Japan is facing a dementia crisis - can technology help? Last year, more than 18,000 older people living with dementia left their homes and wandered off in Japan. Almost 500 were later found dead. Police say such cases have doubled since 2012. Elderly people aged 65 and over now make up nearly 30% of Japan's population - the second-highest proportion in the world after Monaco, according to the World Bank.

Deep neural networks often fail when deployed in real-world contexts due to distribution shift, a critical barrier to building safe and reliable systems. An emerging approach to address this problem relies on \emph{disagreement discrepancy} -- a measure of how the disagreement between two models changes under a shifting distribution. The process of maximizing this measure has seen applications in bounding error under shifts, testing for harmful shifts, and training more robust models. However, this optimization involves the non-differentiable zero-one loss, necessitating the use of practical surrogate losses. We prove that existing surrogates for disagreement discrepancy are not Bayes consistent, revealing a fundamental flaw: maximizing these surrogates can fail to maximize the true disagreement discrepancy. To address this, we introduce new theoretical results providing both upper and lower bounds on the optimality gap for such surrogates. Guided by this theory, we propose a novel disagreement loss that, when paired with cross-entropy, yields a provably consistent surrogate for disagreement discrepancy. Empirical evaluations across diverse benchmarks demonstrate that our method provides more accurate and robust estimates of disagreement discrepancy than existing approaches, particularly under challenging adversarial conditions.

As artificial intelligence and machine learning tools become more accessible, and scientists face new obstacles to data collection (e.g., rising costs, declining survey response rates), researchers increasingly use predictions from pre-trained algorithms as substitutes for missing or unobserved data. Though appealing for financial and logistical reasons, using standard tools for inference can misrepresent the association between independent variables and the outcome of interest when the true, unobserved outcome is replaced by a predicted value. In this paper, we characterize the statistical challenges inherent to drawing inference with predicted data (IPD) and show that high predictive accuracy does not guarantee valid downstream inference. We show that all such failures reduce to statistical notions of (i) bias, when predictions systematically shift the estimand or distort relationships among variables, and (ii) variance, when uncertainty from the prediction model and the intrinsic variability of the true data are ignored. We then review recent methods for conducting IPD and discuss how this framework is deeply rooted in classical statistical theory. We then comment on some open questions and interesting avenues for future work in this area, and end with some comments on how to use predicted data in scientific studies that is both transparent and statistically principled.

Background: Text-to-image generation models are widely used across numerous domains. Among these models, Stable Diffusion (SD) - an open-source text-to-image generation model - has become the most popular, producing over 12 billion images annually. However, the widespread use of these models raises concerns regarding their social and environmental sustainability. Aims: To reduce the harm that SD models may have on society and the environment, we introduce SustainDiffusion, a search-based approach designed to enhance the social and environmental sustainability of SD models. Method: SustainDiffusion searches the optimal combination of hyperparameters and prompt structures that can reduce gender and ethnic bias in generated images while also lowering the energy consumption required for image generation. Importantly, SustainDiffusion maintains image quality comparable to that of the original SD model. Results: We conduct a comprehensive empirical evaluation of SustainDiffusion, testing it against six different baselines using 56 different prompts. Our results demonstrate that SustainDiffusion can reduce gender bias in SD3 by 68%, ethnic bias by 59%, and energy consumption (calculated as the sum of CPU and GPU energy) by 48%. Additionally, the outcomes produced by SustainDiffusion are consistent across multiple runs and can be generalised to various prompts. Conclusions: With SustainDiffusion, we demonstrate how enhancing the social and environmental sustainability of text-to-image generation models is possible without fine-tuning or changing the model's architecture.

