KAUST ·
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KAUST ·
KAUST and Technische Universität München (TUM) have been collaborating on research since 2009, focusing on chemistry, computer science, and mathematics. TUM President Prof. Herrmann visited KAUST on March 25, discussing the KAUST-TUM collaboration in high-performance computing and catalytic chemistry. He emphasized the need for an entrepreneurial and interdisciplinary approach to solve complex scientific problems, highlighting trust and complementary expertise as key to the partnership's success. Why it matters: This partnership strengthens research capabilities in Saudi Arabia, promoting innovation and addressing complex challenges through international collaboration in key areas like computing and chemistry.
arXiv ·
The Symposium on Data Mining and Applications (SDMA 2014) was organized by MEGDAM to foster collaboration among data mining and machine learning researchers in Saudi Arabia, GCC countries, and the Middle East. The symposium covered areas such as statistics, computational intelligence, pattern recognition, databases, Big Data Mining and visualization. Acceptance was based on originality, significance and quality of contribution.
KAUST ·
Areej Aljarb is a Ph.D. student in material science and engineering at KAUST, researching 2D materials within the KAUST 2D Materials Research Lab under Professors Lain-Jong Li and Xixiang Zhang. Her research focuses on the controlled growth and fundamental phenomena of two-dimensional atomic layer thin materials, specifically controlling the orientation of 2D transition metal dichalcogenides (TMDs). Aljarb aims to achieve single-orientation epitaxial monolayer 2D TMDs to fully utilize the potential of these materials. Why it matters: This highlights KAUST's commitment to fostering local talent and contributing to advanced materials research with potential applications in various technology sectors.
arXiv ·
This paper introduces a novel evaluation framework for Arabic language models, addressing gaps in linguistic accuracy and cultural alignment. The authors analyze existing datasets and present the Arabic Depth Mini Dataset (ADMD), a curated collection of 490 questions across ten domains. Evaluating GPT-4, Claude 3.5 Sonnet, Gemini Flash 1.5, CommandR 100B, and Qwen-Max using ADMD reveals performance variations, with Claude 3.5 Sonnet achieving the highest accuracy at 30%. Why it matters: The work emphasizes the importance of cultural competence in Arabic language model evaluation, providing practical insights for improvement.
arXiv ·
This paper explores Dialectal Arabic (DA) to Modern Standard Arabic (MSA) machine translation using prompting and fine-tuning techniques for Levantine, Egyptian, and Gulf dialects. The study found that few-shot prompting outperformed zero-shot and chain-of-thought methods across six large language models, with GPT-4o achieving the highest performance. A quantized Gemma2-9B model achieved a chrF++ score of 49.88, outperforming zero-shot GPT-4o (44.58). Why it matters: The research provides a resource-efficient pipeline for DA-MSA translation, enabling more inclusive language technologies by addressing the challenges posed by dialectal variations in Arabic.
KAUST ·
The Red Sea Development Company (TRSDC) and KAUST have signed a Master Research Agreement (MRA) to collaborate on sustainability research. Prior collaborations included flora and fauna assessments and the Brains-for-Brine Challenge. The MRA focuses on marine environments, waste management, food production, energy conservation, and carbon sequestration. Why it matters: This partnership aims to develop regenerative tourism practices, preserve the Red Sea's biodiversity, and establish a model for sustainable tourism in the region.