Beneath gilded portraits and suits of armour in Windsor Castle, 160 guests wined and dined at a lavish banquet to fete US President Donald Trump's unprecedented second state visit to the UK on Wednesday evening. Along with the impeccable table settings, three-course meal and custom cocktail, who was there and, just as importantly, who was seated next to who is carefully planned, since the event is as much about diplomacy as it is about fine dining. This year's guest list was conspicuously missing screen stars or celebrity faces, with not even royal perennials like Sir David Beckham or Sir Elton John attending. Instead, the list was mostly royals, tech and finance executives, and politicos from both sides of the Atlantic. From Trump's seat of honour at the centre of the table, next to his host King Charles III, those up and down the table ranged from lesser-known but influential White House players to professional golfers.

The number of planning approvals for new homes in England is unacceptable, the new housing secretary has said, after official data showed permission for building homes fell to a record low during Labour's first year in office. Fewer than 29,000 projects were granted permission by councils in the year ending June 2025 - striking a blow to the government's promise to deliver 1.5 million homes by the next election. Steve Reed, who has taken over from Angela Rayner as housing secretary, said fixing the planning system won't happen overnight. Conservative shadow housing secretary Sir James Cleverly said that Labour had promised to'build, build, build' but their flagship planning reforms clearly aren't working. You can see the figures for your local area in BBC Verify's housing tracker.

In 1907, Duncan MacDougall put dying patients on a scale. William Blake's 1805 illustration for Scottish poet Robert Blair's poem The Grave imagines the soul rising from the body at death. Breakthroughs, discoveries, and DIY tips sent every weekday. It's a little complicated to weigh a dying person on a hospital bed, but that didn't matter to Duncan MacDougall. In the early 20th century, MacDougall's unique, purpose-built scale was ready to receive test subjects.

A former Reform UK MP is under investigation over alleged racial abuse against a Sky News journalist. James McMurdock, who represents South Basildon and East Thurrock in Essex, is accused of starting a chain of posts on X that spelled out a racial slur on 4 August. He appeared to deny making the post, saying his accuser, Huntingdon MP Ben Obese-Jecty, had nothing better to do. The Parliamentary standards commissioner is due to rule if he breached the House of Commons code of conduct. It was investigating a potential violation of rule 11, defined as actions causing significant damage to the reputation to the House of Commons or its MPs.

Which way is up? the way people hold the control for video games varies. Which way is up? the way people hold the control for video games varies. Why do some gamers invert their controls? Scientists now have answers, but they're not what you think F ive years ago, on the verge of the first Covid lockdown, I wrote an article asking what seemed to be an extremely niche question: why do some people invert their controls when playing 3D games? A majority of players push down on the controller to make their onscreen character look down, and up to make them look up.

Modern multi-modal and multi-site data frequently suffer from blockwise missingness, where subsets of features are missing for groups of individuals, creating complex patterns that challenge standard inference methods. Existing approaches have critical limitations: complete-case analysis discards informative data and is potentially biased; doubly robust estimators for non-monotone missingness-where the missingness patterns are not nested subsets of one another-can be theoretically efficient but lack closed-form solutions and often fail to scale; and blackbox imputation can leverage partially observed data to improve efficiency but provides no inferential guarantees when misspecified. To address the limitations of these existing methods, we propose imputation-powered inference (IPI), a model-lean framework that combines the flexibility of blackbox imputation with bias correction using fully observed data, drawing on ideas from prediction-powered inference and semiparametric inference. IPI enables valid and efficient M-estimation under missing completely at random (MCAR) blockwise missingness and improves subpopulation inference under a weaker assumption we formalize as first-moment MCAR, for which we also provide practical diagnostics. Simulation studies and a clinical application demonstrate that IPI may substantially improve subpopulation efficiency relative to complete-case analysis, while maintaining statistical validity in settings where both doubly robust estimators and naive imputation fail to achieve nominal coverage.

