Redundancy Removing Aggregation Network with Distance Calibration for Video Face Recognition

Attention-based techniques have been successfully used for rating image quality, and have been widely employed for set-based face recognition. Nevertheless, for video face recognition, where the base convolutional neural network (CNN) trained on large-scale data already provides discriminative features, fusing features with only predicted quality scores to generate representation are likely to cause duplicate sample dominant problem, and degrade performance correspondingly. To resolve the problem mentioned above, we propose a redundancy removing aggregation network (RRAN) for video face recognition. Compared with other quality-aware aggregation schemes, RRAN can take advantage of similarity information to tackle the noise introduced by redundant video frames. By leveraging metric learning, RRAN introduces a distance calibration scheme to align distance distributions of negative pairs of different video representations, which improves the accuracy under a uniform threshold. A series of experiments is conductedon multiple realistic data sets to evaluate the performance of RRAN, including YouTube Faces, IJB-A, and IJB-C. In comprehensive experiments, we demonstrate that our method can diminish the overall influence of poor quality components with large proportion in the video and further improve the overall recognition performance with individual difference. Specifically, RRAN achieves a 96.84% accuracy on YouTube Face, outperforming all existing aggregation schemes.Peer reviewe

Establishment and Optimization of Two-dimensional Electrophoresis System for Spleen Proteome of Sillago sihama Forsskål

Taking Sillago sihama Forsskål as the research object, through the optimization of the extraction method, hydration and isoelectric focusing, a two-dimensional electrophoresis system was established for spleen proteome of S. sihama Forsskål. The results showed that the twp-step hydration and isoelectric focusing method is better than the hydration and isoelectric focusing integrated method. The protein spots of the spleen of S. sihama Forsskål were detected by two-dimensional electrophoresis mainly at pH 4.0-7.0. The establishment of this technical system will lay a foundation for further research on proteomics of S. sihama Forsskål in the future

The Safety of Cold-Chain Food in Post-COVID-19 Pandemic: Precaution and Quarantine

Since the outbreak of coronavirus disease-19 (COVID-19), cold-chain food contamination caused by the pathogenic severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has attracted huge concern. Cold-chain foods provide a congenial environment for SARS-CoV-2 survival, which presents a potential risk for public health. Strengthening the SARS-CoV-2 supervision of cold-chain foods has become the top priority in many countries. Methodologically, the potential safety risks and precaution measures of SARS-CoV-2 contamination on cold-chain food are analyzed. To ensure the safety of cold-chain foods, the advances in SARS-CoV-2 detection strategies are summarized based on technical principles and target biomarkers. In particular, the techniques suitable for SARS-CoV-2 detection in a cold-chain environment are discussed. Although many quarantine techniques are available, the field-based quarantine technique on cold-chain food with characteristics of real-time, sensitive, specific, portable, and large-scale application is urgently needed

Metformin exerts anti-tumor effects via Sonic hedgehog signaling pathway by targeting AMPK in HepG2 cells

Metformin, a traditional first-line pharmacologic treatment for type 2 diabetes, has recently been shown to impart anti-cancer effects on hepatocellular carcinoma (HCC). However, the molecular mechanism of metformin on its antitumor activity is still not completely clear. The Sonic hedgehog (Shh) signaling pathway is closely associated with the initiation and progression of HCC. Therefore, the aim of the current study was to investigate the effects of metformin on the biological behavior of HCC and the underlying functional mechanism of metformin on the Shh pathway. The HCC cellular was induced in HepG2 cells by recombinant human Shh (rhShh). The effects of metformin on proliferation and metastasis were evaluated by proliferation, wound healing and invasion assays in vitro. The mRNA and protein expression levels of proteins related to the Shh pathway were measured by western blotting, quantitative PCR and immunofluorescence staining. Metformin inhibited rhShh-induced proliferation and metastasis. Furthermore, metformin decreased mRNA and protein expression of components of the Shh pathway including Shh, Ptch, Smo and Gli-1. Silencing of AMPK in the presence of metformin revealed that metformin could exert its inhibitory effect via AMPK. Our findings demonstrate that metformin can suppress the migration and invasion of HepG2 cells via AMPK-mediated inhibition of the Shh pathway.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Research on transformer condition evaluation method based on association rule set pair analysis theory

Combining the advantages of set pair analysis and association rules, This paper proposes a transformer condition evaluation based on association rule with set pair analysis theory. In this paper, by analyzing the correlation between the various fault symptoms of transformer, a set of fault types is obtained. At the same time, this paper introduces variable weight formula based on the support degree and confidence degree of association rules, and finally the weight coefficients of fault types and fault symptoms are obtained. By comparing and calculating the support and confidence of association rules, while introducing variable weight formulas, the weight coefficients of fault types and fault symptoms are obtained. it effectively avoid the subjectivity of expert opinions or experiences. Based on the scalability of set pair analysis, a 5-element connection degree is adopted to improve the accuracy of handling uncertain factors in transformer fault diagnosis

Effect of Temperatures and Graphene on the Mechanical Properties of the Aluminum Matrix: A Molecular Dynamics Study

Graphene has become an ideal reinforcement for reinforced metal matrix composites due to its excellent mechanical properties. However, the theory of graphene reinforcement in graphene/aluminum matrix composites is not yet well developed. In this paper, the effect of different temperatures on the mechanical properties of the metal matrix is investigated using a classical molecular dynamics approach, and the effects of the configuration and distribution of graphene in the metal matrix on the mechanical properties of the composites are also described in detail. It is shown that in the case of a monolayer graphene-reinforced aluminum matrix, the simulated stretching process does not break the graphene as the strain increases, but rather, the graphene and the aluminum matrix have a shearing behavior, and thus, the graphene “pulls out" from the aluminum matrix. In the parallel stretching direction, the tensile stress tends to increase with the increase of the graphene area ratio. In the vertical stretching direction, the tensile stress tends to decrease as the percentage of graphene area increases. In the parallel stretching direction, the tensile stress of the system tends to decrease as the angle between graphene and the stretching direction increases. It is important to investigate the effect of a different graphene distribution in the aluminum matrix on the mechanical properties of the composites for the design of high-strength graphene/metal matrix composites