A crash between two double-decker buses close to a city centre has left 13 people needing hospital treatment. Two Bee Network buses crashed on Rochdale Road off Livesey Street, Manchester, but no-one was seriously injured, Greater Manchester Police (GMP) said. Images show debris strewn across the highway as one of the vehicles appeared to have hit the back of the other. A GMP spokesman said the road remained shut while emergency services were at the scene.PatKarneyAn air ambulance was seen at the site of the crash on Rochdale Road Police were called to the incident at about 08:30 GMT. Manchester councillor Pat Karney, who was at the site, posted on X to say there had been "unbelievable damage" to the front of the bus.

Large Language Models (LLMs) often encounter conflicts between their learned, internal (parametric knowledge, PK) and external knowledge provided during inference (contextual knowledge, CK). Understanding how LLMs models prioritize one knowledge source over the other remains a challenge. In this paper, we propose a novel probing framework to explore the mechanisms governing the selection between PK and CK in LLMs. Using controlled prompts designed to contradict the model's PK, we demonstrate that specific model activations are indicative of the knowledge source employed. We evaluate this framework on various LLMs of different sizes and demonstrate that mid-layer activations, particularly those related to relations in the input, are crucial in predicting knowledge source selection, paving the way for more reliable models capable of handling knowledge conflicts effectively.

A man who pretended to be a paramedic has been found guilty of raping and sexually assaulting women he met on an online dating website. Jamie Kadolski, 24, of Ladysmith Road, Norwich, was found guilty of committing nine sexual offences over an 18-month period. During the trial at Norwich Crown Court he denied the charges made by four different women, which he met on Tinder. The court had previously heard how the former ambulance call handler had told the women he was a paramedic and had used stickers to hide his real role on his work ID card.SuppliedKadolski worked in medical sector but never as a paramedic Kadolski worked as a call handler for the East of England Ambulance Service. The prosecution told the jury that he used stickers to hide his more junior role, so he could claim to the women he met that he was a paramedic.

This would likely have three parts: a lander, an autonomous ice-thawing robot, and some sort of self-navigating submersible. Indeed, several groups from multiple countries already have working prototypes of ice-diving robots and smart submersibles that they are set to test in Earth's own frigid landscapes, from Alaska to Antarctica, in the next few years But Earth's oceans are pale simulacra of Europa's extreme environment. To plumb the ocean of this Jovian moon, engineers must work out a way to get missions to survive a never-ending rain of radiation that fries electronic circuits. They must also plow through an ice shell that's at least twice as thick as Mount Everest is tall. "There are a lot of hard problems that push up right against the limits of what's possible," says Richard Camilli, an expert on autonomous robotic systems at the Woods Hole Oceanographic Institution's Deep Submergence Laboratory.

Understanding spatio-temporal patterns in polar ice layers is essential for tracking changes in ice sheet balance and assessing ice dynamics. While convolutional neural networks are widely used in learning ice layer patterns from raw echogram images captured by airborne snow radar sensors, noise in the echogram images prevents researchers from getting high-quality results. Instead, we focus on geometric deep learning using graph neural networks, aiming to build a spatio-temporal graph neural network that learns from thickness information of the top ice layers and predicts for deeper layers. In this paper, we developed a novel multi-branch spatio-temporal graph neural network that used the GraphSAGE framework for spatio features learning and a temporal convolution operation to capture temporal changes, enabling different branches of the network to be more specialized and focusing on a single learning task. We found that our proposed multi-branch network can consistently outperform the current fused spatio-temporal graph neural network in both accuracy and efficiency.

The wreck was like a bug on the wall, a jumbly shape splayed on the abyssal plain. It was noticed by a team of autonomous-underwater-vehicle operators on board a subsea exploration vessel, working at an undisclosed location in the Atlantic Ocean, about a thousand miles from the nearest shore. The analysts belonged to a small private company that specializes in deep-sea search operations; I have been asked not to name it. They were looking for something else. In the past decade, the company has helped to transform the exploration of the seabed by deploying fleets of A.U.V.s--underwater drones--which cruise in formation, mapping large areas of the ocean floor with high-definition imagery.

