Nadine El Naggar from City, University of London presented research on RNN learning of counting behavior, formalizing it as Dyck-1 acceptance. Empirically, RNN models struggle to learn exact counting and fail on longer sequences, even when weights are correctly initialized. Theoretically, Counter Indicator Conditions (CICs) were proposed and proven necessary/sufficient for exact counting in single-cell RNNs, but experiments show these CICs are not found or are unlearned during training. Why it matters: This work highlights challenges in RNNs learning systematic tasks, suggesting gradient descent-based optimization may not achieve exact counting behavior with standard setups.
Researchers in Saudi Arabia have developed a deep learning framework for automated counting and geolocation of palm trees using aerial images. The system uses a Faster R-CNN model trained on a dataset of 10,000 palm tree instances collected in the Kharj region using DJI drones. Geolocation accuracy of 2.8m was achieved using geotagged metadata and photogrammetry techniques.
A global census, with KAUST participation, assessed reef shark populations using Baited Remote Underwater Video Systems (BRUVS). The study found reef shark populations thrive where there are marine conservation policies and fishing regulations. However, they are scarce in areas with overfishing and poor resource protections, with sharks absent on nearly 20% of surveyed reefs. Why it matters: The research highlights the importance of conservation management for reef sharks, key apex predators and indicators of reef health, especially in the Red Sea region.
KAUST researchers developed a statistical approach to improve the identification of cancer-related protein mutations by reducing false positives. The method uses Bayesian statistics to analyze protein domain data from tumor samples, accounting for potential errors due to limited data. The team tested their method on prostate cancer data, successfully identifying a known cancer-linked mutation in the DNA binding protein cd00083. Why it matters: This enhances the reliability of cancer research at the molecular level, potentially accelerating the discovery of new therapeutic targets.
Former KAUST President Professor Choon Fong Shih was presented with the Graduate School of Arts and Sciences (GSAS) Centennial Medal by Harvard University in May. Shih received his Ph.D. in applied mathematics from Harvard in 1973 and was recognized for his contributions to knowledge and society. He served as the founding president of KAUST from 2008 and previously held positions at the National University of Singapore and GE Corporate Research Lab. Why it matters: The award recognizes the impact of a key figure in KAUST's early development and highlights the university's connection to globally recognized researchers and institutions.
Todd Nims, a filmmaker born in Saudi Arabia, premiered his film "Joud" at KAUST's 2018 Winter Enrichment Program. The film, set in Saudi Arabia, explores the cycle of life in reverse and the meaning of "Joud" (generosity in the face of scarcity). Nims describes Saudi Arabia as a "magical place" due to its rich storytelling tradition. Why it matters: The article highlights KAUST's role in showcasing cultural works and supporting Saudi artists, though the AI relevance is limited.
A KAUST team led by Hossein Fariborzi won second place in the MEMS Design Contest for their "MEMS Resonator for Oscillator, Tunable Filter and Re-Programmable Logic Applications." The device is runtime-reprogrammable, allowing the function of each device in the circuit to be changed during operation. The KAUST team demonstrated that two MEMS resonators could replace over 20 transistors in applications like digital adders, reducing digital circuit complexity. Why it matters: This innovation could significantly reduce power consumption, chip area, and manufacturing costs in microprocessors, advancing the development of energy-efficient microcomputers in the region.