The real-world data of power networks is often inaccessible due to privacy and security concerns, highlighting the need for tools to generate realistic synthetic network data. Existing methods ...

This study proposes an important new approach to analyzing cell-count data, which are often undersampled and cannot be accurately assessed using traditional statistical methods. The case studies ...

The hierarchical flow for clock domain crossing (CDC) and reset domain crossing (RDC) is a methodology used in the verification of large, complex digital integrated circuits. It’s a divide-and-conquer ...

Some cars invite you in with chrome and comfort. The Model T invites you into a time machine, hands you three pedals that mean the wrong things, and politely asks you to learn 1910s. Then it coughs, ...

Abstract: Low-rank representations such as the Tucker decomposition underlie many frequentist methods for tensor analysis. Bayesian analogues, in contrast, have received less attention. Notably ...

The central vein (CV) region, driven by the Wnt signaling pathway, and the portal vein (PV) region, driven by the Notch signaling pathway, enable us to construct a biomimetic liver lobule ...

Singapore-based AI startup Sapient Intelligence has developed a new AI architecture that can match, and in some cases vastly outperform, large language models (LLMs) on complex reasoning tasks, all ...

Model Context Protocol, or MCP, is arguably the most powerful innovation in AI integration to date, but sadly, its purpose and potential are largely misunderstood. So what's the best way to really ...

Empowered by technological progress, sports teams and bookmakers strive to understand relationships between player and team activity and match outcomes. For this purpose, the probability of an event ...

We adapt a semi-Bayesian hierarchical modeling framework to jointly characterize the space–time variability of seasonal precipitation totals and precipitation extremes across the Northern Great Plains ...