Could Ukraine hold a presidential election right now? Will Europe use frozen Russian assets to fund war? How can Ukraine rebuild China ties? 'Ukraine is running out of men, money and time' How the US left Ukraine exposed to Russia's winter war Nighttime shelling by Ukrainian forces inflicted "serious damage" on the Russian city of Belgorod, the region's Governor Vyacheslav Gladkov said. "The enemy has shelled the civilian city of Belgorod. Everyone knows we have no military targets. There has been serious damage. I have been out to look around," Gladkov said on the Telegram messaging app.

Daycare scandal deepens as unearthed video shows parents'pretending to drop kids off before they all leave just MINUTES later' Incredible Chinese military feat has chilling consequences for America and its allies as new'High North' threat emerges Everyone's getting sacked': An electrifying phone call, spiralling costs and a troubling'transition'... as Harry and Meghan's most loyal aide leaves, insiders tell ALISON BOSHOFF what's really going on behind the scenes I was told my weight gain, facial hair and fatigue were normal. Astronaut reveals depression after an'avalanche of misogyny' following Blue Origin all-female space flight The mob used Marilyn Monroe as bait to blackmail the Kennedys. And when it didn't work she was murdered... in the most obscene way George Clooney, wife Amal and their eight-year-old twins become French citizens despite the actor admitting he's'bad' at speaking the language Blonde-haired teenage girl reveals what she thinks of Elon Musk's'creepy' public lust for her CIA'carries out drone strike' on Venezuelan drug port in first US land attack inside the country I shed a staggering 100lbs WITHOUT Ozempic: How I conquered my'out of control' eating habits to transform my life with a simple change Grim details of how shark lover's body was identified after she was killed by one of the predators while swimming off California coast US strikes'terrorist boat' lurking in international waters as dramatic footage shows devastating moment of impact A Boy Scout vanished in the mountains then stumbled into a police station 12 years later. The tale gripped social media... but then the truth came out Inside the somber birthday of Rob Reiner's heartbroken daughter Romy: Pictured for first time since parents' murders... she seeks solace at the beach with boyfriend and family by her side Daycare accused of multimillion-dollar fraud shifts blame for'revealing' mistake above its front door... as kids are suddenly'trucked in' David Muir's stunning $7m lakeside retreat revealed... as locals in cozy town where ABC News star can be himself offer intriguing glimpses into his private life Loch Ness Monster has been'spotted' FIVE times this year, official records show The Loch Ness Monster was'spotted' five times in 2025, official records have revealed. The mythical creature has been a staple feature of Scottish folklore for centuries, but gained worldwide attention in 1933, when the first photo was snapped.

Prime Minister Sanae Takaichi attends a summit with five Central Asian nations in Tokyo on Saturday. Japan and five Central Asian nations adopted a joint declaration at their first summit, held in Tokyo for two days through Saturday. The declaration identifies transportation infrastructure development, decarbonization and people-to-people exchanges as three priority areas. The current rapidly changing environment surrounding Central Asia, due to recent changes in the international situation, is making regional and global cooperation more important, Prime Minister Sanae Takaichi said at the summit. The summit was also attended by the leaders of Kazakhstan, Uzbekistan, Turkmenistan, Kyrgyzstan and Tajikistan.

Notably, SAR possesses the capability to acquire geographical images under any weather conditions, irrespective of factors such as sunlight, land cover, or certain types of camouflage, as demonstrated in Figure 1(a).

While Speech Large Language Models (Speech-LLMs) show strong performance in many applications, their robustness is critically under-tested, especially to speech disfluency. Existing evaluations often rely on idealized inputs, overlooking common disfluencies, particularly those associated with conditions like Parkinson's disease. This work investigates whether current Speech-LLMs can maintain performance when interacting with users who have speech impairments. To facilitate this inquiry, we introduce VocalBench-DF, a framework for the systematic evaluation of disfluency across a multi-dimensional taxonomy. Our evaluation of 22 mainstream Speech-LLMs reveals substantial performance degradation, indicating that their real-world readiness is limited. Further analysis identifies phoneme-level processing and long-context modeling as primary bottlenecks responsible for these failures. Strengthening recognition and reasoning capability from components and pipelines can substantially improve robustness. These findings highlight the urgent need for new methods to improve disfluency handling and build truly inclusive Speech-LLMs

