A crucial part of image classification consists capturing non-local spatial semantics content. This paper describes the multi-scale hybrid vision transformer (MSHViT), an extension classical convolutional neural network (CNN) backbone, for multi-label sewer defect classification. To better model in images, features are aggregated at different scales non-locally through use a lightweight transfo...

Federated learning has generated significant interest, with nearly all works focused on a "star" topology where nodes/devices are each connected to central server. We migrate away from this architecture and extend it through the network dimension case there multiple layers of nodes between end devices Specifically, we develop multi-stage hybrid federated (MH-FL), intra- inter-layer model that c...

The general image segmentation approach used in other domains may not be applicable to the remote sensing field, which is due to the following factors: remotely sensed data is multi-spectral, always very large in size, and in multi-scale as well. How to quickly and efficiently segment remotely sensed imagery is still a big issue to be solved. Based on human vision mechanism, a new hybrid multi-...

Hyperspectral image (HSI) classification is an important concern in remote sensing, but it complex since few numbers of labelled training samples and the high-dimensional space with many spectral bands. Hence, essential to develop a more efficient neural network architecture improve performance HSI task. Deep learning models are contemporary techniques for pixel-based hyperspectral classificati...

This paper presents a novel hybrid particle-grid method that tightly couples Lagrangian particle approach with Eulerian grid approach to simulate multi-scale diffuse materials varying from disperse droplets to dissipating spray and their natural mixture and transition, originated from a violent (high-speed) liquid stream. Despite the fact that Lagrangian particles are widely employed for repres...

Multi-scale computer simulations combine the computationally efficient classical algorithms with more expensive but also more accurate ab-initio quantum mechanical algorithms. This work describes one implementation of multi-scale computations using the Atomistic Simulation Environment (ASE). This implementation can mix classical codes like LAMMPS and the Density Functional Theory-based GPAW. An...