MBZUAI Professor Agathe Guilloux developed the SigLasso model to forecast hospitalizations using real-time data from Google and Météo France during the COVID-19 pandemic. The model integrates mobility data and weather patterns to predict hospitalization rates 10-14 days in advance. SigLasso outperformed industry standards like GRU and Neural CDE in reducing reconstruction error. Why it matters: This research demonstrates the potential of AI to improve healthcare resource allocation and crisis management by accurately predicting patient flow using readily available data.
KAUST researchers Anthony Cioppa and Silvio Giancola have developed SoccerNet, an open platform for AI-driven sports analysis. SoccerNet uses a large reference set of soccer game recordings (500 games, 850 hours) to provide a platform for research. It enables researchers to develop AI systems that understand and analyze soccer games. Why it matters: This platform addresses the challenge of limited datasets in sports AI research, fostering innovation and standardized performance comparison.
The researchers introduce KAU-CSSL, the first continuous Saudi Sign Language (SSL) dataset focusing on complete sentences. They propose a transformer-based model using ResNet-18 for spatial feature extraction and a Transformer Encoder with Bidirectional LSTM for temporal dependencies. The model achieved 99.02% accuracy in signer-dependent mode and 77.71% in signer-independent mode, advancing communication tools for the SSL community.
KAUST Professor Peter Markowich discusses the role of mathematics in football, describing a match as a random process with a drift. The randomness stems from player conditions, referee decisions, weather, and more, while the drift represents the higher probability of the better team winning. He notes that the complexity arising from 11 players on each side increases the randomness compared to sports like tennis. Why it matters: This perspective highlights the interplay of chance and skill in sports, offering a mathematical lens for understanding game dynamics.
Technology Innovation Institute's (TII) Directed Energy Research Center (DERC) is integrating machine learning (ML) techniques into signal processing to accelerate research. One project used convolutional neural networks to predict COVID-19 pneumonia from chest x-rays with 97.5% accuracy. DERC researchers also demonstrated that ML-based signal and image processing can retrieve up to 68% of text information from electromagnetic emanations. Why it matters: This adoption of ML for signal processing at TII highlights the potential for advanced AI techniques to enhance research and security applications in the UAE.
KAUST professor Pierre Magistretti has been elected to the Norwegian Academy of Science and Letters. His election recognizes his contributions to neuroscience, specifically his work on lactate's role in brain function. Magistretti's research focuses on the lactate shuttle system and how neurons and glial cells cooperate to meet energy demands. Why it matters: This honor highlights KAUST's contribution to international neuroscience and can foster further collaboration in the field.