Integrated wind-solar-wave marine energy systems hold broad promise for supplying clean electricity in offshore and coastal regions. By leveraging the spatiotemporal complementarity of multiple resources, such systems can effectively mitigate the intermittency and volatility of single-source outputs, thereby substantially improving overall power-generation efficiency and resource utilization. Accurate ultra-short-term forecasting is crucial for ensuring secure operation and optimizing proactive dispatch. However, most existing forecasting methods construct separate models for each energy source, insufficiently account for the complex couplings among multiple energies, struggle to capture the system's nonlinear and nonstationary dynamics, and typically depend on extensive manual parameter tuning-limitations that constrain both predictive performance and practicality. We address this issue using a Bayesian-optimized Multivariate Variational Mode Decomposition-Long Short-Term Memory (MVMD-LSTM) framework. The framework first applies MVMD to jointly decompose wind, solar and wave power series so as to preserve cross-source couplings; it uses Bayesian optimization to automatically search the number of modes and the penalty parameter in the MVMD process to obtain intrinsic mode functions (IMFs); finally, an LSTM models the resulting IMFs to achieve ultra-short-term power forecasting for the integrated system. Experiments based on field measurements from an offshore integrated energy platform in China show that the proposed framework significantly outperforms benchmark models in terms of MAPE, RMSE and MAE. The results demonstrate superior predictive accuracy, robustness, and degree of automation.

Institute of Mathematics and Statistics, Federal University of Rio Grande do Sul (UFRGS), Av. Center for Coastal and Oceanic Geology Studies (CECO), Federal University of Rio Grande do Sul (UFRGS), Av. Abstract The forecast of wave variables are important for several applications that depend on a better description of the ocean state. Due to the chaotic behaviour of the differential equations which model this problem, a well know strategy to overcome the difficulties is basically to run several simulations, by for instance, varying the initial condition, and averaging the result of each of these, creating an ensemble. Moreover, in the last few years, considering the amount of available data and the computational power increase, machine learning algorithms have been applied as surrogate to traditional numerical models, yielding comparative or better results. In this work, we present a methodology to create an ensemble of different artificial neural networks architectures, namely, MLP, RNN, LSTM, CNN and a hybrid CNN-LSTM, which aims to predict significant wave height on six different locations in the Brazilian coast. The networks are trained using NOAA's numerical reforecast data and target the residual between observational data and the numerical model output. A new strategy to create the training and target datasets is demonstrated. Introduction Numerical simulations of both weather and ocean parameters rely on the evolution of nonlinear dynamical systems that have a high sensitivity on initial conditions. Considering that errors in the observations and analysis are present, and therefore in the initial conditions, the concept of a unique deterministic solution of the governing equations becomes fragile [1, 2].

Large language models (LLMs) struggle to navigate culturally specific communication norms, limiting their effectiveness in global contexts. We focus on Persian taarof, a social norm in Iranian interactions, which is a sophisticated system of ritual politeness that emphasizes deference, modesty, and indirectness, yet remains absent from existing cultural benchmarks. We introduce TaarofBench, the first benchmark for evaluating LLM understanding of taarof, comprising 450 role-play scenarios covering 12 common social interaction topics, validated by native speakers. Our evaluation of five frontier LLMs reveals substantial gaps in cultural competence, with accuracy rates 40-48% below native speakers when taarof is culturally appropriate. Performance varies between interaction topics, improves with Persian-language prompts, and exhibits gender-based asymmetries. We also show that responses rated "polite" by standard metrics often violate taarof norms, indicating the limitations of Western politeness frameworks. Through supervised fine-tuning and Direct Preference Optimization, we achieve 21.8% and 42.3% improvement in model alignment with cultural expectations. Our human study with 33 participants (11 native Persian, 11 heritage, and 11 non-Iranian speakers) forms baselines in varying degrees of familiarity with Persian norms. This work lays the foundation for developing diverse and culturally aware LLMs, enabling applications that better navigate complex social interactions.

Effectively leveraging diversity has been shown to improve performance for various machine learning models, including large language models (LLMs). However, determining the most effective way of using diversity remains a challenge. In this work, we compare two diversity approaches for answering binary questions using LLMs: model diversity, which relies on multiple models answering the same question, and question interpretation diversity, which relies on using the same model to answer the same question framed in different ways. For both cases, we apply majority voting as the ensemble consensus heuristic to determine the final answer. Our experiments on boolq, strategyqa, and pubmedqa show that question interpretation diversity consistently leads to better ensemble accuracy compared to model diversity. Furthermore, our analysis of GPT and LLaMa shows that model diversity typically produces results between the best and the worst ensemble members without clear improvement.