King Abdullah University of Science and Technology

About: King Abdullah University of Science and Technology is a education organization based out in Jeddah, Saudi Arabia. It is known for research contribution in the topics: Catalysis & Membrane. The organization has 6221 authors who have published 22019 publications receiving 625706 citations. The organization is also known as: KAUST.

Side-chain tunability of furan-containing low-band-gap polymers provides control of structural order in efficient solar cells.

Abstract: The solution-processability of conjugated polymers in organic solvents has classically been achieved by modulating the size and branching of alkyl substituents appended to the backbone. However, these substituents impact structural order and charge transport properties in thin-film devices. As a result, a trade-off must be found between material solubility and insulating alkyl content. It was recently shown that the substitution of furan for thiophene in the backbone of the polymer PDPP2FT significantly improves polymer solubility, allowing for the use of shorter branched side chains while maintaining high device efficiency. In this report, we use PDPP2FT to demonstrate that linear alkyl side chains can be used to promote thin-film nanostructural order. In particular, linear side chains are shown to shorten π–π stacking distances between backbones and increase the correlation lengths of both π–π stacking and lamellar spacing, leading to a substantial increase in the efficiency of bulk heterojunction solar...

DeepGCNs: Can GCNs Go as Deep as CNNs?

Abstract: Convolutional Neural Networks (CNNs) achieve impressive performance in a wide variety of fields. Their success benefited from a massive boost when very deep CNN models were able to be reliably trained. Despite their merits, CNNs fail to properly address problems with non-Euclidean data. To overcome this challenge, Graph Convolutional Networks (GCNs) build graphs to represent non-Euclidean data, borrow concepts from CNNs, and apply them in training. GCNs show promising results, but they are usually limited to very shallow models due to the vanishing gradient problem. As a result, most state-of-the-art GCN models are no deeper than 3 or 4 layers. In this work, we present new ways to successfully train very deep GCNs. We do this by borrowing concepts from CNNs, specifically residual/dense connections and dilated convolutions, and adapting them to GCN architectures. Extensive experiments show the positive effect of these deep GCN frameworks. Finally, we use these new concepts to build a very deep 56-layer GCN, and show how it significantly boosts performance (+3.7% mIoU over state-of-the-art) in the task of point cloud semantic segmentation. We believe that the community can greatly benefit from this work, as it opens up many opportunities for advancing GCN-based research.

A Survey of Urban Reconstruction

Abstract: This paper provides a comprehensive overview of urban reconstruction. While there exists a considerable body of literature, this topic is still under active research. The work reviewed in this survey stems from the following three research communities: computer graphics, computer vision and photogrammetry and remote sensing. Our goal is to provide a survey that will help researchers to better position their own work in the context of existing solutions, and to help newcomers and practitioners in computer graphics to quickly gain an overview of this vast field. Further, we would like to bring the mentioned research communities to even more interdisciplinary work, since the reconstruction problem itself is by far not solved.