We propose Point-PNG, a novel self-supervised learning framework that generates conditional pseudo-negatives in the latent space to learn point cloud representations that are both discriminative and transformation-sensitive. Conventional self-supervised learning methods focus on achieving invariance, discarding transformation-specific information. Recent approaches incorporate transformation sensitivity by explicitly modeling relationships between original and transformed inputs. However, they often suffer from an invariant-collapse phenomenon, where the predictor degenerates into identity mappings, resulting in latent representations with limited variation across transformations. To address this, we propose Point-PNG that explicitly penalizes invariant collapse through pseudo-negatives generation, enabling the network to capture richer transformation cues while preserving discriminative representations. To this end, we introduce a parametric network, COnditional Pseudo-Negatives Embedding (COPE), which learns localized displacements induced by transformations within the latent space. A key challenge arises when jointly training COPE with the MAE, as it tends to converge to trivial identity mappings. To overcome this, we design a loss function based on pseudo-negatives conditioned on the transformation, which penalizes such trivial invariant solutions and enforces meaningful representation learning. We validate Point-PNG on shape classification and relative pose estimation tasks, showing competitive performance on ModelNet40 and ScanObjectNN under challenging evaluation protocols, and achieving superior accuracy in relative pose estimation compared to supervised baselines.

The government says some 50,000 young people are expected to benefit from a programme to expand apprenticeships as it looks to tackle youth unemployment. The £725 million package, which was earmarked in the Budget and covers the next three years, will be used to create apprenticeships in sectors including AI, hospitality and engineering. Apprenticeships for people under the age of 25 at small and medium-sized businesses will be fully funded as part of the package, removing the 5% that they currently have to pay. The government is aiming to reverse a decline in the number of young people starting apprenticeships, which has fallen by almost 40% in the past decade. The funding also includes £140m for a pilot that the Department for Work and Pensions says will allow local mayors to connect young people with employers and apprenticeship opportunities, although it is unclear exactly how the money will be used.

Kevin Sinfield has completed seven ultramarathons in seven days to raise money and awareness for motor neurone disease (MND). The rugby league legend ran about 300km (185 miles) throughout the week, starting at Bury St Edmunds Rugby Club and ending at Leeds Rhinos home ground, Headingley Stadium. The 45-year-old completed an ultramarathon of at least 45km (27.9 miles) each day of his challenge, in bursts of 7km (4.3 miles). On Sunday he crossed the finish line in front of hundreds of supporters, who had gathered in the stadium's North and West stands to cheer him on. He said: To the MND Community and the people we've met on route, all through the last week, all through the past five years, to everybody we've met - it's an absolutely beautiful community.

An ensemble of about 10 robots - which will not be androids but ground vehicles between 40cm and 2m tall - will work with humans to learn how to be funny. An ensemble of about 10 robots - which will not be androids but ground vehicles between 40cm and 2m tall - will work with humans to learn how to be funny. A robot walks into a bar: can a Melbourne researcher get AI to do comedy? Robots can make humans laugh - mostly when they fall over - but a new research project is looking at whether robots using AI could ever be genuinely funny. If you ask ChatGPT for a funny joke, it will serve you up something that belongs in a Christmas cracker: "Why don't skeletons fight each other? Because they don't have the guts."

Chernobyl radiation shield'lost safety function' after drone strike, UN watchdog says A protective shield covering the Chernobyl nuclear reactor in Ukraine can no longer provide its main containment function following a drone strike earlier this year, according to a UN watchdog. International Atomic Energy Agency (IAEA) inspectors found that the massive structure, built over the site of the 1986 nuclear disaster, had lost its primary safety functions including the confinement capability. In February, Ukraine accused Russia of targeting the power plant - a claim the Kremlin denied. The IAEA said repairs were essential to prevent further degradation of the nuclear shelter. However environmental expert Jim Smith told the BBC: It is not something to panic about.

A drone attack on the town of Kalogi, in Sudan's South Kordofan region, is said to have hit a kindergarten and killed at least 50 people, including 33 children. The Rapid Support Forces (RSF), the paramilitary group battling the army in Sudan's civil war, was accused of Thursday's attack by a medical organisation, the Sudan Doctors' Network, and the army. There was no immediate comment from the RSF. The RSF in turn accused the army of hitting a market on Friday in a drone attack in the Darfur region, on a fuel depot at the Adre border crossing with Chad. Sudan has been ravaged by war since April 2023 when a power struggle broke out between the RSF and the army, who were formerly allies .