Researchers from MBZUAI have proposed a new taxonomy of eight temporal frames and studied their persuasive use in news discourse. They created a multilingual dataset by expertly annotating 458 English and German news articles, identifying over 2,000 temporally framed sentences and approximately 3,000 annotations. Their experiments demonstrated that temporal framing is learnable at the sentence level, with supervised models significantly outperforming zero-shot classification approaches. Why it matters: This research provides a valuable dataset and methodology for understanding how time-related language shapes interpretation in news, contributing to advancements in NLP for media analysis and potentially countering disinformation.
A cryptanalysis team at the UAE's Cryptography Research Center (CRC) has set new records in computation by decrypting a McEliece ciphertext without the secret key at INRIA’s McEliece decoding challenge, taking first and second place. The record computation took about 31.4 days on a cluster using 256 CPU-cores. The team also achieved top ranks in decoding quasi-cyclic codes and ternary codes, used in post-quantum cryptography. Why it matters: This achievement demonstrates the UAE's growing capabilities in advanced cryptography research and its contributions to the global effort to develop quantum-resistant algorithms.
TII's Secure Systems Research Center (SSRC) in Abu Dhabi has launched a motion capture (MOCAP) facility for testing drones in augmented reality, the first such facility outside the US. SSRC's MOCAP facility will simulate environments like Abu Dhabi city to enable high-precision ground truth for experiments. The facility will allow the modelling and operation of a cloud-based secure autonomous system of drones. Why it matters: This positions the UAE as a leader in drone security research, enabling advanced testing and development of secure drone systems for various critical applications.
TII's Secure Systems Research Center (SSRC) has partnered with Purdue University on a three-year cybersecurity project focused on ensuring the safe and efficient use of Unmanned Aerial Vehicles (UAVs) in urban environments. The collaboration will study security and resilience in cyber-physical and autonomous systems, addressing vulnerabilities in communication, navigation, and command and control. The project includes four phases: modeling and analysis of UAS security, developing algorithms for high-assurance autonomy, constructing an experimental environment, and testing mitigation strategies. Why it matters: The partnership enhances the UAE's capabilities in securing critical digital systems and fosters the growth of commercial autonomous drones and robots, opening new opportunities for enterprises.
This paper introduces DaringFed, a novel dynamic Bayesian persuasion pricing mechanism for online federated learning (OFL) that addresses the challenge of two-sided incomplete information (TII) regarding resources. It formulates the interaction between the server and clients as a dynamic signaling and pricing allocation problem within a Bayesian persuasion game, demonstrating the existence of a unique Bayesian persuasion Nash equilibrium. Evaluations on real and synthetic datasets demonstrate that DaringFed optimizes accuracy and convergence speed and improves the server's utility.
KAUST Ph.D. student Chiheb Ben Hammouda won the best poster award at the Society for Industrial and Applied Mathematics Conference on Financial Mathematics & Engineering (FM19) for his work on option pricing under the rough Bergomi model. The winning poster, titled "Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model," details research carried out under the supervision of KAUST Professor Raul Tempone. The research group designed new efficient numerical methods for pricing derivatives under the rough Bergomi model by combining smoothing techniques. Why it matters: This award highlights KAUST's growing expertise in financial mathematics and its contribution to solving complex problems in the field using advanced numerical methods.
KAUST Ph.D. student Jian Cao received a best paper award from the American Statistical Association (ASA) for his paper on computing high-dimensional normal and Student-t probabilities. The paper uses Tile-Low-Rank Quasi-Monte Carlo and Block Reordering. Cao, a member of Professor Marc Genton's group, will be recognized at the ASA's Joint Statistical Meetings. Why it matters: This award highlights KAUST's strength in high-performance computing and statistical research, contributing to advancements in handling complex, high-dimensional datasets.
A new neural network architecture called Orchid was introduced that uses adaptive convolutions to achieve quasilinear computational complexity O(N logN) for sequence modeling. Orchid adapts its convolution kernel dynamically based on the input sequence. Evaluations across language modeling and image classification show that Orchid outperforms attention-based architectures like BERT and Vision Transformers, often with smaller model sizes. Why it matters: Orchid extends the feasible sequence length beyond the practical limits of dense attention layers, representing progress toward more efficient and scalable deep learning models.