Sampling via the aggregation value for data-driven manufacturing.

Data-driven modelling has shown promising potential in many industrial applications, while the expensive and time-consuming labelling of experimental and simulation data restricts its further development. Preparing a more informative but smaller dataset to reduce labelling efforts has been a vital research problem. Although existing techniques can assess the value of individual data samples, how to represent the value of a sample set remains an open problem.

Transfer Learning Under Conditional Shift Based on Fuzzy Residual.

Transfer learning has received much attention recently and has been proven to be effective in a wide range of applications, whereas studies on regression problems are still scarce. In this article, we focus on the transfer learning problem for regression under the situations of conditional shift where the source and target domains share the same marginal distribution while having different conditional probability distributions. We propose a new framework called transfer learning based on fuzzy residual (ResTL) which learns the target model by preserving the distribution properties of the source data in a model-agnostic way.

An integrated experimental and theoretical study on the optical properties of uniform hairy noble metal nanoparticles.

We report a viable route to plasmonic nanoparticles with well-controlled sizes, shapes, and compositions. A series of monodisperse Ag and Au nanoparticles capped with polystyrene chains (i.e.