KAUST researchers developed a machine learning algorithm to control a deformable mirror within the Subaru Telescope's exoplanet imaging camera, compensating for atmospheric turbulence. The algorithm, which computes a partial singular value decomposition (SVD), outperforms a standard SVD by a factor of four. The KAUST team received a best paper award at the PASC Conference for this work, which has already been deployed at the Subaru Telescope. Why it matters: This advancement enables sharper images of exoplanets, facilitating their identification and study, and showcases the impact of optimizing core linear algebra algorithms.
The AQABA project, a collaboration involving KAUST and international institutions, studies air quality and climate change in the Arabian Basin using a marine research vessel. The vessel traveled from France through the Suez Canal, around the Arabian Peninsula, and stopped at KAUST. Researchers presented findings on atmospheric dust, air pollution, and aerosol measurements, highlighting the impact of dust on renewable energy and air pollution on health. Why it matters: The project provides crucial data for understanding and addressing climate challenges and air quality issues in the Middle East.
MBZUAI researchers introduce VideoMathQA, a new benchmark for evaluating mathematical reasoning in videos, requiring models to interpret visual information, text, and spoken cues. The dataset spans 10 mathematical domains with videos ranging from 10 seconds to over 1 hour, and includes multi-step reasoning annotations. The benchmark aims to evaluate temporal cross-modal reasoning and highlights the limitations of existing approaches in complex video-based mathematical problem solving.
KAUST collaborated with NASA's Langley Research Center to launch six weather balloons from KAUST's Coastal & Marine Laboratory, reaching an altitude of 35 kilometers. The balloons were equipped with instruments to measure meteorological properties and characterize the optical properties of aerosols, including a Compact Optical Backscatter Aerosol Detector (COBALD). The research focuses on understanding the impact of dust aerosols on the Arabian Peninsula, including their effects on climate, air quality, and solar energy. Why it matters: This collaboration advances understanding of atmospheric aerosols in the region, with implications for climate modeling, solar energy efficiency, and Red Sea ecosystems.
Patrick van der Smagt, Director of AI Research at Volkswagen Group, discussed the use of generative machine learning models for predicting and controlling complex stochastic systems in robotics. The talk highlighted examples in robotics and beyond and addressed the challenges of achieving quality and trust in AI systems. He also mentioned his involvement in a European industry initiative on trust in AI and his membership in the AI Council of the State of Bavaria. Why it matters: Understanding control in robotics, along with trust in AI, are key issues for further development of autonomous systems, especially in industrial applications within the GCC region.
A new benchmark, LongShOTBench, is introduced for evaluating multimodal reasoning and tool use in long videos, featuring open-ended questions and diagnostic rubrics. The benchmark addresses the limitations of existing datasets by combining temporal length and multimodal richness, using human-validated samples. LongShOTAgent, an agentic system, is also presented for analyzing long videos, with both the benchmark and agent demonstrating the challenges faced by state-of-the-art MLLMs.