In the face of sea ice loss, some of the bears on the Norwegian archipelago are gaining weight. Three polar bear cubs gather around their tranquilized mother. She had a litter of three cubs (an unusual brood size) and the smallest cub only weighed 11 pounds (five kilograms). Breakthroughs, discoveries, and DIY tips sent six days a week. The Arctic's polar bears () are often the poster species for the perils of climate change .

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' Russian attacks on Ukraine's southern Zaporizhia killed a 44-year-old man and wounded several others, Governor Ivan Fedorov said on Monday, as the death toll from other assaults on Sunday continued to rise. Ukrainian officials said the attacks on Sunday killed two people in the eastern Donetsk region and a 69-year-old man in the northern Sumy region.

Neural operators are becoming the default tools to learn solutions to governing partial differential equations (PDEs) in weather and ocean forecasting applications. Despite early promising achievements, significant challenges remain, including long-term prediction stability and adherence to physical laws, particularly for high-frequency processes. In this paper, we take a step toward addressing these challenges in high-resolution ocean prediction by incorporating temporal Fourier modes, demonstrating how this modification enhances physical fidelity. This study compares the standard Fourier Neural Operator (FNO) with its variant, FNOtD, which has been modified to internalize the dispersion relation while learning the solution operator for ocean PDEs. The results demonstrate that entangling space and time in the training of integral kernels enables the model to capture multiscale wave propagation and effectively learn ocean dynamics. FNOtD substantially improves long-term prediction stability and consistency with underlying physical dynamics in challenging high-frequency settings compared to the standard FNO. It also provides competitive predictive skill relative to a state-of-the-art numerical ocean model, while requiring significantly lower computational cost.

Is Chicago the violent crime capital of the US? How did India-US relations decline so fast? A Ukrainian drone attack killed two women in the village of Golovchino in Russia's Belgorod region, Russia's state TASS news agency reports. A man who was seriously injured in a Ukrainian drone attack in Russia's Belgorod region in April has died in hospital, TASS reports. TASS also reported that Russian forces shot down 82 Ukrainian drones in a 24-hour period.

Russian military exercises near NATO borders follow the recent incursion of Russian drones into the airspace of Poland and Romania, further stoking tensions with the West. On Sunday, Russia released images of its launch of a 3M22 Zircon hypersonic missile from a frigate in the Barents Sea, in the Arctic Ocean, near NATO borders. The launch comes against a backdrop of rising tensions with the West, just days after several Russian drones violated the airspace of North Atlantic Treaty Organization member countries Poland and Romania. The Zircon test is part of the Zapad 2025 joint maneuvers with Belarus, a week of military exercises aimed at assessing defensive and coordination capabilities between the two allied countries. It also serves to show that Russia's military force has not lost its strength, despite heavy losses more than three years after the start of the invasion of Ukraine .

How is Russia replenishing its military? What is a'coalition of the willing'? How China forgot promises and'debts' to Ukraine How are Europe, the US pulling apart on Ukraine? Russia and Ukraine have been engaging in major aerial battles, targeting energy and transportation infrastructure, as Moscow presses its fierce ground assault in the Ukrainian east in the war's fourth year and tests a type of hypersonic weapon. Russia's Ministry of Defence announced on Sunday that its air defences shot down 361 drones, four guided aerial bombs, and rockets from a US-made high mobility artillery rocket system (HIMARS) overnight.

How is Russia replenishing its military? What is a'coalition of the willing'? How China forgot promises and'debts' to Ukraine How are Europe, the US pulling apart on Ukraine? NATO fighter jets headed to eastern Europe under new'Eastern Sentry' Russian attacks on Ukraine killed at least three people in the Donetsk region and another in Kharkiv, the Kyiv Independent reported on Saturday, citing local officials. A drone breached Romanian airspace during a Russian attack on Ukrainian infrastructure, prompting Romania to scramble fighter jets, the country's defence minister, Ionut Mosteanu, said.

Uncertainty estimation is essential for enhancing the reliability of Large Language Models (LLMs), particularly in high-stakes applications. Existing methods often overlook semantic dependencies, relying on token-level probability measures that fail to capture structural relationships within the generated text. We propose GENUINE: Graph ENhanced mUlti-level uncertaINty Estimation for Large Language Models, a structure-aware framework that leverages dependency parse trees and hierarchical graph pooling to refine uncertainty quantification. By incorporating supervised learning, GENUINE effectively models semantic and structural relationships, improving confidence assessments. Extensive experiments across NLP tasks show that GENUINE achieves up to 29% higher AUROC than semantic entropy-based approaches and reduces calibration errors by over 15%, demonstrating the effectiveness of graph-based uncertainty modeling. The code is available at https://github.com/ODYSSEYWT/GUQ.

Diplomacy is a complex multiplayer game that requires both cooperation and competition, posing significant challenges for AI systems. Traditional methods rely on equilibrium search to generate extensive game data for training, which demands substantial computational resources. Large Language Models (LLMs) offer a promising alternative, leveraging pre-trained knowledge to achieve strong performance with relatively small-scale fine-tuning. However, applying LLMs to Diplomacy remains challenging due to the exponential growth of possible action combinations and the intricate strategic interactions among players. To address this challenge, we propose DipLLM, a fine-tuned LLM-based agent that learns equilibrium policies for Diplomacy. DipLLM employs an autoregressive factorization framework to simplify the complex task of multi-unit action assignment into a sequence of unit-level decisions. By defining an equilibrium policy within this framework as the learning objective, we fine-tune the model using only 1.5% of the data required by the state-of-the-art Cicero model, surpassing its performance. Our results demonstrate the potential of fine-tuned LLMs for tackling complex strategic decision-making in multiplayer games.

In recent years, diffusion models have emerged as powerful tools for generating ensemble members in meteorology. In this work, we demonstrate that a Denoising Diffusion Implicit Model (DDIM) can effectively control ensemble variance by varying the number of diffusion steps. Introducing a theoretical framework, we relate diffusion steps to the variance expressed by the reverse diffusion process. Focusing on reanalysis downscaling, we propose an ensemble diffusion model for the full ERA5-to-CERRA domain, generating variance-calibrated ensemble members for wind speed at full spatial and temporal resolution. Our method aligns global mean variance with a reference ensemble dataset and ensures spatial variance is distributed in accordance with observed meteorological variability. Additionally, we address the lack of ensemble information in the CARRA dataset, showcasing the utility of our approach for efficient, high-resolution ensemble generation.