Abstract: Domain adaptation (DA) models are widely used in the fault diagnosis of rotating machines under variable operating conditions, in which most of the existing models assume the same number of ...

In large public multi-site fMRI datasets, the sample characteristics, data acquisition methods, and MRI scanner models vary across sites and datasets. This non-neural variability obscures neural ...

Abstract: Model-based reinforcement learning (RL), which learns an environment model from the offline dataset and generates more out-of-distribution model data, has become an effective approach to the ...

FastDomainCheck MCP Server is a Model Context Protocol (MCP) server implementation that enables secure, two-way connections between AI tools (like Claude) and domain availability data. By following ...

Generating time series data is important for many applications, including data augmentation, synthetic datasets, and scenarios. However, when there is more than one, this process becomes too complex ...

OpenRFT is an open-source project that aims to adapt generalist reasoning foundation models to domain-specific tasks through Reinforcement Fine-Tuning (RFT). By leveraging domain-specific samples, ...

The Tesla Model Y has been the most popular electric car for a few years now, and it makes sense. The Model Y is reasonably priced for an EV while offering a good range and an excellent software ...

The study presents valuable findings where two-domain thermodynamic model for TetR accurately predicts in vivo phenotype changes brought about as a result of various mutations. The evidence provided ...

Understand the pitfalls of using enumeration types in the domain layer of your .NET applications and the advantages of using record types instead. When working on applications, you will often need to ...