This paper presents a communication and energy-aware Multi-UAV Coverage Path Planning (mCPP) method for scenarios requiring continuous inter-UAV communication, such as cooperative search and rescue and surveillance missions. Unlike existing mCPP solutions that focus on energy, time, or coverage efficiency, our approach generates coverage paths that require minimal the communication range to maintain inter-UAV connectivity while also optimizing energy consumption. The mCPP problem is formulated as a multi-objective optimization task, aiming to minimize both the communication range requirement and energy consumption. Our approach significantly reduces the communication range needed for maintaining connectivity while ensuring energy efficiency, outperforming state-of-the-art methods. Its effectiveness is validated through simulations on complex and arbitrary shaped regions of interests, including scenarios with no-fly zones. Additionally, real-world experiment demonstrate its high accuracy, achieving 99\% consistency between the estimated and actual communication range required during a multi-UAV coverage mission involving three UAVs.

The spatiotemporally continuous data of normalized difference snow index (NDSI) are key to understanding the mechanisms of snow occurrence and development as well as the patterns of snow distribution changes. However, the presence of clouds, particularly prevalent in polar regions such as the Greenland Ice Sheet (GrIS), introduces a significant number of missing pixels in the MODIS NDSI daily data. To address this issue, this study proposes the utilization of a spatiotemporal extreme gradient boosting (STXGBoost) model generate a comprehensive NDSI dataset. In the proposed model, various input variables are carefully selected, encompassing terrain features, geometry-related parameters, and surface property variables. Moreover, the model incorporates spatiotemporal variation information, enhancing its capacity for reconstructing the NDSI dataset. Verification results demonstrate the efficacy of the STXGBoost model, with a coefficient of determination of 0.962, root mean square error of 0.030, mean absolute error of 0.011, and negligible bias (0.0001). Furthermore, simulation comparisons involving missing data and cross-validation with Landsat NDSI data illustrate the model's capability to accurately reconstruct the spatial distribution of NDSI data. Notably, the proposed model surpasses the performance of traditional machine learning models, showcasing superior NDSI predictive capabilities. This study highlights the potential of leveraging auxiliary data to reconstruct NDSI in GrIS, with implications for broader applications in other regions. The findings offer valuable insights for the reconstruction of NDSI remote sensing data, contributing to the further understanding of spatiotemporal dynamics in snow-covered regions.

Artificial intelligence can help accurately map and track penguin colonies in Antarctica by analysing tourist photos. "Right now, everyone has a camera in their pocket, and so the sheer volume of data being collected around the world is incredible," says Heather Lynch at Stony Brook University in New York. Haoyu Wu at Stony Brook University and his colleagues, including Lynch, used an AI tool developed by Meta to highlight Adélie penguins in photographs taken by tourists or scientists on the ground. With guidance from a human expert, the AI tool was able to automatically identify and outline entire colonies in photos. This semi-automated method is much faster than doing everything manually because the AI tool takes just 5 to 10 seconds per image, compared with a person taking 1 to 2 minutes, says Wu. The team also created a 3D digital model of the Antarctic landscape using satellite imagery and terrain elevation data.

Machine learning (ML) for text classification has been widely used in various domains, such as toxicity detection, chatbot consulting, and review analysis. These applications can significantly impact ethics, economics, and human behavior, raising serious concerns about trusting ML decisions. Several studies indicate that traditional metrics, such as model confidence and accuracy, are insufficient to build human trust in ML models. These models often learn spurious correlations during training and predict based on them during inference. In the real world, where such correlations are absent, their performance can deteriorate significantly. To avoid this, a common practice is to test whether predictions are reasonable. Along with this, a challenge known as the trustworthiness oracle problem has been introduced. Due to the lack of automated trustworthiness oracles, the assessment requires manual validation of the decision process disclosed by explanation methods, which is time-consuming and not scalable. We propose TOKI, the first automated trustworthiness oracle generation method for text classifiers, which automatically checks whether the prediction-contributing words are related to the predicted class using explanation methods and word embeddings. To demonstrate its practical usefulness, we introduce a novel adversarial attack method targeting trustworthiness issues identified by TOKI. We compare TOKI with a naive baseline based solely on model confidence using human-created ground truths of 6,000 predictions. We also compare TOKI-guided adversarial attack method with A2T, a SOTA adversarial attack method. Results show that relying on prediction uncertainty cannot distinguish between trustworthy and untrustworthy predictions, TOKI achieves 142% higher accuracy than the naive baseline, and TOKI-guided adversarial attack method is more effective with fewer perturbations than A2T.