N=2N=2 and 44 Super Yang-Mills Theories on M4×Z2×Z2M_4 \times Z_2 \times Z_2 Geometry

We derive the $N=2$ and $4$ super Yang-Mills theories from the viewpoint of the $M_4\times Z_2\times Z_2$ gauge theory. Scalars and pseudoscalars appearing in the theories are regarded as gauge fields along the directions on $Z_2\times Z_2$ discrete space.Comment: 13 pages, LaTeX fil

Thermodynamic models and analysis for determination of water container

554-565To investigate a problem existing in reality, heat transfer theory is used to develop a temperature model for water by treating with heat convection and heat conduction. The calculated results over an ideal range of temperature show that this model is accurate and easy to use. The model developed for hot water was extended to comparing these simulations with the ones based on the experiment, proving the validity of the basic model. With the adjustable parameters obtained from the improved model, the model can accurately predict the temperature tendency of miniature thermal systems and help make strategies

Antiviral Decoction of Isatidis Radix (板藍根 bǎn lán gēn) Inhibited Influenza Virus Adsorption on MDCK Cells by Cytoprotective Activity

AbstractThe aim of this study is to elucidate how the Isatidis Radix (板藍根 bǎn lán gēn) tonic, as an aqueous mixture of hundreds of compositions, interrupts the infection of influenza viruses to their host cells. The efficacy of the tonic was evaluated and expressed as cell proliferation rate and plaque reduction rate in Madin-Darby Canine Kidney (MDCK) cells, against 3 strains of influenza A and B viruses. This boiling water (at 100°C) extract of Isatidis Radix (RIE) showed antiviral activity against influenza virus A and B. The concentration for 50% inhibition of influenza virus A replication (IC50) in MDCK cell was 12.6mg/mL with a therapeutic index >8. When cells were incubated with RIE prior to virus adsorption, the numbers of viable cell were at least doubled compared to the numbers of virus control, RIE incubation after virus adsorption and RIE incubation with virus prior to adsorption, in both influenza virus A and B. Moreover, much less virus particles were spotted by scanning electron microscope (SEM) in the RIE pre-treated cells than the cells without RIE treatment. These results indicate the antiviral activity of RIE is mainly attributed to its host cell protection effect but not actions on virus or post-virus-adsorption interruption. Cell, but not virus, is more likely to be the action target of RIE

Comparison of the quality of life for spectacle wearers and contact lens wearers before and after refractive surgery

AIM: To compare the difference of the quality of life(QOL)of spectacle wearers or contact lens wearers before and after refractive surgery by the quality of life impact of refractive correction(QIRC). METHODS:Totally 72 cases were enrolled in the investigation with 50 spectacle wearers and 22 contact lens wearers. The QOL of them were surveyed by Chinese QIRC before surgery and 3 months later after surgery. RESULTS: 1. The QOL scores(42.29±4.90 in post-operation vs 39.30±5.16 in pre-operation)and satisfaction scores(86.51±9.14 in post-operation vs 71.58±13.24 in pre-operation)of spectacle wearers were higher after surgery more than before, and the difference was statistically significant(P<0.01). The vision and physical functioning(P<0.05), driving and activity in night

Network representation learning: From traditional feature learning to deep learning

Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of graph data. It has been successfully applied in many real-world tasks related to network science, such as social network data processing, biological information processing, and recommender systems. Deep Learning is a powerful tool to learn data features. However, it is non-trivial to generalize deep learning to graph-structured data since it is different from the regular data such as pictures having spatial information and sounds having temporal information. Recently, researchers proposed many deep learning-based methods in the area of NRL. In this survey, we investigate classical NRL from traditional feature learning method to the deep learning-based model, analyze relationships between them, and summarize the latest progress. Finally, we discuss open issues considering NRL and point out the future directions in this field. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved

DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions

Multiple object tracking (MOT) tends to become more challenging when severe occlusions occur. In this paper, we analyze the limitations of traditional Convolutional Neural Network-based methods and Transformer-based methods in handling occlusions and propose DNMOT, an end-to-end trainable DeNoising Transformer for MOT. To address the challenge of occlusions, we explicitly simulate the scenarios when occlusions occur. Specifically, we augment the trajectory with noises during training and make our model learn the denoising process in an encoder-decoder architecture, so that our model can exhibit strong robustness and perform well under crowded scenes. Additionally, we propose a Cascaded Mask strategy to better coordinate the interaction between different types of queries in the decoder to prevent the mutual suppression between neighboring trajectories under crowded scenes. Notably, the proposed method requires no additional modules like matching strategy and motion state estimation in inference. We conduct extensive experiments on the MOT17, MOT20, and DanceTrack datasets, and the experimental results show that our method outperforms previous state-of-the-art methods by a clear margin.Comment: ACM Multimedia 202