Probabilistic Regression Trees (PRTrees) generalize traditional decision trees by incorporating probability functions that associate each data point with different regions of the tree, providing smooth decisions and continuous responses. This paper introduces an adaptation of PRTrees capable of handling missing values in covariates through three distinct approaches: (i) a uniform probability method, (ii) a partial observation approach, and (iii) a dimension-reduced smoothing technique. The proposed methods preserve the interpretability properties of PRTrees while extending their applicability to incomplete datasets. Simulation studies under MCAR conditions demonstrate the relative performance of each approach, including comparisons with traditional regression trees on smooth function estimation tasks. The proposed methods, together with the original version, have been developed in R with highly optimized routines and are distributed in the PRTree package, publicly available on CRAN. In this paper we also present and discuss the main functionalities of the PRTree package, providing researchers and practitioners with new tools for incomplete data analysis.

Uncertainty-aware wind power prediction is essential for grid integration and reliable wind farm operation. We apply neural diffusion processes (NDPs)--a recent class of models that learn distributions over functions--and extend them to a multi-task NDP (MT-NDP) framework for wind power prediction. We provide the first empirical evaluation of NDPs in real supervisory control and data acquisition (SCADA) data. We introduce a task encoder within MT-NDPs to capture cross-turbine correlations and enable few-shot adaptation to unseen turbines. The proposed MT-NDP framework outperforms single-task NDPs and GPs in terms of point accuracy and calibration, particularly for wind turbines whose behaviour deviates from the fleet average. In general, NDP-based models deliver calibrated and scalable predictions suitable for operational deployment, offering sharper, yet trustworthy, predictive intervals that can support dispatch and maintenance decisions in modern wind farms. Introduction Wind energy has become a cornerstone of the global transition to clean power. As wind power capacity expands worldwide, ensuring reliability and minimising downtime are critical to both energy security and the financial viability of wind farms. Beyond energy balancing, uncertainty-aware forecasting also reduces operational uncertainty for wind farm operators, enabling more efficient maintenance scheduling and reducing costly unplanned downtime. This is especially important given that operation and maintenance costs represent a significant share of total expenditure, with unexpected failures making up the largest component [1, 2]. Supervisory control and data acquisition (SCADA) systems provide a low-cost and widely available source of wind turbine data. They capture environmental and operational variables with high frequency, making them invaluable for prediction applications. However, their use is complicated by measurement noise, turbine downtime, and limited public availability [3, 4].

Accountability regimes typically encourage record-keeping to enable the transparency that supports oversight, investigation, contestation, and redress. However, implementing such record-keeping can introduce considerations, risks, and consequences, which so far remain under-explored. This paper examines how record-keeping practices bring algorithmic systems within accountability regimes, providing a basis to observe and understand their effects. For this, we introduce, describe, and elaborate 'accountability capture' -- the re-configuration of socio-technical processes and the associated downstream effects relating to record-keeping for algorithmic accountability. Surveying 100 practitioners, we evidence and characterise record-keeping issues in practice, identifying their alignment with accountability capture. We further document widespread record-keeping practices, tensions between internal and external accountability requirements, and evidence of employee resistance to practices imposed through accountability capture. We discuss these and other effects for surveillance, privacy, and data protection, highlighting considerations for algorithmic accountability communities. In all, we show that implementing record-keeping to support transparency in algorithmic accountability regimes can itself bring wider implications -- an issue requiring greater attention from practitioners, researchers, and policymakers alike.

While deep learning methods for detecting informal settlements have already been developed, they have not yet fully utilized the potential offered by recent pretrained neural networks. We compare two types of pretrained neural networks for detecting the favelas of Rio de Janeiro: 1. Generic networks pretrained on large diverse datasets of unspecific images, 2. A specialized network pretrained on satellite imagery . While the latter is more specific to the target task, the former has been pretrained on significantly more images. Hence, this research investigates whether task specificity or data volume yields superior performance in urban informal settlement detection.

