The spatial prepositions in and on apply to a wide range of containment and support relations, making exhaustive definitions difficult. Theories differ in whether they endorse geometric or functional properties and how these properties are related to meaning and use. This study directly examines the roles of geometric and functional information in adults’ and children’s use of in and on by deve...

Features are important to maintain geometric and non-geometric relations across product models. The generic feature model provides flexibility and finer-level of granularity for an integrated feature-based modelling framework between CAD/CAM applications. Unfortunately, often during data translation some useful geometric and non-geometric information such as features and their associated semant...

Machine learning techniques have been recently applied in predicting deformation amorphous materials. In this study, we extract structural features around liquid film vertices from images of flowing 2D foam and apply a multilayer perceptron to predict local yielding. We evaluate their importance the description T1 events show that high level predictability may be achieved using well-chosen comb...

Background: 3D object detection based on point clouds in road scenes has attracted much attention recently. The voxel-based methods voxelize the scene to regular grids, which can be processed with advanced feature learning frameworks convolutional layers for semantic learning. point-based extract geometric of due coordinate reservations. combination two is effective ...

Monte Carlo ray tracing (MCRT) simulations are the most reliable non-experimental means for predicting radiative properties of randomly packed structures or porous media, particularly in polydisperse heterogenous environments. These methods can approximate a void region filled with homogeneous solids by averaging across large pool independent random simulations. However, due to stringent precis...