Weathering the Storm: Smarter Load Forecasting for a Changing Climate
A new probabilistic forecasting framework improves power grid resilience by accurately predicting extreme energy demand, even with limited data.
Science
Scaling Up Graph Intelligence
New research details a framework for building and pre-training graph foundation models at an unprecedented scale, unlocking advanced capabilities for heterogeneous graph data.
Smarter Supply Chains: Forecasting Demand Without Sharing Data
A new approach combining federated learning and blockchain technology offers a path to more accurate retail demand forecasting and reduced food waste.
AI Designs AI: A New Dataset for Neural Network Research
Researchers are leveraging the power of large language models to automatically generate complex neural network architectures, creating a valuable resource for improving AI reliability and adaptability.
Ocean Blooms Forecast with Deep Learning
![Across oceanic basins from 2012 to 2017, a comparative analysis using Convolutional Neural Networks (CNN) and U-Nets demonstrates a strong correlation between remotely sensed chlorophyll saturation [latex]log(ChlSat)[/latex] and reconstructed chlorophyll concentrations [latex]log(Chl)[/latex], indicating the potential for accurate chlorophyll estimation via these distinct deep learning architectures.](https://arxiv.org/html/2602.04689v1/Fig2.png)
New research shows deep neural networks can accurately predict phytoplankton biomass-a key indicator of ocean health-by learning from physical ocean conditions.
Decoding Plasma Chaos: A Neural Network Approach
Researchers have developed a convolutional operator network capable of modeling and predicting the complex dynamics of plasma turbulence, opening new avenues for understanding and controlling this pervasive phenomenon.
Engineering Trust: Navigating the Risks of Generative AI
As Large Language Models become increasingly integrated into complex systems, a robust framework for identifying and mitigating potential hazards is critical.
Mapping Risk in DeFi: A New Forecasting Model
Researchers have developed a novel approach to predict credit exposures and systemic vulnerabilities within the rapidly evolving landscape of decentralized finance.
Decoding Crypto Risk: A New Index for a Volatile Market
![The aggregate measure of systemic risk, represented by the ASRI, is decomposed into its constituent parts-stablecoin risk, DeFi liquidity risk, contagion risk, and arbitrage opacity-each contributing proportionally to the overall stress, and shifts in these relative contributions during crisis periods illuminate the dominant channels of systemic transmission, adhering to the decomposition property where [latex] \sum_{i} w_{i} S_{i} [/latex] equals the total ASRI at any given time.](https://arxiv.org/html/2602.03874v1/x1.png)
Researchers have developed a comprehensive index to measure systemic risk within the rapidly evolving cryptocurrency landscape.
Decoding Preferences with AI: A New Approach to Economic Measurement
Researchers are leveraging the power of artificial intelligence to better understand economic choices by analyzing the underlying reasoning revealed in survey responses.