Breakthroughs, discoveries, and DIY tips sent every weekday. For the first time, a brown rat has been caught on camera actively hunting bats . The never-before-seen footage shows the rat grabbing a snack at hibernation sites in northern Germany. While it's undeniably impressive that rats can grab their supper mid-air, the new footage does not bode well for the bats. According to a study recently published in the journal, rat predation may cause enough damage to significantly threaten local bat populations .

Trump-Xi meeting: Who has the upper hand? Could Trump go for a third term? Is the US eyeing its next Latin American target? Why is Trump tearing down parts of the White House? United States President Donald Trump and his Chinese counterpart Xi Jinping have agreed to a trade truce under which the US will ease tariffs and Beijing will restart imports of US soya beans, delay the introduction of export restrictions on some of its rare earth metals and intensify efforts to curb illegal fentanyl trafficking.

Virgin Media O2 is set to become the first mobile operator to offer UK customers automatic connectivity via satellite in places without mobile signal. O2 Satellite will be an optional service due to launch in the first half of 2026. The firm has not yet revealed how much it will cost, but it will be an additional fee to pay each month. O2 has partnered with Elon Musk's satellite business Starlink to offer the service. Enabled smartphones will automatically switch to satellite coverage in parts of the UK where there is no terrestrial signal available - for example in rural areas.

Who is Rob Jetten, tipped to become youngest Dutch prime minister? Rob Jetten's achievement in dragging his socially liberal D66 party from fifth place to the top of Dutch politics in less than two years has been extraordinary. But politically, all the stars were perfectly aligned for the 38-year-old to do so. The result of Wednesday's election is too close to call, with Jetten vying with anti-Islam populist Geert Wilders for the most seats in parliament. No other political leader commanded as much screen time during the campaign as Jetten and his smile and cheerful message resonated with voters, while his rivals sometimes struggled.

Burnout is a psychological syndrome marked by emotional exhaustion, depersonalization, and reduced personal accomplishment, with a significant impact on individual well-being and organizational performance. This study proposes a machine learning approach to predict burnout risk using the HackerEarth Employee Burnout Challenge dataset. Three supervised algorithms were evaluated: nearest neighbors (KNN), random forest, and support vector machine (SVM), with model performance evaluated through 30-fold cross-validation using the determination coefficient (R2). Among the models tested, SVM achieved the highest predictive performance (R2 = 0.84) and was statistically superior to KNN and Random Forest based on paired $t$-tests. To ensure practical applicability, an interactive interface was developed using Streamlit, allowing non-technical users to input data and receive burnout risk predictions. The results highlight the potential of machine learning to support early detection of burnout and promote data-driven mental health strategies in organizational settings.

Despite their impressive performance, vision-language models (VLMs) still struggle on culturally situated inputs. To understand how VLMs process culturally grounded information, we study the presence of culture-sensitive neurons, i.e. neurons whose activations show preferential sensitivity to inputs associated with particular cultural contexts. We examine whether such neurons are important for culturally diverse visual question answering and where they are located. Using the CVQA benchmark, we identify neurons of culture selectivity and perform causal tests by deactivating the neurons flagged by different identification methods. Experiments on three VLMs across 25 cultural groups demonstrate the existence of neurons whose ablation disproportionately harms performance on questions about the corresponding cultures, while having minimal effects on others. Moreover, we propose a new margin-based selector - Contrastive Activation Selection (CAS), and show that it outperforms existing probability- and entropy-based methods in identifying culture-sensitive neurons. Finally, our layer-wise analyses reveals that such neurons tend to cluster in certain decoder layers. Overall, our findings shed new light on the internal organization of multimodal representations.

Unlike static architectures, it enables agents to evolve via an entropy-guided, memory-aware online learning mechanism, retrieving high-value prior cases from an episodic memory bank and exploring diverse historical contexts.

Ensemble methods for stream mining necessitate managing multiple models and updating them as data distributions evolve. Considering the calls for more sustainability, established methods are however not sufficiently considerate of ensemble members' computational expenses and instead overly focus on predictive capabilities. To address these challenges and enable green online learning, we propose heterogeneous online ensembles (HEROS). For every training step, HEROS chooses a subset of models from a pool of models initialized with diverse hyperparameter choices under resource constraints to train. We introduce a Markov decision process to theoretically capture the trade-offs between predictive performance and sustainability constraints. Based on this framework, we present different policies for choosing which models to train on incoming data. Most notably, we propose the novel $ζ$-policy, which focuses on training near-optimal models at reduced costs. Using a stochastic model, we theoretically prove that our $ζ$-policy achieves near optimal performance while using fewer resources compared to the best performing policy. In our experiments across 11 benchmark datasets, we find empiric evidence that our $ζ$-policy is a strong contribution to the state-of-the-art, demonstrating highly accurate performance, in some cases even outperforming competitors, and simultaneously being much more resource-friendly.

Prompt engineering is crucial for unlocking the potential of Large Language Models (LLMs). Still, since manual prompt design is often complex, non-intuitive, and time-consuming, automatic prompt optimization has emerged as a research area. However, a significant challenge in prompt optimization is managing the inherent trade-off between task performance, such as accuracy, and context size. Most existing automated methods focus on a single objective, typically performance, thereby failing to explore the critical spectrum of efficiency and effectiveness. This paper introduces the MOPrompt, a novel Multi-objective Evolutionary Optimization (EMO) framework designed to optimize prompts for both accuracy and context size (measured in tokens) simultaneously. Our framework maps the Pareto front of prompt solutions, presenting practitioners with a set of trade-offs between context size and performance, a crucial tool for deploying Large Language Models (LLMs) in real-world applications. We evaluate MOPrompt on a sentiment analysis task in Portuguese, using Gemma-2B and Sabiazinho-3 as evaluation models. Our findings show that MOPrompt substantially outperforms the baseline framework. For the Sabiazinho model, MOPrompt identifies a prompt that achieves the same peak accuracy (0.97) as the best baseline solution, but with a 31% reduction in token length.

Is the US eyeing its next Latin American target? Why is Trump tearing down parts of the White House? Trump-Xi meeting: What's at stake and who has the upper hand? United States President Donald Trump expects "a lot of problems" will be solved between Washington and Beijing when he meets China's President Xi Jinping in South Korea for a high-stakes meeting on Thursday, amid growing trade tensions between the two. Relations between the two world powers have been strained in recent years, with Washington and Beijing imposing tit-for-tat trade tariffs topping 100 percent against each other this year, the US restricting its exports of semiconductors vital for artificial intelligence (AI) development and Beijing restricting exports of critical rare-earth metals which are vital for the defence industry and also the development of AI, among other issues. On the sidelines of the Asia-Pacific Economic Cooperation (APEC) summit in Gyeongju, South Korea, on Wednesday, Trump said an expected trade deal between China and the US would be good for both countries and "something very exciting for everybody".