Recent advancements in large language models (LLMs) have transformed natural language understanding and generation, leading to extensive benchmarking across diverse tasks. However, cryptanalysis - a critical area for data security and its connection to LLMs' generalization abilities - remains underexplored in LLM evaluations. To address this gap, we evaluate the cryptanalytic potential of state-of-the-art LLMs on ciphertexts produced by a range of cryptographic algorithms. We introduce a benchmark dataset of diverse plaintexts, spanning multiple domains, lengths, writing styles, and topics, paired with their encrypted versions. Using zero-shot and few-shot settings along with chain-of-thought prompting, we assess LLMs' decryption success rate and discuss their comprehension abilities. Our findings reveal key insights into LLMs' strengths and limitations in side-channel scenarios and raise concerns about their susceptibility to under-generalization-related attacks. This research highlights the dual-use nature of LLMs in security contexts and contributes to the ongoing discussion on AI safety and security.

Transitional accounts of evolution emphasise a few changes that shape what is evolvable, with dramatic consequences for derived lineages. More recently it has been proposed that cognition might also have evolved via a series of major transitions that manipulate the structure of biological neural networks, fundamentally changing the flow of information. We used idealised models of information flow, artificial neural networks (ANNs), to evaluate whether changes in information flow in a network can yield a transitional change in cognitive performance. We compared networks with feed-forward, recurrent and laminated topologies, and tested their performance learning artificial grammars that differed in complexity, controlling for network size and resources. We documented a qualitative expansion in the types of input that recurrent networks can process compared to feed-forward networks, and a related qualitative increase in performance for learning the most complex grammars. We also noted how the difficulty in training recurrent networks poses a form of transition barrier and contingent irreversibility -- other key features of evolutionary transitions. Not all changes in network topology confer a performance advantage in this task set. Laminated networks did not outperform non-laminated networks in grammar learning. Overall, our findings show how some changes in information flow can yield transitions in cognitive performance.

--It is shown that the port behavior of a resistor-diode network corresponds to the solution of a ReLU monotone operator equilibrium network (a neural network in the limit of infinite depth), giving a parsimonious construction of a neural network in analog hardware. We furthermore show that the gradient of such a circuit can be computed directly in hardware, using a procedure we call hardware linearization . This allows the network to be trained in hardware, which we demonstrate with a device-level circuit simulation. We extend the results to cascades of resistor-diode networks, which can be used to implement feedforward and other asymmetric networks. We finally show that different nonlinear elements give rise to different activation functions, and introduce the novel diode ReLU which is induced by a non-ideal diode model. The idea of building a neural network in analog hardware is classical [1]-[5]. Since the discovery of semiconductor devices with memristive properties [6], and in light of the growing energy intensiveness of machine learning systems, there has been a resurgence of interest in building devices which incorporate analog memristive components and are specially suited for deep learning applications [7], [8]. One of the primary advantages of such devices is that memristors, and similar elements such as phase change memory, act as both memory and computational units. This allows the transport delay between memory and computation to be circumvented. A particularly successful design is to arrange a number of memristors in a crossbar array, which can be used to perform matrix-vector calculation in a single operation [9]-[12].

The increasing use of generative AI in scientific writing raises urgent questions about attribution and intellectual credit. When a researcher employs ChatGPT to draft a manuscript, the resulting text may echo ideas from sources the author has never encountered. If an AI system reproduces insights from, for example, an obscure 1975 paper without citation, does this constitute plagiarism? We argue that such cases exemplify the 'provenance problem': a systematic breakdown in the chain of scholarly credit. Unlike conventional plagiarism, this phenomenon does not involve intent to deceive (researchers may disclose AI use and act in good faith) yet still benefit from the uncredited intellectual contributions of others. This dynamic creates a novel category of attributional harm that current ethical and professional frameworks fail to address. As generative AI becomes embedded across disciplines, the risk that significant ideas will circulate without recognition threatens both the reputational economy of science and the demands of epistemic justice. This Perspective analyzes how AI challenges established norms of authorship, introduces conceptual tools for understanding the provenance problem, and proposes strategies to preserve integrity and fairness in scholarly communication.