This paper presents a reinforcement learning framework for optimizing energy pricing in peer-to-peer (P2P) energy systems. The framework aims to maximize the profit of all components in a microgrid, including consumers, prosumers, the service provider, and a community battery. Experimental results on the Pymgrid dataset demonstrate the approach's effectiveness in price optimization, considering the interests of different components and the impact of community battery capacity.
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.
Xi Chen from NYU Stern gave a talk at MBZUAI on digital privacy in personalized pricing using differential privacy. The talk also covered research in Web3 and decentralized finance, including delta hedging liquidity positions on Uniswap V3. Chen highlighted open problems in decentralized finance during the presentation. Why it matters: The talk suggests MBZUAI's interest in exploring the intersection of AI, privacy, and blockchain technologies, reflecting growing trends in data protection and decentralized systems.
The article discusses Sri Lanka's initiative to utilize Artificial Intelligence to modify airfare pricing on key routes. This move aims to optimize ticket costs and potentially enhance the competitiveness of the national airline or the overall travel sector. No specific AI models, companies, or timelines are detailed in the provided title. Why it matters: This news is outside the scope of Middle East AI developments.
The paper introduces Sparse-Quantized Representation (SpQR), a new compression format and quantization technique for large language models (LLMs). SpQR identifies outlier weights and stores them in higher precision while compressing the remaining weights to 3-4 bits. The method achieves less than 1% accuracy loss in perplexity for LLaMA and Falcon LLMs and enables a 33B parameter LLM to run on a single 24GB consumer GPU. Why it matters: This enables near-lossless compression of LLMs, making powerful models accessible on resource-constrained devices and accelerating inference without significant accuracy degradation.
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.
Insyab, a startup specializing in collaborative robotics and drone solutions, was founded by KAUST alumnus Dr. Ahmed Bader and KAUST Professor Mohamed-Slim Alouini. Their flagship product, AirFabric™, is a broadband ultra-low-latency wireless connectivity solution enabling teams of unmanned vehicles to collaborate effectively. The technology allows robots to interact in real time and share learning, unlocking a "1+1=3" value proposition. Why it matters: This highlights KAUST's role in fostering deep-tech entrepreneurship and developing innovative solutions for industrial automation in the region.
KAUST startup Lihytech has raised US$6 million in funding from Ma'aden and the KAUST Innovation Ventures Fund. Lihytech's patented membrane technology, developed by Professor Zhiping Lai at KAUST, extracts battery-grade lithium from sources like seawater. The funding will be used to build a pilot facility at KAUST to extract lithium from the Red Sea and other in-Kingdom resources. Why it matters: This investment supports Saudi Arabia's goal of developing a complete electric vehicle value chain and becoming a key player in meeting global lithium demand.