In nonlinear dynamical systems, tipping refers to a critical transition from one steady state to another, typically catastrophic, steady state, often resulting from a saddle-node bifurcation. Recently, the machine-learning framework of parameter-adaptable reservoir computing has been applied to predict tipping in systems described by low-dimensional stochastic differential equations. However, anticipating tipping in complex spatiotemporal dynamical systems remains a significant open problem. The ability to forecast not only the occurrence but also the precise timing of such tipping events […]

Ver mais

Not long ago, “life hacks” meant simple tricks—like using keyboard shortcuts, keeping a to-do list, or waking up earlier to get more done. But today, the concept of life hacking is evolving. Artificial intelligence and smart digital tools are changing how we manage work, time, and information. Instead of trying to do everything ourselves, we can now use technology to handle repetitive tasks and speed up everyday processes. The result isn’t just higher productivity—it’s more time to focus […]

Ver mais

We investigate document page ordering on 5,461 shuffled WOO documents (Dutch freedom of information releases) using page embeddings. These documents are heterogeneous collections such as emails, legal texts, and spreadsheets compiled into single PDFs, where semantic ordering signals are unreliable. We compare five methods, including pointer networks, seq2seq transformers, and specialized pairwise ranking models. The best performing approach successfully reorders documents up to 15 pages, with Kendall’s tau ranging from 0.95 for short documents (2-5 pages) to 0.72 […]

Ver mais

The conflict between Anthropic and Washington shows how artificial intelligence intersects with geopolitics. It began as a principled refusal to support military applications and has now evolved into a bigger question: Who controls these advanced AI systems? The companies building them or the government wanting to deploy them? This is a rare and strategic opportunity for the United Kingdom. Britain is portraying itself as a more reliable and aligned partner, one that may offer regular flexibility without any […]

Ver mais

arXiv:2602.00866v1 Announce Type: new Abstract: The Controller Area Network (CAN) bus provides a rich source of vehicular signals increasingly leveraged for applications in automotive and auto insurance domains, including collision detection, predictive maintenance, and driver risk modeling. Despite this potential, existing pipelines largely train isolated task-specific models on raw CAN data, with only limited efforts exploring decoded signals. Such fragmentation prevents shared representation learning and limits cross-task generalization. By contrast, natural language processing (NLP) and computer vision (CV) […]

Ver mais

arXiv:2602.15162v1 Announce Type: new Abstract: Mobile robots operating in agroindustrial environments, such as Mediterranean greenhouses, are subject to challenging conditions, including uneven terrain, variable friction, payload changes, and terrain slopes, all of which significantly affect control performance and stability. Despite the increasing adoption of robotic platforms in agriculture, the lack of standardized, reproducible benchmarks impedes fair comparisons and systematic evaluations of control strategies under realistic operating conditions. This paper presents a comprehensive benchmarking framework for evaluating mobile robot […]

Ver mais

arXiv:2512.10032v2 Announce Type: replace-cross Abstract: Finding cause-effect relationships is of key importance in science. Causal discovery aims to recover a graph from data that succinctly describes these cause-effect relationships. However, current methods face several challenges, especially when dealing with high-dimensional data and complex dependencies. Incorporating prior knowledge about the system can aid causal discovery. In this work, we leverage Cluster-DAGs as a prior knowledge framework to warm-start causal discovery. We show that Cluster-DAGs offer greater flexibility than existing […]

Ver mais

Real-world scientific applications frequently encounter incomplete observational data due to sensor limitations, geographic constraints, or measurement costs. Although neural operators significantly advanced PDE solving in terms of computational efficiency and accuracy, their underlying assumption of fully-observed spatial inputs severely restricts applicability in real-world applications. We introduce the first systematic framework for learning neural operators from partial observation. We identify and formalize two fundamental obstacles: (i) the supervision gap in unobserved regions that prevents effective learning of physical correlations, […]

Ver mais