arXiv ·
Researchers introduce TomFormer, a transformer-based model for accurate and early detection of tomato leaf diseases, with the goal of deployment on the Hello Stretch robot for real-time diagnosis. TomFormer combines a visual transformer and CNN, achieving state-of-the-art results on KUTomaDATA, PlantDoc, and PlantVillage datasets. KUTomaDATA was collected from a greenhouse in Abu Dhabi, UAE.
arXiv ·
This paper introduces a convolutional transformer model for classifying tomato maturity, along with a new UAE-sourced dataset, KUTomaData, for training segmentation and classification models. The model combines CNNs and transformers and was tested against two public datasets. Results showed state-of-the-art performance, outperforming existing methods by significant margins in mAP scores across all three datasets.
KAUST ·
Christopher Fabian, co-founder of UNICEF’s Innovation Unit, spoke at KAUST about using data and technology to improve lives. He highlighted how IoT and wearables can connect remote populations in developing countries with their governments. The talk emphasized using data to include unaccounted populations. Why it matters: The discussion reinforces KAUST's commitment to leveraging technology for global development and aligns with Saudi Arabia's broader goals for digital transformation.
Kuwait AI ·
The Korea Institute of Science and Technology Information (KISTI) has signed MOUs with the Kuwait Institute for Scientific Research (KISR) and Kuwait University (KU) to cooperate in AI and data science. The partnerships aim to enhance research capabilities and promote technological innovation in both countries through joint projects and knowledge sharing. KISTI will collaborate with KISR on AI-based smart environmental monitoring and with KU on developing data analysis and AI technologies. Why it matters: These partnerships can accelerate the development and adoption of AI technologies in Kuwait and foster collaboration between Korean and Kuwaiti researchers.
KAUST ·
KAUST held a research workshop on Optimization and Big Data, gathering researchers to discuss challenges and opportunities in the field. Speakers presented novel optimization algorithms and distributed systems for handling large datasets. The workshop featured 20 speakers from KAUST, global universities, and Microsoft Research. Why it matters: The event highlights KAUST's role as a regional hub for advancing research and development in big data and optimization, crucial for AI and various computational fields.
KAUST ·
KAUST's Visualization Core Lab (KVL) has released inshimtu, a pseudo in situ visualization system for scientists working with large datasets and supercomputer simulations. Inshimtu simplifies the implementation of in situ visualization by using existing simulation output files without requiring changes to the simulation code. It helps scientists determine if implementing a full in situ visualization into their code is worthwhile. Why it matters: This open-source tool can improve the efficiency of supercomputing research in the region by allowing researchers to assess the value of in situ visualization before fully committing to it.
KAUST ·
KAUST Professor Raul Tempone, an expert in Uncertainty Quantification (UQ), has been appointed as an Alexander von Humboldt Professor at RWTH Aachen University in Germany. This professorship will enable him to further his research on mathematics for uncertainty quantification with new collaborators. Tempone believes the KAUST Strategic Initiative for Uncertainty Quantification (SRI-UQ) contributed to this award. Why it matters: This appointment enhances KAUST's visibility and facilitates cross-fertilization between European and KAUST research groups, benefiting both institutions and attracting talent.