A fly-tipper dressed as a giant purple T. rex has been caught on camera dumping rubbish in a street. The brightly coloured rogue raptor was spotted checking for traffic before crossing a road in Southend, Essex. The prehistoric predator then looks around before slinging two black bin bags to the ground next to large black bin. Footage of the incident, first reported by Your Southend, was captured on a resident's CCTV just before 21:30 GMT on Tuesday. The city council told the BBC it had not received any reports of fly-tipping in relation to the incident.

What are freeze-dried sweets and why are they popular? When Savannah Louise West first tasted freeze-dried gummies, she was intrigued. I think the crunch is so satisfying, and I find it interesting to experience a candy I'm familiar with that has an entirely new texture, says the Toronto resident. Ms West is describing one of the main features of this spin-off candy that independent and major confectionary manufacturers have been releasing onto shelves, both online and offline, for the past three years. It's been largely a US phenomena, hence we'll use the US term candy, but for our UK readers, we're talking about sweets here.

Meta is shifting some of its investments in the metaverse to AI glasses and wearables, hoping to capitalise on the momentum in that segment, a company spokesperson has said. Over the last decade, Meta has poured billions of dollars to build the metaverse, which lets people to interact in a virtual reality. However, the tech giant has struggled to convince investors of the viability of the nascent technology. Bloomberg first reported on Thursday that Meta would cut its metaverse investment by as much as 30%. Its shares climbed more than 3.4% following the news.

The government says it is to deport 60 takeaway-delivery riders found to be working illegally in the UK. The Home Office says the group are among 171 riders arrested over seven days in November in a national enforcement blitz in villages, towns and cities across the country. It comes as Home Secretary Shabana Mahmood has been targeting people working unlawfully in the gig economy. Border Security Minister Alex Norris has also met representatives from food-delivery firms to encourage them to do more to tackle the issue - such as using facial recognition checks to prevent riders sharing their identities with people who do not have permission to take up work in the UK. Norris said November's action ought to send a clear message: if you are working illegally in this country, you will be arrested and removed.

Waterstones would stock books created using artificial intelligence, the company's boss has said, as long as they were clearly labelled, and if customers wanted them. However, James Daunt, a veteran of the bookselling industry, said he personally did not expect that to happen. There's a huge proliferation of AI generated content and most of it are not books that we should be selling, he said. But it would be up to the reader. An explosion in the use of artificial intelligence, or AI, has prompted heated debate in the publishing industry, with writers concerned about the impact on their livelihoods.

Spectral gradient methods, such as the recently popularized Muon optimizer, are a promising alternative to standard Euclidean gradient descent for training deep neural networks and transformers, but it is still unclear in which regimes they are expected to perform better. We propose a simple layerwise condition that predicts when a spectral update yields a larger decrease in the loss than a Euclidean gradient step. This condition compares, for each parameter block, the squared nuclear-to-Frobenius ratio of the gradient to the stable rank of the incoming activations. To understand when this condition may be satisfied, we first prove that post-activation matrices have low stable rank at Gaussian initialization in random feature regression, feedforward networks, and transformer blocks. In spiked random feature models we then show that, after a short burn-in, the Euclidean gradient's nuclear-to-Frobenius ratio grows with the data dimension while the stable rank of the activations remains bounded, so the predicted advantage of spectral updates scales with dimension. We validate these predictions in synthetic regression experiments and in NanoGPT-scale language model training, where we find that intermediate activations have low-stable-rank throughout training and the corresponding gradients maintain large nuclear-to-Frobenius ratios. Together, these results identify conditions for spectral gradient methods, such as Muon, to be effective in training deep networks and transformers.

Boolean matrix factorization (BMF) approximates a given binary input matrix as the product of two smaller binary factors. Unlike binary matrix factorization based on standard arithmetic, BMF employs the Boolean OR and AND operations for the matrix product, which improves interpretability and reduces the approximation error. It is also used in role mining and computer vision. In this paper, we first propose algorithms for BMF that perform alternating optimization (AO) of the factor matrices, where each subproblem is solved via integer programming (IP). We then design different approaches to further enhance AO-based algorithms by selecting an optimal subset of rank-one factors from multiple runs. To address the scalability limits of IP-based methods, we introduce new greedy and local-search heuristics. We also construct a new C++ data structure for Boolean vectors and matrices that is significantly faster than existing ones and is of independent interest, allowing our heuristics to scale to large datasets. We illustrate the performance of all our proposed methods and compare them with the state of the art on various real datasets, both with and without missing data, including applications in topic modeling and imaging.

