KAUST researchers contributed to an international collaboration demonstrating that an ionic salt molecule called CPMAC enhances perovskite solar cell performance by 0.6%. CPMAC improves the electronic properties and reduces defects in the electron transfer layer compared to C60. CPMAC solar cells also exhibited greater stability, with a one-third reduction in power conversion efficiency drop compared to C60 cells under heat and humidity. Why it matters: This advancement addresses a key limitation in perovskite solar cell stability, potentially leading to more efficient and durable renewable energy solutions.
Malaria No More, the Crown Prince Court of Abu Dhabi, and the Reaching the Last Mile program launched the Institute for Malaria and Climate Solutions (IMACS) to combat malaria amidst climate change. Mohamed Bin Zayed University for Artificial Intelligence (MBZUAI) joined as a technical partner, providing research support leveraging AI and data science. The initiative aims to develop and implement AI-driven strategies to address the impact of climate change on malaria transmission. Why it matters: This partnership highlights the UAE's commitment to using AI for global health challenges, particularly in combating climate-sensitive diseases like malaria.
Conor McMenamin from Universitat Pompeu Fabra presented a seminar on State Machine Replication (SMR) without honest participants. The talk covered the limitations of current SMR protocols and introduced the ByRa model, a framework for player characterization free of honest participants. He then described FAIRSICAL, a sandbox SMR protocol, and discussed how the ideas could be extended to real-world protocols, with a focus on blockchains and cryptocurrencies. Why it matters: This research on SMR protocols and their incentive compatibility could lead to more robust and secure blockchain technologies in the region.
Marcus Engsig from DERC will present a paper at the MATLAB User Group Meeting in Abu Dhabi on October 6. The paper, titled ‘Generalization of Higher Order Methods For Fast Iterative Matrix Inversion Compatible With GPU Acceleration’, discusses a novel approach to matrix inversion using GPUs. The method, named Nested Neumann, achieves 4-100x acceleration compared to standard MATLAB methods for large matrices. Why it matters: This research contributes to faster computation in numerical and physical modeling, crucial for processing large datasets in various scientific and engineering applications in the region.
MBZUAI Associate Professor Martin Takáč is working on high-performance computing and machine learning with applications in logistics, supply chain management, and other areas. His research focuses on using AI to improve precision and efficiency in tasks like predicting demand and optimizing delivery routes. Takáč's interests include imitative learning, predictive modeling, and reinforcement learning to enable AI to mimic human behavior and predict future outcomes. Why it matters: This research contributes to the development of more efficient and reliable AI systems that can be applied to a wide range of industries in the UAE and beyond.
Dezhen Song from Texas A&M University presented a talk on Co-Modality Active sensing and Perception (C-MAP) for robotics, covering sensor fusion for autonomous vehicles, augmented reality, and remote environmental monitoring. The talk highlighted lessons learned in sensor fusion using autonomous motorcycles and NASA Robonaut as examples. Recent works in robotic remote environment monitoring, especially focused on subsurface surface void and pipeline mapping were discussed. Why it matters: This research explores sensor fusion techniques to enhance robot perception, which could improve the robustness and capabilities of autonomous systems developed and deployed in the Middle East, particularly in challenging environments.
A research paper by Dr. Reda Alami of the AI and Digital Science Research Center (AIDRC) at TII has been accepted for publication at the 14th Asian Conference on Machine Learning (ACML 2022). The paper addresses sequential decision-making under uncertainty in non-stationary environments, proposing a Bayesian Change-Point Detection with Thompson Sampling (Bayesian-CPD-TS) algorithm. The algorithm combines decision-making under uncertainty and sequential detection of abrupt changes. Why it matters: This recognition highlights the growing AI research capabilities within the UAE and its contribution to the global machine learning community.
This paper introduces rational counterfactuals, a method for identifying counterfactuals that maximize the attainment of a desired consequent. The approach aims to identify the antecedent that leads to a specific outcome for rational decision-making. The theory is applied to identify variable values that contribute to peace, such as Allies, Contingency, Distance, Major Power, Capability, Democracy, and Economic Interdependency. Why it matters: The research provides a framework for analyzing and promoting conditions conducive to peace using counterfactual reasoning.