DarkVisionNet: Low-Light Imaging via RGB-NIR Fusion with Deep Inconsistency Prior

RGB-NIR fusion is a promising method for low-light imaging. However, high-intensity noise in low-light images amplifies the effect of structure inconsistency between RGB-NIR images, which fails existing algorithms. To handle this, we propose a new RGB-NIR fusion algorithm called Dark Vision Net (DVN) with two technical novelties: Deep Structure and Deep Inconsistency Prior (DIP). The Deep Structure extracts clear structure details in deep multiscale feature space rather than raw input space, which is more robust to noisy inputs. Based on the deep structures from both RGB and NIR domains, we introduce the DIP to leverage the structure inconsistency to guide the fusion of RGB-NIR. Benefiting from this, the proposed DVN obtains high-quality lowlight images without the visual artifacts. We also propose a new dataset called Dark Vision Dataset (DVD), consisting of aligned RGB-NIR image pairs, as the first public RGBNIR fusion benchmark. Quantitative and qualitative results on the proposed benchmark show that DVN significantly outperforms other comparison algorithms in PSNR and SSIM, especially in extremely low light conditions.Comment: Accepted to AAAI 202

Electron interaction-driven insulating ground state in Bi2Se3 topological insulators in the two dimensional limit

We report a transport study of ultrathin Bi2Se3 topological insulators with thickness from one quintuple layer to six quintuple layers grown by molecular beam epitaxy. At low temperatures, the film resistance increases logarithmically with decreasing temperature, revealing an insulating ground state. The sharp increase of resistance with magnetic field, however, indicates the existence of weak antilocalization, which should reduce the resistance as temperature decreases. We show that these apparently contradictory behaviors can be understood by considering the electron interaction effect, which plays a crucial role in determining the electronic ground state of topological insulators in the two dimensional limit.Comment: 4 figure

Activation of Toll-Like Receptor 3 Impairs the Dengue Virus Serotype 2 Replication through Induction of IFN-β in Cultured Hepatoma Cells

Toll-like receptors (TLRs) play an important role in innate immunity against invading pathogens. Although TLR signaling has been indicated to protect cells from infection of several viruses, the role of TLRs in Dengue virus (DENV) replication is still unclear. In the present study, we examined the replication of DENV serotype 2 (DENV2) by challenging hepatoma cells HepG2 with different TLR ligands. Activation of TLR3 showed an antiviral effect, while pretreatment of other TLR ligands (including TLR1/2, TLR2/6, TLR4, TLR5 or TLR7/8) did not show a significant effect. TLR3 ligand poly(I∶C) treatment prior to viral infection or simultaneously, but not post-treatment, significantly down-regulated virus replication. Pretreatment with poly(I∶C) reduced viral mRNA expression and viral staining positive cells, accompanying an induction of the type I interferon (IFN-β) and type III IFN (IL-28A/B). Intriguingly, neutralization of IFN-β alone successfully restored the poly(I∶C)-inhibited replication of DENV2. The poly(I∶C)-mediated effects, including IFN induction and DENV2 suppression, were significantly reversed by IKK inhibitor, further suggesting that IFN-β is the dominant factor involved in the poly(I∶C) mediated antiviral effect. Our study presented the first evidence to show that activation of TLR3 is effective in blocking DENV2 replication via IFN-β, providing an experimental clue that poly(I∶C) may be a promising immunomodulatory agent against DENV infection and might be applicable for clinical prevention

An investigation into customer perception and behaviour through social media research – an empirical study of the United Airline overbooking crisis

Airlines have been adopting yield management to optimise the perishable seat control problem and overbooking is a common strategy. This study outlines the connections between yield management, crises, and crisis communication. Using big data captured on a social media platform, this study aims to combine traditional yield management with emerging social big data analytics. As part of this, we use the twitter data on the 2017 United Airline (UA) to analyse the overbooking crisis. Our findings shed light on the importance of a more effective orchestration of yield management to avoid the escalation of crises during crisis communication phases

Development of a novel cycling impact–sliding wear rig to investigate the complex friction motion

Abstract In many industrial devices, impact-sliding wear is caused by a variety of complex vibrations between the contacted interfaces. Under actual conditions, impact and sliding motions do not occur in only one direction, and different complex impact-sliding motions exist on the tribology surfaces. In this study, an impact-sliding wear test rig is developed to investigate the wear effect of different complex motions. Using this rig, multi-type impact-sliding wear effects are realized and measured, such as those derived from unidirectional, reciprocating, and multi-mode combination motions. These three types of impact–sliding wear running behavior are tested and the wear damage mechanism is discussed

ICENETv2: A Fine-Grained River Ice Semantic Segmentation Network Based on UAV Images

Accurate ice segmentation is one of the most crucial techniques for intelligent ice monitoring. Compared with ice segmentation, it can provide more information for ice situation analysis, change trend prediction, and so on. Therefore, the study of ice segmentation has important practical significance. In this study, we focused on fine-grained river ice segmentation using unmanned aerial vehicle (UAV) images. This has the following difficulties: (1) The scale of river ice varies greatly in different images and even in the same image; (2) the same kind of river ice differs greatly in color, shape, texture, size, and so on; and (3) the appearances of different kinds of river ice sometimes appear similar due to the complex formation and change procedure. Therefore, to perform this study, the NWPU_YRCC2 dataset was built, in which all UAV images were collected in the Ningxia–Inner Mongolia reach of the Yellow River. Then, a novel semantic segmentation method based on deep convolution neural network, named ICENETv2, is proposed. To achieve multiscale accurate prediction, we design a multilevel features fusion framework, in which multi-scale high-level semantic features and lower-level finer features are effectively fused. Additionally, a dual attention module is adopted to highlight distinguishable characteristics, and a learnable up-sampling strategy is further used to improve the segmentation accuracy of the details. Experiments show that ICENETv2 achieves the state-of-the-art on the NWPU_YRCC2 dataset. Finally, our ICENETv2 is also applied to solve a realistic problem, calculating drift ice cover density, which is one of the most important factors to predict the freeze-up data of the river. The results demonstrate that the performance of ICENETv2 meets the actual application demand