Science

Researchers propose a stable and consistent framework for quintom dark energy, potentially resolving long-standing theoretical issues with existing models.

A new framework coordinates multiple unmanned aerial vehicles, ensuring stable and scalable path following in time-critical scenarios.

A new logical framework combines probabilistic reasoning with nuanced evaluations of certainty, enabling more sophisticated knowledge representation and reasoning.

New research reveals that the way AI systems are instructed can create critical vulnerabilities, potentially turning email defenses into attack vectors.

A novel framework, Robust Principal Component Completion, efficiently separates meaningful data from obscuring noise by modeling sparsity and low-rank structure.

A novel neural network model, constrained by both lattice QCD and hadron resonance gas data, provides a refined picture of the QCD phase transition.

A new framework leverages functorial embeddings to systematically classify solutions across six related subproblems of the Four Subspace Problem.

A new framework demonstrates that predictable, verifiable AI isn’t just desirable-it’s achievable through fundamental changes to how machine learning systems are built.

A new framework combines dynamic trading with static hedging to effectively manage covariance risk in incomplete markets.

A new review delves into the construction of holographic dualities in non-relativistic string theory, revealing connections between gravity, quantum mechanics, and the search for simplified models of complex systems.