'We had people come just to see it': Amazon delivers its first UK parcels by drone Amazon has become the first retailer in the UK to start a drone delivery service with a limited launch in Darlington, County Durham. Packages weighing less than 5lb (2.2kg) and containing everyday items such as beauty products, batteries and cables are now being delivered within a 7.5 mile (12km) radius of Amazon's fulfilment centre. The tech giant is convinced there is demand for ultra-fast deliveries and hopes to slowly expand the service. Rob Shield let Amazon use an Airbnb on his farm for its first test runs. Initially it was a novelty, so we were ordering everything under the sun, he says.

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US and UK sign major nuclear power deal: What does it include? British Prime Minister Keir Starmer and United States President Donald Trump have signed a multibillion-pound deal to expand nuclear power across both nations. Known as the Atlantic Partnership for Advanced Nuclear Energy, the agreement aims to speed up the construction of new reactors and provide reliable, low-carbon energy for high-demand sectors, including energy-intensive artificial intelligence data centres. Britain's largest energy supplier, Centrica, will pair up with the US firm X-energy to develop up to 12 advanced modular reactors in Hartlepool, a port town in northeast England, which could power 1.5 million homes and create up to 2,500 jobs. US nuclear technology company Holtec, France's state-backed energy giant EDF Energy, and United Kingdom real estate and investment firm Tritax will develop advanced data centres powered by small modular reactors (SMRs) in Nottinghamshire, East Midlands, valued at about 11 billion pounds ($15bn).

Powerful moment Charlie Kirk's widow Erika holds hands with Usha Vance on his final journey on Air Force Two REVEALED: The truth about the'vanishing plane' five miles from Charlie Kirk's assassination... as private jet owner is unmasked Charlie Kirk's incredible welcome to young gay man who wants to join his conservative movement And the armed militia mystery. FBI terror hunter blows the lid on search for Charlie Kirk's assassin... and the vital clue cops are desperate for Kristin Chenoweth fans surprised over her grieving comment on Charlie Kirk's final video about abortion Charlotte Tilbury reveals the secrets behind the Dallas Cowboys cheerleaders' flawless look Go inside the killing that has rocked America - on Daily Mail's podcast The Assassination of Charlie Kirk Charlie Kirk's gesture to my son tells you everything about the man: JILLIAN MICHAELS on her unlikely camaraderie with the conservative giant Joe Rogan is speechless as he learns of Charlie Kirk's assassination on his podcast McDonald's fans disgusted by what customer thinks is'parasite' found in Filet-O-Fish Walkers and hikers have an exciting opportunity to find meteorite fragments that scattered over Scotland this summer, scientists say. The bright meteor was witnessed by some Scots as it streaked across the sky in the early hours of Thursday July 3. It is believed to have exploded over northern Scotland, with the'fall zone' straddling Loch Treig in Lochaber, Highland. The aerial event was captured on some cameras and shared on social media, showing a big yellow spark soaring through the dark sky.

The difficulty of multiple-choice questions (MCQs) is a crucial factor for educational assessments. Predicting MCQ difficulty is challenging since it requires understanding both the complexity of reaching the correct option and the plausibility of distractors, i.e., incorrect options. In this paper, we propose a novel, two-stage method to predict the difficulty of MCQs. First, to better estimate the complexity of each MCQ, we use large language models (LLMs) to augment the reasoning steps required to reach each option. We use not just the MCQ itself but also these reasoning steps as input to predict the difficulty. Second, to capture the plausibility of distractors, we sample knowledge levels from a distribution to account for variation among students responding to the MCQ. This setup, inspired by item response theory (IRT), enable us to estimate the likelihood of students selecting each (both correct and incorrect) option. We align these predictions with their ground truth values, using a Kullback-Leibler (KL) divergence-based regularization objective, and use estimated likelihoods to predict MCQ difficulty. We evaluate our method on two real-world \emph{math} MCQ and response datasets with ground truth difficulty values estimated using IRT. Experimental results show that our method outperforms all baselines, up to a 28.3\% reduction in mean squared error and a 34.6\% improvement in the coefficient of determination. We also qualitatively discuss how our novel method results in higher accuracy in predicting MCQ difficulty.

