Trump administration'unlocks' 140MILLION barrels of precious Iranian oil with major policy change to fight back against'hoarding' China... here's what it means for your wallet Buffy the Vampire Slayer star Nicholas Brendon dead at 54 as'heartbroken' family reveal cause of death Joseph Duggar's wife Kendra is arrested for allegedly endangering welfare of a minor as he faces new charges Behind closed doors, the Duggar family's next nightmare began long before Joseph's arrest: Insiders reveal what they knew and how they plan to recover America is about to be torn apart by a financial tsunami - and it's not just an oil crisis to fear. However, it seems not every corner of Britain is quite so beautiful - as a survey has revealed the least scenic locations. Voters on the Scenic Or Not survey awarded the top spot to Basingstoke's Newbury Road. This unappealing location received the lowest possible score, with just one out of 10 for'scenicness'. And while Cornwall might be renowned for its beautiful scenery, a rather less attractive part of the county - the Electricity Station in Landulph - joins Basingstoke at the bottom of the pile.

Accurate electric vehicle (EV) charging demand forecasting is essential for stable grid operation and proactive EV participation in electricity market. Existing forecasting methods, particularly those based on graph neural networks, are often limited to modeling pairwise relationships between stations, failing to capture the complex, group-wise dynamics inherent in urban charging networks. To address this gap, we develop a novel forecasting framework namely HyperCast, leveraging the expressive power of hypergraphs to model the higher-order spatiotemporal dependencies hidden in EV charging patterns. HyperCast integrates multi-view hypergraphs, which capture both static geographical proximity and dynamic demand-based functional similarities, along with multi-timescale inputs to differentiate between recent trends and weekly periodicities. The framework employs specialized hyper-spatiotemporal blocks and tailored cross-attention mechanisms to effectively fuse information from these diverse sources: views and timescales. Extensive experiments on four public datasets demonstrate that HyperCast significantly outperforms a wide array of state-of-the-art baselines, demonstrating the effectiveness of explicitly modeling collective charging behaviors for more accurate forecasting.

Information Extraction (IE) refers to automatically extracting structured relation tuples from unstructured texts. Common IE solutions, including Relation Extraction (RE) and open IE systems, can hardly handle cross-sentence tuples, and are severely restricted by limited relation types as well as informal relation specifications (e.g., free-text based relation tuples). In order to overcome these weaknesses, we propose a novel IE framework named QA4IE, which leverages the flexible question answering (QA) approaches to produce high quality relation triples across sentences. Based on the framework, we develop a large IE benchmark with high quality human evaluation. This benchmark contains 293K documents, 2M golden relation triples, and 636 relation types. We compare our system with some IE baselines on our benchmark and the results show that our system achieves great improvements.