KAUST's Rapid Research Response Team (R3T), including Professor Samir Hamdan, is working to understand and counteract the spread of COVID-19. The team assembled a complete homemade, one-step RT-PCR test, comparable to commercial kits, with a patent-free manufacturing recipe. KAUST R3T is also researching faster, more accurate point-of-care tests, including a CRISPR-based molecular test. Why it matters: This research provides accessible testing solutions and contributes to more effective and rapid detection methods for combating viral spread in the region and globally.
KAUST's Image and Video Understanding Lab is developing machine learning algorithms for computer vision and object tracking, with applications in video content search and UAV navigation. Their algorithms can detect specific activities in videos, helping platforms detect unwanted content and deliver relevant ads. The object tracking algorithm is also used to empower UAVs, enabling them to follow objects autonomously. Why it matters: This research enhances video content analysis and UAV capabilities, positioning KAUST as a leader in computer vision and AI applications within the region.
Naheed.pk, a Pakistani online retailer, has expanded its services to over 30 cities, offering same-day delivery in Karachi, Lahore, and Islamabad. The company has seen 100% year-on-year growth and aims to capture 5% of Pakistan's retail market share. Naheed.pk focuses on providing genuine products and a trustworthy shopping experience to compete with brick-and-mortar stores. Why it matters: The growth of e-commerce platforms like Naheed.pk signals a shift in consumer behavior in Pakistan and the increasing importance of online retail in the region's economy.
This paper presents six experiments evaluating personalization and user tracking in web search engine results. The experiments involve comparing search results based on VPN location (including UAE vs others), logged-in status, network type, search engine, browser, and trained Google accounts. The study measures total hits, first hit, and correlation between hits to identify patterns of personalization. Why it matters: The findings shed light on the extent of filter bubble effects and potential biases in search results for users in the UAE and globally.
Researchers at MBZUAI have introduced a novel approach to enhance Large Multimodal Models (LMMs) for autonomous driving by integrating 3D tracking information. This method uses a track encoder to embed spatial and temporal data, enriching visual queries and improving the LMM's understanding of driving scenarios. Experiments on DriveLM-nuScenes and DriveLM-CARLA benchmarks demonstrate significant improvements in perception, planning, and prediction tasks compared to baseline models.