Vision-language models trained on large-scale multimodal datasets show strong demographic biases, but the role of training data in producing these biases remains unclear. A major barrier has been the lack of demographic annotations in web-scale datasets such as LAION-400M. We address this gap by creating person-centric annotations for the full dataset, including over 276 million bounding boxes, perceived gender and race/ethnicity labels, and automatically generated captions. These annotations are produced through validated automatic labeling pipelines combining object detection, multimodal captioning, and finetuned classifiers. Using them, we uncover demographic imbalances and harmful associations, such as the disproportionate linking of men and individuals perceived as Black or Middle Eastern with crime-related and negative content. We also show that 60-70% of gender bias in CLIP and Stable Diffusion can be linearly explained by direct co-occurrences in the data. Our resources establish the first large-scale empirical link between dataset composition and downstream model bias.

AMD's shares surge on deal to supply AI chips to OpenAI United States chipmaker AMD will supply artificial intelligence chips to OpenAI in a multi-year deal that would bring in tens of billions of dollars in annual revenue and give the ChatGPT creator the option to buy up to roughly 10 percent of the company. Shares of the chipmaker surged more than 34 percent on Monday when the deal was announced, putting them on track for their biggest one-day gain in more than nine years and adding roughly $80bn to the company's market value. "We view this deal as certainly transformative, not just for AMD, but for the dynamics of the industry," AMD executive vice president Forrest Norrod told the Reuters news agency. The agreement closely ties the startup at the centre of the AI boom to AMD, one of the strongest rivals of Nvidia, which recently agreed to make substantial investments in OpenAI. Analysts said it was a significant vote of confidence in AMD's AI chips and software but is unlikely to dent Nvidia's dominance, as the market leader continues to sell every AI chip it can make.

New Supreme Court term will reshape Trump's powers The US Supreme Court begins its new term on Monday with a docket already full of potentially significant cases that could define the scope of Donald Trump's presidential authority - and the prospect of more to come. In the eight months that Trump has been back in the White House, he has tested the limits of executive power, unilaterally implementing new policies, slashing federal budgets and workforce, and attempting to bring previously independent agencies and institutions more directly under his control. The latest brewing legal battle comes from the president's attempts to take control of state National Guard units and deploy them in cities where he claims there is public unrest and rampant crime - over the objection of local and state officials. In Oregon, a federal judge has issued orders blocking Trump's deployment of troops to Portland. An appeals court is set to review the move in the coming days.

The tiny shark and a ghost-like crab are two of the latest species uncovered in a yearslong expedition. Breakthroughs, discoveries, and DIY tips sent every weekday. Oceanographers scouring the waters off of Western Australia have discovered two new deep-sea oddities . On October 6, Australia's Commonwealth Scientific and Industrial Research Organization (CSIRO) showcased these new species originally collected in 2022: a bioluminescent lanternshark and a tiny, semi-translucent porcelain crab . The team revealed two of its initial finds--the painted hornshark and the ridged-egg catshark --in 2023.

British microcomputers were among more than 100,000 foreign-made parts contained in Russian missiles and drones used in Sunday's deadly strikes on Ukraine, Volodymyr Zelensky has said. The Ukrainian president called for further effective sanctions after saying parts originating in allied countries including Germany, Japan and the US have been identified in Russian weapons. The Department for Business and Trade (DBT) said it had recently undertaken efforts to crack down on UK firms whose products have continued to make their way into Russia's military supply chain. We take reports of goods from UK companies being found in Russian weaponry incredibly seriously, a government spokesperson said. The spokesperson said the government had banned the export of thousands of goods to Russia including every battlefield item Ukraine has brought to our attention, adding that they have imposed the most the most severe package of sanctions. What are the sanctions on Russia and are they working?

Can Ukraine restore its pre-war borders? Why are Tomahawk missiles for Ukraine a'red line' for Russia? Is Russia testing NATO with aerial incursions in Europe? Ukraine's Zelenskyy says Western parts found in Russian drones, missiles Ukrainian President Volodymyr Zelenskyy has alleged that drones and missiles fired by Russia against his country are filled with parts sourced from Western companies. In a social media post on Monday, Zelenskyy said the hundreds of weapons used in Russian attacks over the previous two nights contained tens of thousands of components produced by firms in the United States, United Kingdom, Germany, Switzerland, Japan, South Korea, the Netherlands, Taiwan and China.