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.

A Rhodanine Flanked Nonfullerene Acceptor for Solution-Processed Organic Photovoltaics

Abstract: A novel small molecule, FBR, bearing 3-ethylrhodanine flanking groups was synthesized as a nonfullerene electron acceptor for solution-processed bulk heterojunction organic photovoltaics (OPV). A straightforward synthesis route was employed, offering the potential for large scale preparation of this material. Inverted OPV devices employing poly(3-hexylthiophene) (P3HT) as the donor polymer and FBR as the acceptor gave power conversion efficiencies (PCE) up to 4.1%. Transient and steady state optical spectroscopies indicated efficient, ultrafast charge generation and efficient photocurrent generation from both donor and acceptor. Ultrafast transient absorption spectroscopy was used to investigate polaron generation efficiency as well as recombination dynamics. It was determined that the P3HT:FBR blend is highly intermixed, leading to increased charge generation relative to comparative devices with P3HT:PC60BM, but also faster recombination due to a nonideal morphology in which, in contrast to P3HT:PC60BM d...

Pure Cs4PbBr6: Highly Luminescent Zero-Dimensional Perovskite Solids

Abstract: So-called zero-dimensional perovskites, such as Cs4PbBr6, promise outstanding emissive properties. However, Cs4PbBr6 is mostly prepared by melting of precursors that usually leads to a coformation of undesired phases. Here, we report a simple low-temperature solution-processed synthesis of pure Cs4PbBr6 with remarkable emission properties. We found that pure Cs4PbBr6 in solid form exhibits a 45% photoluminescence quantum yield (PLQY), in contrast to its three-dimensional counterpart, CsPbBr3, which exhibits more than 2 orders of magnitude lower PLQY. Such a PLQY of Cs4PbBr6 is significantly higher than that of other solid forms of lower-dimensional metal halide perovskite derivatives and perovskite nanocrystals. We attribute this dramatic increase in PL to the high exciton binding energy, which we estimate to be ∼353 meV, likely induced by the unique Bergerhoff–Schmitz–Dumont-type crystal structure of Cs4PbBr6, in which metal-halide-comprised octahedra are spatially confined. Our findings bring this class...

Robust Visual Tracking via Structured Multi-Task Sparse Learning

Abstract: In this paper, we formulate object tracking in a particle filter framework as a structured multi-task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT). Since we model particles as linear combinations of dictionary templates that are updated dynamically, learning the representation of each particle is considered a single task in Multi-Task Tracking (MTT). By employing popular sparsity-inducing $$\ell _{p,q}$$ mixed norms $$(\text{ specifically} p\in \{2,\infty \}$$ and $$q=1),$$ we regularize the representation problem to enforce joint sparsity and learn the particle representations together. As compared to previous methods that handle particles independently, our results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity. Interestingly, we show that the popular $$L_1$$ tracker (Mei and Ling, IEEE Trans Pattern Anal Mach Intel 33(11):2259---2272, 2011) is a special case of our MTT formulation (denoted as the $$L_{11}$$ tracker) when $$p=q=1.$$ Under the MTT framework, some of the tasks (particle representations) are often more closely related and more likely to share common relevant covariates than other tasks. Therefore, we extend the MTT framework to take into account pairwise structural correlations between particles (e.g. spatial smoothness of representation) and denote the novel framework as S-MTT. The problem of learning the regularized sparse representation in MTT and S-MTT can be solved efficiently using an Accelerated Proximal Gradient (APG) method that yields a sequence of closed form updates. As such, S-MTT and MTT are computationally attractive. We test our proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that S-MTT is much better than MTT, and both methods consistently outperform state-of-the-art trackers.

Smart surfaces with switchable superoleophilicity and superoleophobicity in aqueous media: Toward controllable oil/water separation

Abstract: Peng Wang and co-workers have devised smart surfaces that have switchable wettability – affinity to oil and water phases. Such materials are useful for applications that require oil/water separation, and might help clean up oil spills in future. The researchers have endowed materials with smart surfaces by decorating them with a polymer made of two flexible blocks with different properties. One block exhibits a high affinity to oil phases and low affinity to aqueous ones, while the other changes its wettability but also its shape through protonation, depending on the acidity of the aqueous phase. At different pHs, therefore, either one block or the other is predominantly exposed to the solution, endowing the surface with different wettability characteristics. These smart surfaces can be coated on commonly used materials such as textiles and sponges, and showed good properties for oil capture and release applications.

DAPs: Deep Action Proposals for Action Understanding

Abstract: Object proposals have contributed significantly to recent advances in object understanding in images. Inspired by the success of this approach, we introduce Deep Action Proposals (DAPs), an effective and efficient algorithm for generating temporal action proposals from long videos. We show how to take advantage of the vast capacity of deep learning models and memory cells to retrieve from untrimmed videos temporal segments, which are likely to contain actions. A comprehensive evaluation indicates that our approach outperforms previous work on a large scale action benchmark, runs at 134 FPS making it practical for large-scale scenarios, and exhibits an appealing ability to generalize, i.e. to retrieve good quality temporal proposals of actions unseen in training.