The ninth edition of the International Exhibition for National Security and Resilience (ISNR) 2026 is set to launch next week in Abu Dhabi. This biennial event typically showcases innovations in national security, policing, and disaster management technologies. It provides a platform for government entities, industry professionals, and technology providers to present solutions and foster collaborations. Why it matters: The launch of this major security exhibition underlines the UAE's continuous focus on enhancing its national security infrastructure and adopting advanced technologies, which often include AI applications in surveillance, threat detection, and emergency response.
The ninth edition of the International Exhibition for National Security and Resilience (ISNR) is set to launch next week in Abu Dhabi. This event will serve as a significant platform for global leaders, experts, and companies to showcase advanced solutions and discuss strategies in national security. It aims to foster collaboration and innovation across various defense and security sectors. Why it matters: Such exhibitions often feature advanced technologies including AI applications in surveillance, cybersecurity, and robotics, making it relevant for tracking regional technology adoption in critical infrastructure.
KAUST Professor Pierre Magistretti received the 2016 Fondation IPSEN Neuronal Plasticity prize for his work in neuroenergetics. The award recognizes Magistretti's contributions to understanding the relationship between neuronal activity and brain energy consumption. He shares the award with Dr. David Attwell and Dr. Marcus Raichle, and will be honored at FENS in Copenhagen. Why it matters: This award highlights KAUST's contribution to international neuroscience research and strengthens its reputation in biological and environmental science.
The paper introduces MedNNS, a neural network search framework designed for medical imaging, addressing challenges in architecture selection and weight initialization. MedNNS constructs a meta-space encoding datasets and models based on their performance using a Supernetwork-based approach, expanding the model zoo size by 51x. The framework incorporates rank loss and Fréchet Inception Distance (FID) loss to capture inter-model and inter-dataset relationships, improving alignment in the meta-space and outperforming ImageNet pre-trained DL models and SOTA NAS methods.