Complex and flexible data access policy in attribute-based encryption

The Journal of Supercomputing - With the development of cloud computing application, attribute-based encryption (ABE) with flexibly fine-grained data access control is widely adopted. However,...     

POI recommendation based on a multiple bipartite graph network model

The Journal of Supercomputing - In location-based social network platforms, the point-of-interest(POI) recommendation is an essential function to serve users. The existing POI recommendation...     

A Survey on Long-Tailed Visual Recognition

The heavy reliance on data is one of the major reasons that currently limit the development of deep learning. Data quality directly dominates the effect of deep learning models, and the long-tailed distribution is one of the factors affecting data quality. The long-tailed phenomenon is prevalent due to the prevalence of power law in nature. In this case, the performance of deep learning models is often dominated by the head classes while the learning of the tail classes is severely underdeveloped. In order to learn adequately for all classes, many researchers have studied and preliminarily addressed the long-tailed problem. In this survey, we focus on the problems caused by long-tailed data distribution, sort out the representative long-tailed visual recognition datasets and summarize some mainstream long-tailed studies. Specifically, we summarize these studies into ten categories from the perspective of representation learning, and outline the highlights and limitations of each category. Besides, we have studied four quantitative metrics for evaluating the imbalance, and suggest using the Gini coefficient to evaluate the long-tailedness of a dataset. Based on the Gini coefficient, we quantitatively study 20 widely-used and large-scale visual datasets proposed in the last decade, and find that the long-tailed phenomenon is widespread and has not been fully studied. Finally, we provide several future directions for the development of long-tailed learning to provide more ideas for readers.

     

微波辅助萃取-超高效液相色谱/静电场轨道阱高分辨质谱法快速测定纺织品中有害有机溶剂残留量

建立了1个快速测定纺织品中有害有机溶剂残留量的超高效液相色谱/静电场轨道阱高分辨质谱(UPLC/Orbitrap HRMS)方法,该方法以乙醇为萃取溶剂,100℃下微波萃取纺织品中残留的有害有机溶剂,萃取液直接进行UPLC/Orbitrap HRMS,提取离子色谱峰面积外标法定量。对样品的提取条件、分析条件进行了优化。该方法的定量限为0.5μg/kg~10.0μg/kg。在3个不同加标浓度水平下,方法的平均加标回收率为81.8~94.5%,相对偏差偏差为3.8%~10.5%(n=9)。该方法简便快速,灵敏度高,定量限低,可有效地解决纺织品中残留的有害有机溶剂的快速测定问题。     

Immobilization of Pt Nanoparticles via Rapid and Reusable Electropolymerization of Dopamine on TiO2 Nanotube Arrays for Reversible SERS Substrates and Nonenzymatic Glucose Sensors

Inspired by mussel‐adhesion phenomena in nature, polydopamine (PDA) coatings are a promising route to multifunctional platforms for decorating various materials. The typical self‐polymerization process of dopamine is time‐consuming and the coatings of PDA are not reusable. Herein, a reusable and time‐saving strategy for the electrochemical polymerization of dopamine (EPD) is reported. The PDA layer is deposited on vertically aligned TiO₂ nanotube arrays (NTAs). Owing to the abundant catechol and amine groups in the PDA layer, uniform Pt nanoparticles (NPs) are deposited onto the TiO₂ NTAs and can effectively prevent the recombination of electron–hole pairs generated from photo‐electrocatalysis and transfer the captured electrons to participate in the photo‐electrocatalytic reaction process. Compared with pristine TiO₂ NTAs, the as‐prepared Pt@TiO₂ NTA composites exhibit surface‐enhanced Raman scattering sensitivity for detecting rhodamine 6G and display excellent UV‐assisted self‐cleaning ability, and also show promise as a nonenzymatic glucose biosensor. Furthermore, the mussel‐inspired electropolymerization strategy and the fast EPD‐reduced nanoparticle decorating process presented herein can be readily extended to various functional substrates, such as conductive glass, metallic oxides, and semiconductors. It is the adaptation of the established PDA system for a selective, robust, and generalizable sensing system that is the emphasis of this work.     

In Situ Bioorthogonal Metabolic Labeling for Fluorescence Imaging of Virus Infection In Vivo

Optical fluorescence imaging is an important strategy to explore the mechanism of virus–host interaction. However, current fluorescent tag labeling strategies often dampen viral infectivity. The present study explores an in situ fluorescent labeling strategy in order to preserve viral infectivity and precisely monitor viral infection in vivo. In contrast to pre‐labeling strategy, mice are first intranasally infected with azide‐modified H5N1 pseudotype virus (N₃‐H5N1p), followed by injection of dibenzocyclooctyl (DBCO)‐functionalized fluorescence 6 h later. The results show that DBCO dye directly conjugated to N₃‐H5N1p in lung tissues through in vivo bioorthogonal chemistry with high specificity and efficacy. More remarkably, in situ labeling rather than conventional prelabeling strategy effectively preserves viral infectivity and immunogenicity both in vitro and in vivo. Hence, in situ bioorthogonal viral labeling is a promising and reliable strategy for imaging and tracking viral infection in vivo.