A baby with a rare neurological disorder, airlifted to hospital after collapsing in a supermarket, is not out of the woods yet, said his father. Seven-month-old Rupert Smith, from Broughton, Flintshire, stopped breathing in a Tesco store in Broughton Park, on Monday. His mother Siobhan, 35, immediately called for help and administered CPR before emergency services, including paramedics, police and an air ambulance arrived. Rupert, who has a disorder called alternating hemiplegia of childhood (AHC), was flown to Alder Hey Children's Hospital in Liverpool for treatment. Dad Dave Smith said Rupert had continued to have quite significant seizures [in hospital] so they have been giving him medication and he has undergone various different tests.

Food Does the Grocery Cart Actually Need a Makeover? The rolling basket we dump our food in hasn't changed much in almost a century, and for good reason--it works. But meddling tech gurus think they know better. In the past few decades there have been numerous incremental changes to grocery stores, like the crazed proliferation of snacks and frozen food, security cameras tracking anything that moves, and self-checkout robots flashing in panic because they can't detect your Twix bar in the bag. Carts remain the open-ceiling prison cells on wheels they were 50 years ago, and baskets don't look much different either.

We introduce Grade School Math with Distracting Context (GSM-DC), a synthetic benchmark to evaluate Large Language Models' (LLMs) reasoning robustness against systematically controlled irrelevant context (IC). GSM-DC constructs symbolic reasoning graphs with precise distractor injections, enabling rigorous, reproducible evaluation. Our experiments demonstrate that LLMs are significantly sensitive to IC, affecting both reasoning path selection and arithmetic accuracy. Additionally, training models with strong distractors improves performance in both in-distribution and out-of-distribution scenarios. We further propose a stepwise tree search guided by a process reward model, which notably enhances robustness in out-of-distribution conditions.

This paper presents a novel approach to optimizing pricing and replenishment strategies in fresh food supermarkets by combining Long Short-Term Memory (LSTM) networks with Particle Swarm Optimization (PSO). The LSTM model, enhanced with an attention mechanism, is used to predict sales volumes, pricing trends, and spoilage rates over a seven-day period. The predictions generated by the LSTM model serve as inputs for the PSO algorithm, which iteratively optimizes pricing and replenishment strategies to maximize profitability while adhering to inventory constraints. The integration of cost-plus pricing allows for dynamic adjustments based on fixed and variable costs, ensuring real-time adaptability to market fluctuations. The framework not only maximizes profits but also reduces food waste, contributing to more sustainable supermarket operations. The attention mechanism enhances the interpretability of the LSTM model by identifying key time points and factors influencing sales, improving decision-making accuracy. This methodology bridges the gap between predictive modeling and optimization, offering a scalable solution for dynamic pricing and inventory management in fresh food retail and other industries dealing with perishable goods.

The rise of large language models (LLMs) has made natural language-driven route planning an emerging research area that encompasses rich user objectives. Current research exhibits two distinct approaches: direct route planning using LLM-as-Agent and graph-based searching strategies. However, LLMs in the former approach struggle to handle extensive map data, while the latter shows limited capability in understanding natural language preferences. Additionally, a more critical challenge arises from the highly heterogeneous and unpredictable spatio-temporal distribution of users across the globe. In this paper, we introduce a novel LLM-Assisted route Planning (LLMAP) system that employs an LLM-as-Parser to comprehend natural language, identify tasks, and extract user preferences and recognize task dependencies, coupled with a Multi-Step Graph construction with iterative Search (MSGS) algorithm as the underlying solver for optimal route finding. Our multi-objective optimization approach adaptively tunes objective weights to maximize points of interest (POI) quality and task completion rate while minimizing route distance, subject to three key constraints: user time limits, POI opening hours, and task dependencies. We conduct extensive experiments using 1,000 routing prompts sampled with varying complexity across 14 countries and 27 cities worldwide. The results demonstrate that our approach achieves superior performance with guarantees across multiple constraints.

Electronic shelf labels, returns machines, robot bag packers and yet more self-service tills – just some of the many technologies that UK retailers are embracing as they try to solve the problem of rising labour costs. Investment in automation was a constant drumbeat amid the flurry of festive trading updates from big retailers in the past few weeks, as they face higher staffing bills from April after the rise in the national minimum wage and employers' national insurance contributions (NICs). The investments could improve productivity – a key government aim – in an industry long reliant on cheap labour. However, they will also replace entry-level jobs and reduce the number of roles in a sector that is the UK's biggest employer. When the British Retail Consortium asked leading retailers' finance directors how they would be responding to the impending increase in employers' NICs, almost a third said they would be using more automation, although this sat behind raising prices, cutting head office jobs and reducing working hours.

But Google Maps has been causing chaos for some shoppers after pranksters set up a'phantom' Aldi in the middle of the Welsh countryside. The small village of Cyffylliog has been inundated with an'endless stream' of confused shoppers looking for somewhere to buy their groceries. Following Google's directions actually brought them to an empty field on a remote farm tens of miles away from the nearest supermarket. While it might have been added as a joke, the fake Aldi has since led to chaos for this small community as deliveries have begun to arrive in search of the non-existent supermarket. The misguided prank has even led to real Aldi deliveries arriving on one farmer's doorstep and becoming stuck on the narrow lanes.

Rapid advancements in artificial intelligence (AI) have made it crucial to integrate moral reasoning into AI systems. However, existing models and datasets often overlook regional and cultural differences. To address this shortcoming, we have expanded the JCommonsenseMorality (JCM) dataset, the only publicly available dataset focused on Japanese morality. The Extended JCM (eJCM) has grown from the original 13,975 sentences to 31,184 sentences using our proposed sentence expansion method called Masked Token and Label Enhancement (MTLE). MTLE selectively masks important parts of sentences related to moral judgment and replaces them with alternative expressions generated by a large language model (LLM), while re-assigning appropriate labels. The model trained using our eJCM achieved an F1 score of 0.857, higher than the scores for the original JCM (0.837), ChatGPT one-shot classification (0.841), and data augmented using AugGPT, a state-of-the-art augmentation method (0.850). Specifically, in complex moral reasoning tasks unique to Japanese culture, the model trained with eJCM showed a significant improvement in performance (increasing from 0.681 to 0.756) and achieved a performance close to that of GPT-4 Turbo (0.787). These results demonstrate the validity of the eJCM dataset and the importance of developing models and datasets that consider the cultural context.

Mediation is often treated as an extension of negotiation, without taking into account the unique role that norms and facts play in legal mediation. Additionally, current approaches for updating argument acceptability in response to changing variables frequently require the introduction of new arguments or the removal of existing ones, which can be inefficient and cumbersome in decision-making processes within legal disputes. In this paper, our contribution is two-fold. First, we introduce a QuAM (Quantitative Argumentation Mediate) framework, which integrates the parties' knowledge and the mediator's knowledge, including facts and legal norms, when determining the acceptability of a mediation goal. Second, we develop a new formalism to model the relationship between the acceptability of a goal argument and the values assigned to a variable associated with the argument. We use a real-world legal mediation as a running example to illustrate our approach.

The humble jar of honey might seem sweet and innocent, but experts warn that British shoppers have been getting stung when spending on this staple. Investigations have revealed that unscrupulous honey producers around the world bulk out their products with cheap sugars that are almost impossible to detect. However, scientists have now developed a test which can easily spot the difference between fake and real honey - without even opening the jar. The light-based technique can detect the unique chemical signature of real honey as well as the syrups that try to imitate it. While the test isn't readily available yet, experts told MailOnline that consumers may be able to spot the frauds in their cupboards using nothing more than their phone torch within five to 10 years.