UML is known to be challenging: Even learning linear mixtures requires highly nontrivial analytical tools, e.g., independent component analysis or nonnegative matrix factorization.

Two popular approaches for model compression are: pruning and quantization.

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? In the past week, Russia has ramped up a diplomacy of intimidation in the Baltic Sea using planes, drones and words aimed at Ukraine's European allies. After threats towards Finland earlier in September, Russia violated Estonian airspace on Friday and German airspace on Sunday, days after it had flown two dozen drones into Poland.

The World's Oceans Are Hurtling Toward Breaking Point Climate change, pollution, and fishing are pushing oceans closer to their limits at an unprecedented rate. The pressure of that human impact is expected to double by 2050, according to a new study. For life on Earth, the oceans are essential. Not only do they supply us with food and resources, they also play a big role in maintaining a stable climate: between one-quarter to one-third of all CO emitted by humans, which would otherwise stay in the atmosphere to further intensify climate change, is captured and stored by the sea . But the oceans are in trouble.

A South Korean court has handed a 15-year prison sentence to the boss of a lithium battery maker after a deadly fire last year. In June 2024, a blaze at a plant in Hwaseong city, about 45km (28 miles) south of the capital Seoul, killed 23 people, including 18 foreign workers, and injured eight others. The court found the blaze was an anticipated disaster and that Aricell chief executive Park Soon-kwan and other executives had caused the deaths of the workers. It is the longest jail term imposed under the country's industrial safety law, which punishes owners or bosses of firms with at least a year in prison, or fines of up to 1 billion won ($717,000; £530,000), for fatal incidents. Prosecutors had sought a 20-year term, arguing that company executives had made changes to the plant that meant it was difficult for workers to escape the fire.

Residential electricity demand at granular scales is driven by what people do and for how long. Accurately forecasting this demand for applications like microgrid management and demand response therefore requires generative models that can produce realistic daily activity sequences, capturing both the timing and duration of human behavior. This paper develops a generative model of human activity sequences using nationally representative time-use diaries at a 10-minute resolution. We use this model to quantify which demographic factors are most critical for improving predictive performance. We propose a hierarchical semi-Markov framework that addresses two key modeling challenges. First, a time-inhomogeneous Markov \emph{router} learns the patterns of ``which activity comes next." Second, a semi-Markov \emph{hazard} component explicitly models activity durations, capturing ``how long" activities realistically last. To ensure statistical stability when data are sparse, the model pools information across related demographic groups and time blocks. The entire framework is trained and evaluated using survey design weights to ensure our findings are representative of the U.S. population. On a held-out test set, we demonstrate that explicitly modeling durations with the hazard component provides a substantial and statistically significant improvement over purely Markovian models. Furthermore, our analysis reveals a clear hierarchy of demographic factors: Sex, Day-Type, and Household Size provide the largest predictive gains, while Region and Season, though important for energy calculations, contribute little to predicting the activity sequence itself. The result is an interpretable and robust generator of synthetic activity traces, providing a high-fidelity foundation for downstream energy systems modeling.

We present a wall-to - wall map of dominant tree species in Swedish forests accompanied by pixel - level uncertainty estimates. The tree species classification is based on spatiotemporal metrics derived from Sentinel-1 and Sentinel - 2 satellite data, combined with field observations from the Swedish National Forest Inventory and auxiliary data on geomorphometry and canopy height. We apply an extreme gradient boosting model with Bayesian optimization to relate field observations to satellite-derived features and generate the final species map. Classification uncertainty is quantified using Shannon's entropy of the predicted class probabilities, which provide a spatially explicit measure of model confidence. The final model achieved an overall accuracy of 85% (F1 score = 0.82, Matthews correlation coefficient = 0.81), and mapped species distributions showed strong agreement with official forest statistics (r = 0.96). V ariable importance analysis revealed that the most influential predictors were optical bands from Sentinel - 2, particularly those acquired in spring and summer. This study provides scalable, interpretable, and policy-relevant method for tree species mapping with integrated uncertainty that are well-suited to meet emerging legislative and environmental goals.

Large Language Models (LLMs) have excelled in question-answering (QA) tasks within single domains. However, their reasoning and coordination capabilities in complex, multi-stage scenarios remain underexplored. Existing benchmarks typically focus on isolated tasks or narrow domains, overlooking models' abilities for multi-stage collaboration and optimization without explicit external guidance. To bridge this gap, we propose \textbf{MSCoRe}, a novel benchmark comprising 126696 domain-specific QA instances spanning scenarios in automotive, pharmaceutical, electronics, and energy sectors. The dataset is created using a structured three-phase pipeline: dynamic sampling, iterative question-answer generation, and a multi-level quality assessment to ensure data quality. Tasks are further categorized into three difficulty levels according to stage coverage and complexity. With MSCoRe, we have conducted a comprehensive evaluation of various state-of-the-art LLM agents. The commercial models performed best across all tasks and scenarios, but a notable gap in ROUGE scores remains between simple and complex tasks. We also tested the models' robustness and found that their performance is negatively affected by noisy data. MSCoRe provides a valuable new resource for the community to evaluate and improve multi-stage reasoning in LLM agents. The code and data are available at https://github.com/D3E0-source/MSCoRE.