What is autism and what are Trump's unproven claims about a Tylenol link? US President Donald Trump has claimed there is a link between the use of painkiller Tylenol by pregnant women and an increased risk of autism in some children. Going against current scientific advice and medical opinion, he said the drug, known as paracetamol in many countries, is no good and women should fight like hell to only take it in extreme cases, such as for high fevers. Medical bodies say the drug is safe and that it remains the best treatment for pain and fever during pregnancy. What is autism and how is it diagnosed?

Why is Israel still in southern Lebanon? A war to shape Lebanon's future Lebanon is pushing to get more support from the United States after another deadly Israeli drone attack on southern Lebanon, which this time killed five people, including three children, the latest in a series of near-daily violations by Israel of the US-brokered November 2024 ceasefire. President Joseph Aoun and other officials met with a delegation led by US Secretary of State Marco Rubio, the Lebanese presidency said in a statement on Tuesday. The Lebanese president said he wants Israel to stop occupying parts of his country, is looking to gear its army with "equipment and supplies" from the US, and needs Washington's support to hold a conference dedicated to reconstruction in Lebanon. Amid ongoing efforts to disarm Hezbollah, Aoun emphasised that the Lebanese army's mandate includes "all Lebanese regions" as the country tries to seize an opportunity "to achieve just, comprehensive, and lasting peace in the Middle East region". He is also scheduled to address the United Nations General Assembly on Tuesday, where he is expected to denounce Israeli attacks across the region, including in Gaza and Lebanon.

A deadly drone attack in an impoverished area of Haiti's capital, Port-au-Prince, which killed at least 11 people, including eight children, is being blamed on the government, as the country's use of the UAVs in its war on gangs comes under increasing scrutiny. The incident happened on Saturday night in Cite Soleil, one of Port-au-Prince's most dangerous neighbourhoods, in the city's west along the coast, as Albert Steevenson, known as Djouma or "King Jouma", who is a suspected gang leader, was celebrating his birthday. One of the group's leaders and most notorious figures, Jimmy Cherizier, known as Barbecue, promised to avenge the attack. Claudia Bobrun, 30, whose daughter was killed in the attack, showed The Associated Press news agency a video of the eight-year-old in a pool of blood, as she burst into tears. Merika, another four-year-old victim of the attack, was playing with other children at 8pm in the Simon Pele neighbourhood, in Cite Soleil, where the suspected kamikaze drone exploded.

Porsche's stock tumbled by more than 7% on Monday after warning last week that delays in its electric vehicle (EV) rollout will dent the carmaker's 2025 earnings. Caught between electrification and its iconic petrol-powered sports cars, the German firm said it will slow its push for EVs as demand weakens. Shares of its parent Volkswagen also fell by more than 7% on the same day after saying it will spend billions to overhaul Porsche's line-up of vehicles. The companies' struggles reflect the challenges for European manufacturers, who are faced with intense competition from Chinese rivals and a slowing economy that's dampening demand for luxury cars. Porsche said in a statement on Friday that it has reduced its projected profit margin from up to 7% to 2% or less.

Language models obtain extensive capabilities through pre-training. However, the pre-training process remains a black box. In this work, we track linear interpretable feature evolution across pre-training snapshots using a sparse dictionary learning method called crosscoders. We find that most features begin to form around a specific point, while more complex patterns emerge in later training stages. Feature attribution analyses reveal causal connections between feature evolution and downstream performance. Our feature-level observations are highly consistent with previous findings on Transformer's two-stage learning process, which we term a statistical learning phase and a feature learning phase. Our work opens up the possibility to track fine-grained representation progress during language model learning dynamics.

Translating cultural content poses challenges for machine translation systems due to the differences in conceptualizations between cultures, where language alone may fail to convey sufficient context to capture region-specific meanings. In this work, we investigate whether images can act as cultural context in multimodal translation. We introduce CaMMT, a human-curated benchmark of over 5,800 triples of images along with parallel captions in English and regional languages. Using this dataset, we evaluate five Vision Language Models (VLMs) in text-only and text+image settings. Through automatic and human evaluations, we find that visual context generally improves translation quality, especially in handling Culturally-Specific Items (CSIs), disambiguation, and correct gender marking. By releasing CaMMT, our objective is to support broader efforts to build and evaluate multimodal translation systems that are better aligned with cultural nuance and regional variations.

