Our work addresses the challenges of understanding tables. Existing methods often struggle with the unpredictable nature of table content, leading to a reliance on preprocessing and keyword matching. They also face limitations due to the lack of contextual information, which complicates the reasoning processes of large language models (LLMs). To overcome these challenges, we introduce an entity-oriented search method to improve table understanding with LLMs. This approach effectively leverages the semantic similarities between questions and table data, as well as the implicit relationships between table cells, minimizing the need for data preprocessing and keyword matching. Additionally, it focuses on table entities, ensuring that table cells are semantically tightly bound, thereby enhancing contextual clarity. Furthermore, we pioneer the use of a graph query language for table understanding, establishing a new research direction. Experiments show that our approach achieves new state-of-the-art performances on standard benchmarks WikiTableQuestions and TabFact.

Quick, efficient and with a bird's eye view of any scene, more police departments are embracing the use of drones to carry out law enforcement work, with some blue cities now even using them to respond to 911 calls. Around 1,500 police departments across the country are currently using drones in some form, according to a report by the Electronic Frontier Foundation, a digital privacy group, with agencies deploying the technology for crowd control purposes, missing people searches, tracking fleeing suspects or mapping crime scenes. Steep budget cuts and dwindling staff numbers in blue cities, in particular, make drones both an effective and cost-saving tool for police in Democratic strongholds. A law enforcement official sets up a drone during a manhunt for suspect Robert Card following a mass shooting on Oct. 27, 2023, in Monmouth, Maine. Today's police drones are much bigger than regular drones commonly used for recreational purposes, with much longer battery lives and features such as thermal sensors, loudspeakers, spotlights or beacons.

Harnessing rich, but unstructured information on social networks in real-time and showing it to relevant audience based on its geographic location is a major challenge. The system developed, TwitterTagger, geotags tweets and shows them to users based on their current physical location. Experimental validation shows a performance improvement of three orders by TwitterTagger compared to that of the baseline model.