Large Language Models (LLMs) have revolutionized the ability to understand and generate text, enabling significant progress in automatic knowledge graph construction from text (Text2KG). Many Text2KG methods, however, rely on iterative LLM prompting, making them computationally expensive and prone to overlooking complex relations distributed throughout the text. To address these limitations, we propose InvertiTune, a framework that combines a controlled data generation pipeline with supervised fine-tuning (SFT). Within this framework, the data-generation pipeline systematically extracts subgraphs from large knowledge bases, applies noise filtering, and leverages LLMs to generate corresponding natural text descriptions, a task more aligned with LLM capabilities than direct KG generation from text. This pipeline enables generating datasets composed of longer texts paired with larger KGs that better reflect real-world scenarios compared to existing benchmarks, thus supporting effective SFT of lightweight models for single-shot KG construction. Experimental results on CE12k, a dataset generated using the introduced pipeline, show that InvertiTune outperforms larger non-fine-tuned LLMs as well as state-of-the-art Text2KG approaches, while also demonstrating stronger cross-dataset generalization on CrossEval-1200, a test set created from three established benchmark datasets and CE12k. These findings highlight the importance of realistic, high-quality training data for advancing efficient and high-performing Text2KG systems.

Previous work shows evidence that cerebrospinal fluid (CSF) plays a crucial role in brain waste clearance processes, and that altered flow patterns are associated with various diseases of the central nervous system. In this study, we investigate the potential of deep learning to predict the distribution in human brain of a gadolinium-based CSF contrast agent (tracer) administered intrathecal. For this, T1-weighted magnetic resonance imaging (MRI) scans taken at multiple time points before and after intrathecal injection were utilized. We propose a U-net-based supervised learning model to predict pixel-wise signal increases at their peak after 24 hours. Its performance is evaluated based on different tracer distribution stages provided during training, including predictions from baseline scans taken before injection. Our findings indicate that using imaging data from just the first two hours post-injection for training yields tracer flow predictions comparable to those trained with additional later-stage scans. The model was further validated by comparing ventricular reflux gradings provided by neuroradiologists, and inter-rater grading among medical experts and the model showed excellent agreement. Our results demonstrate the potential of deep learning-based methods for CSF flow prediction, suggesting that fewer MRI scans could be sufficient for clinical analysis, which might significantly improve clinical efficiency, patient well-being, and lower healthcare costs.

The high seas are probably the most lawless place left on Earth. They're a portal back in time to the way the world looked for most of our history: fierce and open competition for resources and contested territories. Pirating continues to be a way to make a living. It's not a complete free-for-all--most countries require registration of fishing vessels and enforce environmental protocols. Cooperative agreements between countries oversee fisheries in international waters.

Some of Norway's largest companies are joining forces in establishing a national powerhouse for artificial intelligence. Its aim is to improve the quality and capacity for research, education and innovation in the field. Norway has a huge potential to be a pioneer in Artificial Intelligence (AI), but it needs resources and collaboration in order not to lag behind. To strengthen national efforts on artificial intelligence, Telenor, NTNU and SINTEF are inviting Norwegian businesses to partner on the new Norwegian Open AI Lab. While the Norwegian Open AI Lab will develop solutions specific to the partners' industries, it will also consider opportunities where Norway can take positions internationally.

Telenor Group today announces two specific initiatives to support entrepreneurship and competence building in Norway. The ambition is to strengthen the nation s competitiveness and to stimulate job creation by Norwegian startups. In collaboration with the Norwegian University of Science and Technology (NTNU) and the leading research institute SINTEF, Telenor will establish a lab focused on artificial intelligence and big data at NTNU in Trondheim, Norway. As the second initiative, Telenor will develop and launch a dedicated, next-generation Internet of Things (IoT) network in several Norwegian cities. Norwegian startups and students will get cost-free access to the IoT network in order to develop and test their products and services. The first pilot will be located in Oslo, in collaboration with StartupLab.

In collaboration with the Norwegian University of Science and Technology (NTNU) and the leading research institute SINTEF, Telenor will establish a lab focused on artificial intelligence and big data at NTNU in Trondheim, Norway. As the second initiative, Telenor will develop and launch a dedicated, next-generation Internet of Things (IoT) network in several Norwegian cities. Norwegian startups and students will get cost-free access to the IoT network in order to develop and test their products and services. The first pilot will be located in Oslo, in collaboration with StartupLab. "We need to build critical competencies within artificial intelligence and we want to give Norwegian startups the resources they need to succeed. This is imperative for our ability to seize digital opportunities and contribute to creating new jobs. Startups play a key role in net job creation. We aim to stimulate productivity in Norway by developing new competencies and supporting the startup community," says Sigve Brekke, President and CEO, Telenor Group.