This paper introduces Saudi-Dialect-ALLaM, a LoRA fine-tuned version of the Saudi Arabian foundation model ALLaM-7B-Instruct-preview, designed to improve the generation of Saudi dialects (Najdi and Hijazi). The model is trained on a private dataset of 5,466 synthetic instruction-response pairs, with two variants explored: Dialect-Token and No-Token training. Results indicate that the Dialect-Token model achieves superior dialect control and fidelity compared to generic instruction models, although the dataset and model weights are not released.
A KAUST-led team developed NIRVANA, a portable, briefcase-sized device for rapid detection and sequencing of SARS-CoV-2, influenza, and other viruses. The test utilizes isothermal recombinase amplification (RPA) and was validated on clinical samples and wastewater. NIRVANA can differentiate SARS-CoV-2 strains and doesn't require expensive infrastructure. Why it matters: This innovation enables rapid, decentralized virus detection and surveillance, crucial for pandemic response and monitoring new variants across the region.
KAUST Discovery Professor Jesper Tegnér collaborated with UK researchers to develop algorithms explaining decision-making in insects and rats. Assoc. Prof. Robert Hoehndorf's lab introduced a tool for identifying genetic variants linked to rare diseases based on patient symptoms. KAUST scientists also studied monkeypox infection of human skin using stem cells and marine microbiome adaptation to thermal changes. Why it matters: These diverse research projects highlight KAUST's contributions to computational biology, virology, and marine science, advancing knowledge with implications for healthcare and environmental challenges.
KAUST researchers developed CovMT, a COVID-19 mutation tracking system for authorities and scientists to detect variants. CovMT tracks mutation fingerprints using daily data from the GISAID database of over 1.5 million viral genomes. The system identifies mutation hot spots, enabling public health authorities to stay ahead of new variants. Why it matters: This system provides a tool for rapid variant detection and informed public health decision-making in the region and globally.
KAUST Professor Peter Richtárik received a Distinguished Speaker Award at the Sixth International Conference on Continuous Optimization (ICCOPT 2019) in Berlin. Richtárik's lecture series, totaling six hours, focused on stochastic gradient descent (SGD) methods, drawing from recent research by his KAUST group. He highlighted key principles and new variants of SGD, the key method for training modern machine learning models. Why it matters: This award recognizes KAUST's contribution to fundamental machine learning optimization, which is critical for advancing AI in the region.