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: Membrane & Catalysis. The organization has 6221 authors who have published 22019 publications receiving 625706 citations. The organization is also known as: KAUST.

MXene hydrogels: fundamentals and applications.

Abstract: Hydrogels have recently garnered tremendous interest due to their potential application in soft electronics, human-machine interfaces, sensors, actuators, and flexible energy storage. Benefiting from their impressive combination of hydrophilicity, metallic conductivity, high aspect ratio morphology, and widely tuneable properties, when two-dimensional (2D) transition metal carbides/nitrides (MXenes) are incorporated into hydrogel systems, they offer exciting and versatile platforms for the design of MXene-based soft materials with tunable application-specific properties. The intriguing and, in some cases, unique properties of MXene hydrogels are governed by complex gel structures and gelation mechanisms, which require in-depth investigation and engineering at the nanoscale. On the other hand, the formulation of MXenes into hydrogels can significantly increase the stability of MXenes, which is often the limiting factor for many MXene-based applications. Moreover, through simple treatments, derivatives of MXene hydrogels, such as aerogels, can be obtained, further expanding their versatility. This tutorial review intends to show the enormous potential of MXene hydrogels in expanding the application range of both hydrogels and MXenes, as well as increasing the performance of MXene-based devices. We elucidate the existing structures of various MXene-containing hydrogel systems along with their gelation mechanisms and the interconnecting driving forces. We then discuss their distinctive properties stemming from the integration of MXenes into hydrogels, which have revealed an enhanced performance, compared to either MXenes or hydrogels alone, in many applications (energy storage/harvesting, biomedicine, catalysis, electromagnetic interference shielding, and sensing).

SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network

Abstract: Object detection is a fundamental and important problem in computer vision. Although impressive results have been achieved on large/medium sized objects in large-scale detection benchmarks (e.g. the COCO dataset), the performance on small objects is far from satisfactory. The reason is that small objects lack sufficient detailed appearance information, which can distinguish them from the background or similar objects. To deal with the small object detection problem, we propose an end-to-end multi-task generative adversarial network (MTGAN). In the MTGAN, the generator is a super-resolution network, which can up-sample small blurred images into fine-scale ones and recover detailed information for more accurate detection. The discriminator is a multi-task network, which describes each super-resolved image patch with a real/fake score, object category scores, and bounding box regression offsets. Furthermore, to make the generator recover more details for easier detection, the classification and regression losses in the discriminator are back-propagated into the generator during training. Extensive experiments on the challenging COCO dataset demonstrate the effectiveness of the proposed method in restoring a clear super-resolved image from a blurred small one, and show that the detection performance, especially for small sized objects, improves over state-of-the-art methods.

Low-rank sparse learning for robust visual tracking

Abstract: In this paper, we propose a new particle-filter based tracking algorithm that exploits the relationship between particles (candidate targets). By representing particles as sparse linear combinations of dictionary templates, this algorithm capitalizes on the inherent low-rank structure of particle representations that are learned jointly. As such, it casts the tracking problem as a low-rank matrix learning problem. This low-rank sparse tracker (LRST) has a number of attractive properties. (1) Since LRST adaptively updates dictionary templates, it can handle significant changes in appearance due to variations in illumination, pose, scale, etc. (2) The linear representation in LRST explicitly incorporates background templates in the dictionary and a sparse error term, which enables LRST to address the tracking drift problem and to be robust against occlusion respectively. (3) LRST is computationally attractive, since the low-rank learning problem can be efficiently solved as a sequence of closed form update operations, which yield a time complexity that is linear in the number of particles and the template size. We evaluate the performance of LRST by applying it to a set of challenging video sequences and comparing it to 6 popular tracking methods. Our experiments show that by representing particles jointly, LRST not only outperforms the state-of-the-art in tracking accuracy but also significantly improves the time complexity of methods that use a similar sparse linear representation model for particles [1].

A self-cleaning underwater superoleophobic mesh for oil-water separation

Abstract: Oil–water separation has recently become a global challenging task because of the frequent occurrence of oil spill accidents due to the offshore oil production and transportation, and there is an increasing demand for the development of effective and inexpensive approaches for the cleaning-up of the oily pollution in water system. In this study, a self-cleaning underwater superoleophobic mesh that can be used for oil-water separation is prepared by the layer-by-layer (LbL) assembly of sodium silicate and TiO2 nanoparticles on the stainless steel mesh. The integration of the self-cleaning property into the all-inorganic separation mesh by using TiO2 enables the convenient removal of the contaminants by ultraviolet (UV) illumination, and allows for the facile recovery of the separation ability of the contaminated mesh, making it promising for practial oil-water separation applications.