Nanjing University of Science and Technology

About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.

Single Image Dehazing via Multi-scale Convolutional Neural Networks with Holistic Edges

Abstract: Single image dehazing has been a challenging problem which aims to recover clear images from hazy ones. The performance of existing image dehazing methods is limited by hand-designed features and priors. In this paper, we propose a multi-scale deep neural network for single image dehazing by learning the mapping between hazy images and their transmission maps. The proposed algorithm consists of a coarse-scale net which predicts a holistic transmission map based on the entire image, and a fine-scale net which refines dehazed results locally. To train the multi-scale deep network, we synthesize a dataset comprised of hazy images and corresponding transmission maps based on the NYU Depth dataset. In addition, we propose a holistic edge guided network to refine edges of the estimated transmission map. Extensive experiments demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods on both synthetic and real-world images in terms of quality and speed.

Metal–Organic Frameworks and Their Derived Materials: Emerging Catalysts for a Sulfate Radicals-Based Advanced Oxidation Process in Water Purification

Abstract: With the ever-growing environmental issues, sulfate radical (SO )-based advanced oxidation processes (SR-AOPs) have been attracting widespread attention due to their high selectivity and oxidative potential in water purification. Among various methods generating SO , employing heterogeneous catalysts for activation of peroxymonosulfate or persulfate has been demonstrated as an effective strategy. Therefore, the future advances of SR-AOPs depend on the development of adequate catalysts with high activity and stability. Metal-organic frameworks (MOFs) with large surface area, ultrahigh porosity, and diversity of material design have been extensively used in heterogeneous catalysts, and more recently, enormous effort has been made to utilize MOFs-based materials for SR-AOPs applications. In this work, the state-of-the-art research on pristine MOFs, MOFs composites, and their derivatives, such as oxides, metal/carbon hybrids, and carbon materials for SR-AOPs, is summarized. The mechanisms, including radical and nonradical pathways, are also detailed in the discussion. This work will hopefully promote the future development of MOFs-based materials toward SR-AOPs applications.

Federated Learning for Internet of Things: A Comprehensive Survey

Abstract: The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may not be feasible in realistic application scenarios due to the high scalability of modern IoT networks and growing data privacy concerns. Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need for data sharing. In this article, we provide a comprehensive survey of the emerging applications of FL in IoT networks, beginning from an introduction to the recent advances in FL and IoT to a discussion of their integration. Particularly, we explore and analyze the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing, and IoT privacy and security. We then provide an extensive survey of the use of FL in various key IoT applications such as smart healthcare, smart transportation, Unmanned Aerial Vehicles (UAVs), smart cities, and smart industry. The important lessons learned from this review of the FL-IoT services and applications are also highlighted. We complete this survey by highlighting the current challenges and possible directions for future research in this booming area.

Nonlinear Saturable Absorption of Liquid-Exfoliated Molybdenum/Tungsten Ditelluride Nanosheets

Abstract: Molybdenum disulfide (MoS2 ) and tungsten disulfide (WS2 ), two representative transition metal dichalcogenide materials, have captured tremendous interest for their unique electronic, optical, and chemical properties. Compared with MoS2 and WS2 , molybdenum ditelluride (MoTe2 ) and tungsten ditelluride (WTe2 ) possess similar lattice structures while having smaller bandgaps (less than 1 eV), which is particularly interesting for applications in the near-infrared wavelength regime. Here, few-layer MoTe2 /WTe2 nanosheets are fabricated by a liquid exfoliation method using sodium deoxycholate bile salt as surfactant, and the nonlinear optical properties of the nanosheets are investigated. The results demonstrate that MoTe2 /WTe2 nanosheets exhibit nonlinear saturable absorption property at 1.55 μm. Soliton mode-locking operations are realized separately in erbium-doped fiber lasers utilizing two types of MoTe2 /WTe2 -based saturable absorbers, one of which is prepared by depositing the nanosheets on side polished fibers, while the other is fabricated by mixing the nanosheets with polyvinyl alcohol and then evaporating them on substrates. Numerous applications may benefit from the nonlinear saturable absorption features of MoTe2 /WTe2 nanosheets, such as visible/near-infrared pulsed laser, materials processing, optical sensors, and modulators.

Outer product-based neural collaborative filtering

Abstract: In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering. The idea is to use an outer product to explicitly model the pairwise correlations between the dimensions of the embedding space. In contrast to existing neural recommender models that combine user embedding and item embedding via a simple concatenation or element-wise product, our proposal of using outer product above the embedding layer results in a two-dimensional interaction map that is more expressive and semantically plausible. Above the interaction map obtained by outer product, we propose to employ a convolutional neural network to learn high-order correlations among embedding dimensions. Extensive experiments on two public implicit feedback data demonstrate the effectiveness of our proposed ONCF framework, in particular, the positive effect of using outer product to model the correlations between embedding dimensions in the low level of multi-layer neural recommender model.