We present Dur360BEV, a novel spherical camera autonomous driving dataset equipped with a high-resolution 128-channel 3D LiDAR and a RTK-refined GNSS/INS system, along with a benchmark architecture designed to generate Bird-Eye-View (BEV) maps using only a single spherical camera. This dataset and benchmark address the challenges of BEV generation in autonomous driving, particularly by reducing hardware complexity through the use of a single 360-degree camera instead of multiple perspective cameras. Within our benchmark architecture, we propose a novel spherical-image-to-BEV module that leverages spherical imagery and a refined sampling strategy to project features from 2D to 3D. Our approach also includes an innovative application of focal loss, specifically adapted to address the extreme class imbalance often encountered in BEV segmentation tasks, that demonstrates improved segmentation performance on the Dur360BEV dataset. The results show that our benchmark not only simplifies the sensor setup but also achieves competitive performance.

Long-term monitoring and exploration of extreme environments, such as underwater storage facilities, is costly, labor-intensive, and hazardous. Automating this process with low-cost, collaborative robots can greatly improve efficiency. These robots capture images from different positions, which must be processed simultaneously to create a spatio-temporal model of the facility. In this paper, we propose a novel approach that integrates data simulation, a multi-modal deep learning network for coordinate prediction, and image reassembly to address the challenges posed by environmental disturbances causing drift and rotation in the robots' positions and orientations. Our approach enhances the precision of alignment in noisy environments by integrating visual information from snapshots, global positional context from masks, and noisy coordinates. We validate our method through extensive experiments using synthetic data that simulate real-world robotic operations in underwater settings. The results demonstrate very high coordinate prediction accuracy and plausible image assembly, indicating the real-world applicability of our approach. The assembled images provide clear and coherent views of the underwater environment for effective monitoring and inspection, showcasing the potential for broader use in extreme settings, further contributing to improved safety, efficiency, and cost reduction in hazardous field monitoring. Code is available on https://github.com/ChrisChen1023/Micro-Robot-Swarm.

Knowledge components (KCs) mapped to problems help model student learning, tracking their mastery levels on fine-grained skills thereby facilitating personalized learning and feedback in online learning platforms. However, crafting and tagging KCs to problems, traditionally performed by human domain experts, is highly labor-intensive. We present a fully automated, LLM-based pipeline for KC generation and tagging for open-ended programming problems. We also develop an LLM-based knowledge tracing (KT) framework to leverage these LLM-generated KCs, which we refer to as KCGen-KT. We conduct extensive quantitative and qualitative evaluations validating the effectiveness of KCGen-KT. On a real-world dataset of student code submissions to open-ended programming problems, KCGen-KT outperforms existing KT methods. We investigate the learning curves of generated KCs and show that LLM-generated KCs have a comparable level-of-fit to human-written KCs under the performance factor analysis (PFA) model. We also conduct a human evaluation to show that the KC tagging accuracy of our pipeline is reasonably accurate when compared to that by human domain experts.

Pre-trained language models (PLMs) have made significant advances in natural language inference (NLI) tasks, however their sensitivity to textual perturbations and dependence on large datasets indicate an over-reliance on shallow heuristics. In contrast, inductive logic programming (ILP) excels at inferring logical relationships across diverse, sparse and limited datasets, but its discrete nature requires the inputs to be precisely specified, which limits their application. This paper proposes a bridge between the two approaches: neuro-symbolic contrastive learning. This allows for smooth and differentiable optimisation that improves logical accuracy across an otherwise discrete, noisy, and sparse topological space of logical functions. We show that abstract logical relationships can be effectively embedded within a neuro-symbolic paradigm, by representing data as logic programs and sets of logic rules. The embedding space captures highly varied textual information with similar semantic logical relations, but can also separate similar textual relations that have dissimilar logical relations. Experimental results demonstrate that our approach significantly improves the inference capabilities of the models in terms of generalisation and reasoning.

The integration of AI in education offers significant potential to enhance learning efficiency. Large Language Models (LLMs), such as ChatGPT, Gemini, and Llama, allow students to query a wide range of topics, providing unprecedented flexibility. However, LLMs face challenges, such as handling varying content relevance and lack of personalization. To address these challenges, we propose TutorLLM, a personalized learning recommender LLM system based on Knowledge Tracing (KT) and Retrieval-Augmented Generation (RAG). The novelty of TutorLLM lies in its unique combination of KT and RAG techniques with LLMs, which enables dynamic retrieval of context-specific knowledge and provides personalized learning recommendations based on the student's personal learning state. Specifically, this integration allows TutorLLM to tailor responses based on individual learning states predicted by the Multi-Features with Latent Relations BERT-based KT (MLFBK) model and to enhance response accuracy with a Scraper model. The evaluation includes user assessment questionnaires and performance metrics, demonstrating a 10\% improvement in user satisfaction and a 5\% increase in quiz scores compared to using general LLMs alone.