The development of Natural Language Processing (NLP) tools for low-resource languages is critically hindered by the scarcity of annotated datasets. This paper addresses this fundamental challenge by introducing HausaMovieReview, a novel benchmark dataset comprising 5,000 YouTube comments in Hausa and code-switched English. The dataset was meticulously annotated by three independent annotators, demonstrating a robust agreement with a Fleiss' Kappa score of 0.85 between annotators. We used this dataset to conduct a comparative analysis of classical models (Logistic Regression, Decision Tree, K-Nearest Neighbors) and fine-tuned transformer models (BERT and RoBERTa). Our results reveal a key finding: the Decision Tree classifier, with an accuracy and F1-score 89.72% and 89.60% respectively, significantly outperformed the deep learning models. Our findings also provide a robust baseline, demonstrating that effective feature engineering can enable classical models to achieve state-of-the-art performance in low-resource contexts, thereby laying a solid foundation for future research. Keywords: Hausa, Kannywood, Low-Resource Languages, NLP, Sentiment Analysis

Despite growing research on bias in large language models (LLMs), most work has focused on gender and race, with little attention to religious identity. This paper explores how religion is internally represented in LLMs and how it intersects with concepts of violence and geography. Using mechanistic interpretability and Sparse Autoencoders (SAEs) via the Neuronpedia API, we analyze latent feature activations across five models. We measure overlap between religion- and violence-related prompts and probe semantic patterns in activation contexts. While all five religions show comparable internal cohesion, Islam is more frequently linked to features associated with violent language. In contrast, geographic associations largely reflect real-world religious demographics, revealing how models embed both factual distributions and cultural stereotypes. These findings highlight the value of structural analysis in auditing not just outputs but also internal representations that shape model behavior.

Automating radiology report generation poses a dual challenge: building clinically reliable systems and designing rigorous evaluation protocols. We introduce a multi-agent reinforcement learning framework that serves as both a benchmark and evaluation environment for multimodal clinical reasoning in the radiology ecosystem. The proposed framework integrates large language models (LLMs) and large vision models (LVMs) within a modular architecture composed of ten specialized agents responsible for image analysis, feature extraction, report generation, review, and evaluation. This design enables fine-grained assessment at both the agent level (e.g., detection and segmentation accuracy) and the consensus level (e.g., report quality and clinical relevance). We demonstrate an implementation using chatGPT-4o on public radiology datasets, where LLMs act as evaluators alongside medical radiologist feedback. By aligning evaluation protocols with the LLM development lifecycle, including pretraining, finetuning, alignment, and deployment, the proposed benchmark establishes a path toward trustworthy deviance-based radiology report generation.

Low-cost mobile sensors can be used to collect PM$_{2.5}$ concentration data throughout an entire city. However, identifying air pollution hotspots from the data is challenging due to the uneven spatial sampling, temporal variations in the background air quality, and the dynamism of urban air pollution sources. This study proposes a method to identify urban PM$_{2.5}$ hotspots that addresses these challenges, involving four steps: (1) equip citizen scientists with mobile PM$_{2.5}$ sensors while they travel; (2) normalise the raw data to remove the influence of background ambient pollution levels; (3) fit a Gaussian process regression model to the normalised data and (4) calculate a grid of spatially explicit 'hotspot scores' using the probabilistic framework of Gaussian processes, which conveniently summarise the relative pollution levels throughout the city. We apply our method to create the first ever map of PM$_{2.5}$ pollution in Kigali, Rwanda, at a 200m resolution. Our results suggest that the level of ambient PM$_{2.5}$ pollution in Kigali is dangerously high, and we identify the hotspots in Kigali where pollution consistently exceeds the city-wide average. We also evaluate our method using simulated mobile sensing data for Beijing, China, where we find that the hotspot scores are probabilistically well calibrated and accurately reflect the 'ground truth' spatial profile of PM$_{2.5}$ pollution. Thanks to the use of open-source software, our method can be re-applied in cities throughout the world with a handful of low-cost sensors. The method can help fill the gap in urban air quality information and empower public health officials.