Science

New research reveals a method for proactively identifying subtle shifts in market behavior that precede significant liquidity drops in limit order books.

A new analysis of academic literature reveals the fragmented landscape of biodiversity finance and outlines a path toward more effective conservation investments.

Researchers are exploring how large language models can leverage map data and understand traffic scenes to accurately forecast the movements of vehicles.

This research explores a novel neural network approach for accurately and efficiently determining the optimal frequency parameter in hyperbolic polynomial splines.

As artificial intelligence becomes increasingly capable, the question of legal responsibility for AI-driven crime demands urgent attention.

A new approach systematically evaluates the potential safety and security risks arising from the inherent limitations of deep learning-based perception systems in autonomous vehicles.

New research assesses how effectively artificial intelligence can analyze visual content on social media to understand public discourse around climate change.

New research demonstrates how easily graph structures can be revealed even when using privacy-preserving spectral embeddings, and introduces tools to both benchmark this leakage and rebuild fragmented networks.

New research reveals that analyzing past text data with frozen language models can reveal economically relevant information missed by current market valuations.

A new framework classifies the evolving patterns of AI-related incidents to move beyond simple tracking and toward proactive risk mitigation.