A glider deployed by KAUST's Coastal and Marine Resources Core Lab (CMOR) completed 1,000 dives in the Red Sea. The dives, completed by November 22, 2015, are part of ongoing marine research at KAUST. The glider is maintained by Lloyd Smith, supervisor of marine electronics at CMOR. Why it matters: This demonstrates KAUST's commitment to environmental monitoring and research in the Red Sea, contributing to a better understanding of the region's unique marine ecosystem.
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.
KAUST's Coastal and Marine Resources (CMR) Core Lab has been accredited by the International Organization for Standardization (ISO) to ISO/IEC 17025. The accreditation confirms the lab's competence in performing calibrations with global quality standards. KAUST is the first university in the Kingdom and the GCC region to receive such recognition for oceanographic instrument calibration. Why it matters: This certification enhances the reliability of research data and positions KAUST as a leader in marine research infrastructure within the region.
This paper introduces Diffusion-BBO, a new online black-box optimization (BBO) framework that uses a conditional diffusion model as an inverse surrogate model. The framework employs an Uncertainty-aware Exploration (UaE) acquisition function to propose scores in the objective space for conditional sampling. The approach is shown theoretically to achieve a near-optimal solution and empirically outperforms existing online BBO baselines across 6 scientific discovery tasks.
A KAUST-led international team has published research detailing the potential of marine-based solutions to combat climate change. The study assesses the effectiveness of 13 ocean-based measures, including reducing greenhouse gas concentrations and protecting marine ecosystems. The research will inform decision-makers at COP24. Why it matters: Highlighting the potential of ocean-based solutions can broaden the scope of climate action strategies in the region, where coastal environments and marine resources are vital.
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.
KAUST launched the Circular Carbon Initiative (CCI) to address carbon management, capture, conversion, and storage of atmospheric CO2. The initiative involves developing materials and technologies to capture CO2 and exploring geothermal energy and geological storage. Novel fuel production will redefine CO2 as a valuable material through e-fuel developments. Why it matters: The CCI positions KAUST as a key player in developing sustainable technologies and contributing to Saudi Arabia's climate goals.
A new benchmark, LongShOTBench, is introduced for evaluating multimodal reasoning and tool use in long videos, featuring open-ended questions and diagnostic rubrics. The benchmark addresses the limitations of existing datasets by combining temporal length and multimodal richness, using human-validated samples. LongShOTAgent, an agentic system, is also presented for analyzing long videos, with both the benchmark and agent demonstrating the challenges faced by state-of-the-art MLLMs.