERTEX. Results. Of 11,047 admissions, 1,117 admissions (10.1%) evolved to complications, including non-invasive (437 admissions) and invasive ventilation (166), vasopressor (364), blood transfusion (353) and renal replacement therapy (103). Age>80 (OR: 3.10, 95% CI: 2.02-4.92), chronic kidney disease (OR: 2.94, 2.22-3.89), liver cirrhosis (OR: 3.65, 1.82-7.04), low platelets (<50,000 cells/mm3; OR: OR: 2.25, 1.89-2.68), and high leukocytes (>7,000 cells/mm3; OR: 2.47, 2.02-3.03) were significant risk factors for complications. A machine learning tool for predicting complications was proposed, showing accurate discrimination and calibration. Conclusion. We described a large cohort of dengue patients admitted to ICUs and identified key risk factors for severe dengue complications, such as advanced age, presence of comorbidities, higher level of leukocytes and lower level of platelets. The proposed prediction tool can be used for early identification and targeted interventions to improve outcomes in dengue-endemic regions.

This study examines the role of human judges in legal decision-making by using machine learning to predict child physical custody outcomes in French appellate courts. Building on the legal realism-formalism debate, we test whether individual judges' decision-making patterns significantly influence case outcomes, challenging the assumption that judges are neutral variables that apply the law uniformly. To ensure compliance with French privacy laws, we implement a strict pseudonymization process. Our analysis uses 18,937 living arrangements rulings extracted from 10,306 cases. We compare models trained on individual judges' past rulings (specialist models) with a judge-agnostic model trained on aggregated data (generalist models). The prediction pipeline is a hybrid approach combining large language models (LLMs) for structured feature extraction and ML models for outcome prediction (RF, XGB and SVC). Our results show that specialist models consistently achieve higher predictive accuracy than the general model, with top-performing models reaching F1 scores as high as 92.85%, compared to the generalist model's 82.63% trained on 20x to 100x more samples. Specialist models capture stable individual patterns that are not transferable to other judges. In-Domain and Cross-Domain validity tests provide empirical support for legal realism, demonstrating that judicial identity plays a measurable role in legal outcomes. All data and code used will be made available.

Large language models (LLMs) often generate responses that deviate from user input or training data, a phenomenon known as "hallucination." These hallucinations undermine user trust and hinder the adoption of generative AI systems. Addressing hallucinations is essential for the advancement of LLMs. This paper introduces a comprehensive hallucination benchmark, incorporating both new extrinsic and existing intrinsic evaluation tasks, built upon clear taxonomy of hallucination. A major challenge in benchmarking hallucinations is the lack of a unified framework due to inconsistent definitions and categorizations. We disentangle LLM hallucination from "factuality," proposing a clear taxonomy that distinguishes between extrinsic and intrinsic hallucinations, to promote consistency and facilitate research. Extrinsic hallucinations, where the generated content is not consistent with the training data, are increasingly important as LLMs evolve. Our benchmark includes dynamic test set generation to mitigate data leakage and ensure robustness against such leakage. We also analyze existing benchmarks, highlighting their limitations and saturation. The work aims to: (1) establish a clear taxonomy of hallucinations, (2) introduce new extrinsic hallucination tasks, with data that can be dynamically regenerated to prevent saturation by leakage, (3) provide a comprehensive analysis of existing benchmarks, distinguishing them from factuality evaluations.