The Technology Innovation Institute's (TII) Cryptography Research Center (CRC) has launched CLAASP, a cryptographic library for the automated analysis of symmetric primitives. CLAASP, built on SageMath and Python3, automates the design analysis of block ciphers, cryptographic permutations, hash functions, and stream ciphers. Released as an open-source tool with a GPLv3 license, CLAASP aims to ensure design sovereignty for organizations creating symmetric ciphers. Why it matters: This tool provides an important resource for the region to strengthen its cryptographic capabilities and contribute to global efforts in safeguarding digital infrastructure against evolving threats, including quantum computing.
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 researchers have identified a gene, CLAMT1b, in pearl millet that affects its vulnerability to the parasitic weed Striga hermonthica. Pearl millet strains lacking CLAMT1b were found to be resistant to the weed, while those expressing the gene were susceptible. The gene's presence leads to the secretion of strigolactones, promoting interaction with Striga, but its absence does not harm symbiotic relationships with beneficial fungi. Why it matters: This discovery offers new breeding strategies to enhance pearl millet's resistance to parasitic weeds, bolstering food security in arid regions like Saudi Arabia and Africa where the crop is vital.
Professor Arnab Pain's group at KAUST discovered new insights on how a malaria protein enables parasites to spread malaria in human cells. Professor Haavard Rue's group upgraded the Integrated and Nested Laplace Approximation (INLA) for faster real-time modeling of large datasets. A KAUST-led study examined the stability of Y-series nonfullerene acceptors for organic solar cells. Why it matters: KAUST continues producing impactful research across diverse fields from medicine to climate change, advancing scientific knowledge and potential applications.
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.
The Technology Innovation Institute (TII) in Abu Dhabi has launched a 2-micrometer high-power fiber laser for medical and industrial applications. Developed by TII's Directed Energy Research Center, the Thulium-based laser is efficient, compact, and scalable, enabling precise interaction with water-rich materials. TII has partnered with LIMA Photonics, a German MedTech startup, to integrate the laser into clinical solutions, including urinary stone treatment and prostate surgery. Why it matters: This laser technology and partnership showcase the UAE's commitment to translating advanced research into healthcare solutions, positioning Abu Dhabi as a hub for medical technology innovation.
Chuan Xia, a Ph.D. student at KAUST, won the best poster award at the International Conference on Materials for Advanced Technologies (ICMAT) 2017. The poster's topic is not specified in the provided text. Why it matters: Recognition at ICMAT highlights KAUST's contributions to materials science and engineering.
The paper introduces AlcLaM, an Arabic dialectal language model trained on 3.4M sentences from social media. AlcLaM expands the vocabulary and retrains a BERT-based model, using only 13GB of dialectal text. Despite the smaller training data, AlcLaM outperforms models like CAMeL, MARBERT, and ArBERT on various Arabic NLP tasks. Why it matters: AlcLaM offers a more efficient and accurate approach to Arabic NLP by focusing on dialectal Arabic, which is often underrepresented in existing models.