Science writer Dava Sobel spoke at KAUST in 2019 about the importance of longitude and precision timekeeping for navigation. She discussed the historical difficulties in determining longitude, contrasting it with the ease of finding latitude. Sobel highlighted the Longitude Act of 1714 and figures like John Harrison who addressed these challenges. Why it matters: This lecture exposed the KAUST community to the historical context of navigation and the crucial role of timekeeping, relevant to contemporary technologies like GPS.
KAUST's Winter Enrichment Program (WEP) is scheduled from January 13-24, featuring the theme of "TIME." The festival includes 12 keynote lectures from international experts, including a Nobel laureate, and explores topics like biological clocks, sleep, aging, and the universe. The program also offers workshops, exhibitions, and virtual reality experiences. Why it matters: Such science festivals can boost STEM engagement and knowledge dissemination within Saudi Arabia and internationally, aligning with KAUST's mission to promote scientific advancement.
Nobel laureate Dr. Michael Young from Rockefeller University presented his research on circadian rhythms at KAUST as part of the 2019 Winter Enrichment Program. His work on Drosophila genes has significantly advanced the understanding of circadian rhythm mechanisms. Young's research identified nine genes that regulate circadian rhythmicity at the molecular level, influencing thousands of gene expression patterns. Why it matters: This highlights KAUST's role in hosting leading international researchers and fostering scientific exchange on fundamental biological processes.
In 2019, the McLaren Group attended KAUST's Winter Enrichment Program to discuss their extreme performance research partnership. McLaren representatives highlighted the importance of the partnership, providing access to KAUST's researchers and facilities while offering real-world applications for technologies. McLaren emphasized the need for continuous improvement in high-speed R&D to maintain a competitive edge. Why it matters: This partnership highlights KAUST's role in providing advanced research capabilities to cutting-edge industries, fostering innovation and practical application of research in demanding environments.
MBZUAI researchers presented a new approach to video analysis at ICCV in Paris, led by Syed Talal Wasim. The approach builds on still image processing techniques like focal modulation to analyze spatial and temporal information in video separately. It aims to improve temporal aggregation while avoiding the computational complexity of transformers. Why it matters: This research advances video understanding in computer vision by offering a more efficient method for temporal modeling, crucial for applications like activity recognition and video surveillance.
This paper introduces a decentralized multi-agent decision-making framework for search and action problems under time constraints, treating time as a budgeted resource where actions have costs and rewards. The approach uses probabilistic reasoning to optimize decisions, maximizing reward within the given time. Evaluated in a simulated search, pick, and place scenario inspired by the Mohamed Bin Zayed International Robotics Challenge (MBZIRC), the algorithm outperformed benchmark strategies. Why it matters: The framework's validation in a Gazebo environment signals potential for real-world robotic applications, particularly in time-sensitive and cooperative tasks within the robotics domain in the UAE.
Juan Carlos Izpisua Belmonte from the Salk Institute discussed aging and regenerative medicine at the KAUST 2019 Winter Enrichment Program. His team is combining gene editing and stem cell technologies to grow rat organs in mice and human cells in pig and cattle embryos. The Salk team is collaborating with KAUST to rejuvenate organs using noncoding RNAs and small metabolites. Why it matters: This research collaboration between KAUST and the Salk Institute explores innovative approaches to address age-related diseases and organ regeneration, with potential long-term impacts on healthcare in the region.
Researchers in Saudi Arabia have developed a deep learning framework for automated counting and geolocation of palm trees using aerial images. The system uses a Faster R-CNN model trained on a dataset of 10,000 palm tree instances collected in the Kharj region using DJI drones. Geolocation accuracy of 2.8m was achieved using geotagged metadata and photogrammetry techniques.