The Autonomous Robotics Research Center (ARRC) is developing underwater communication systems, including a multimode modem prototype, and has filed three patents. One key technology is the Universal Underwater Software Defined Modem (UniSDM), which supports sound, magnetic induction, light, and radio waves. ARRC also developed a network management framework for automatic network slicing (ANS) of communication resources. Why it matters: These advancements are crucial for improving underwater exploration, industrial maintenance, and marine monitoring in the region, enabling more efficient and reliable communication for underwater robots.
MBZUAI researchers introduce UniMed-CLIP, a unified Vision-Language Model (VLM) for diverse medical imaging modalities, trained on the new large-scale, open-source UniMed dataset. UniMed comprises over 5.3 million image-text pairs across six modalities: X-ray, CT, MRI, Ultrasound, Pathology, and Fundus, created using LLMs to transform classification datasets into image-text formats. UniMed-CLIP significantly outperforms existing generalist VLMs and matches modality-specific medical VLMs in zero-shot evaluations, improving over BiomedCLIP by +12.61 on average across 21 datasets while using 3x less training data.
KAUST has released its 2023 SDG Highlight Report, showcasing the university's efforts in advancing the UN Sustainable Development Goals (SDGs). Key projects featured include the KAUST Coral Restoration Initiative and ClimateCrete. The report also highlights the release of KAUST's new Sustainability Policy and the hosting of the Global Sustainability Development Conference. Why it matters: The report signals KAUST's commitment to aligning with Saudi Vision 2030 and integrating sustainability into its research, operations, and engagement with the global community.
MBZUAI and the UNDP have partnered to advance the use of AI for sustainable development, with MBZUAI becoming the founding knowledge partner for the UNDP’s AI for Sustainable Development Platform (AI4SDP). The partnership, formalized at the World Governments Summit 2024, will focus on environmental resilience, water resources management, climate adaptation, social cohesion, and reducing inequalities. MBZUAI will contribute climate research, use cases, and scenarios to the AI4SDP for implementation in the real world. Why it matters: This collaboration signifies a commitment to leveraging AI for addressing critical global challenges and sustainable development goals in the Arab region.
The Symposium on Data Mining and Applications (SDMA 2014) was organized by MEGDAM to foster collaboration among data mining and machine learning researchers in Saudi Arabia, GCC countries, and the Middle East. The symposium covered areas such as statistics, computational intelligence, pattern recognition, databases, Big Data Mining and visualization. Acceptance was based on originality, significance and quality of contribution.
This paper introduces a unified deep autoregressive model (UAE) for cardinality estimation that learns joint data distributions from both data and query workloads. It uses differentiable progressive sampling with the Gumbel-Softmax trick to incorporate supervised query information into the deep autoregressive model. Experiments show UAE achieves better accuracy and efficiency compared to state-of-the-art methods.
MBZUAI, in collaboration with the Abu Dhabi School of Government, held an ‘AI for Leadership’ training course for government sector leaders. The four-day course was designed by AI experts at MBZUAI to equip leaders with the knowledge to leverage AI applications. The course supports the UAE Strategy for Artificial Intelligence 2031 and aims to diversify the economy and improve government services. Why it matters: The initiative reflects the UAE's commitment to building a thriving AI ecosystem by empowering government leaders to apply AI in their organizations.
The Secure Systems Research Center (SSRC) has partnered with the University of New South Wales (UNSW Sydney) to research enhancements and scaling of the seL4 microkernel on edge devices. The collaboration aims to extend the seL4 microkernel to support dynamic virtualization, combining minimal trusted computing base with strong isolation. This will address challenges related to heterogeneous hardware, software, and environmental factors in edge computing. Why it matters: This partnership aims to improve the security of edge devices in critical sectors, addressing vulnerabilities in cyber-physical and autonomous systems.