This study investigates logistic regression, linear support vector machine, multinomial Naive Bayes, and Bernoulli Naive Bayes for classifying Libyan dialect utterances gathered from Twitter. The dataset used is the QADI corpus, which consists of 540,000 sentences across 18 Arabic dialects. Preprocessing challenges include handling inconsistent orthographic variations and non-standard spellings typical of the Libyan dialect. The chi-square analysis revealed that certain features, such as email mentions and emotion indicators, were not significantly associated with dialect classification and were thus excluded from further analysis. Two main experiments were conducted: (1) evaluating the significance of meta-features extracted from the corpus using the chi-square test and (2) assessing classifier performance using different word and character n-gram representations. The classification experiments showed that Multinomial Naive Bayes (MNB) achieved the highest accuracy of 85.89% and an F1-score of 0.85741 when using a (1,2) word n-gram and (1,5) character n-gram representation. In contrast, Logistic Regression and Linear SVM exhibited slightly lower performance, with maximum accuracies of 84.41% and 84.73%, respectively. Additional evaluation metrics, including log loss, Cohen kappa, and Matthew correlation coefficient, further supported the effectiveness of MNB in this task. The results indicate that carefully selected n-gram representations and classification models play a crucial role in improving the accuracy of Libyan dialect identification. This study provides empirical benchmarks and insights for future research in Arabic dialect NLP applications.

Resilient road infrastructure is a cornerstone of the UN Sustainable Development Goals. Yet a primary indicator of network functionality and resilience is critically lacking: a comprehensive global baseline of road surface information. Here, we overcome this gap by applying a deep learning framework to a global mosaic of Planetscope satellite imagery from 2020 and 2024. The result is the first global multi-temporal dataset of road pavedness and width for 9.2 million km of critical arterial roads, achieving 95.5% coverage where nearly half the network was previously unclassified. This dataset reveals a powerful multi-scale geography of human development. At the planetary scale, we show that the rate of change in pavedness is a robust proxy for a country's development trajectory (correlation with HDI = 0.65). At the national scale, we quantify how unpaved roads constitute a fragile backbone for economic connectivity. We further synthesize our data into a global Humanitarian Passability Matrix with direct implications for humanitarian logistics. At the local scale, case studies demonstrate the framework's versatility: in Ghana, road quality disparities expose the spatial outcomes of governance; in Pakistan, the data identifies infrastructure vulnerabilities to inform climate resilience planning. Together, this work delivers both a foundational dataset and a multi-scale analytical framework for monitoring global infrastructure, from the dynamics of national development to the realities of local governance, climate adaptation, and equity. Unlike traditional proxies such as nighttime lights, which reflect economic activity, road surface data directly measures the physical infrastructure that underpins prosperity and resilience - at higher spatial resolution.

In light of recent progress in video editing, deep learning models focusing on both spatial and temporal dependencies have emerged as the primary method. However, these models suffer from the quadratic computational complexity of traditional attention mechanisms, making them difficult to adapt to long-duration and high-resolution videos. This limitation restricts their applicability in practical contexts such as real-time video processing. To tackle this challenge, we introduce a method to reduce both time and space complexity of these systems by proposing VRWKV-Editor, a novel video editing model that integrates a linear spatio-temporal aggregation module into video-based diffusion models. VRWKV-Editor leverages bidirectional weighted key-value recurrence mechanism of the RWKV transformer to capture global dependencies while preserving temporal coherence, achieving linear complexity without sacrificing quality. Extensive experiments demonstrate that the proposed method achieves up to 3.7x speedup and 60% lower memory usage compared to state-of-the-art diffusion-based video editing methods, while maintaining competitive performance in frame consistency and text alignment. Furthermore, a comparative analysis we conducted on videos with different sequence lengths confirms that the gap in editing speed between our approach and architectures with self-attention becomes more significant with long videos.