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9,348 results on '"Qi, Qi"'

163. A method of building alarm causality graph for anomaly events in network services

Author

ZHANG Lei, JING Yuhan, HE Bo, QI Qi, CHEN Chen, and WANG Jingyu

Subjects
alarm compression, anomaly event, alarm storm summary, causality graph, artificial intelligence for IT operations, Telecommunication, TK5101-6720, Technology
Abstract

In network service systems, the occurrence of anomaly events often leads to a large number of alarm events in the system, forming alarm storms. Operators need to spend a lot of time and effort searching for key information and identifying the root cause of anomaly events from these alarm data. In order to reduce the number of alarms that operators needed to handle, as well as automatically extracted the root alarms in the alarm storm, a method for generating an alarm causality graph based on the analysis of the propagation mode of network service alarms was proposed , and applied to extract key information of the alarm storm when anomaly events occurred. Real datasets of an operator's online network management system were used in experiments to verify the effect of building the alarm causal graph in extracting the alarm storm abstract. A real-world case was used to analyze the physical significance of this method. The results show that the recall rate of extracting alarm storm summary can reach 96% and the vast majority of key information is retained by using the method of alarm causality graph generation. In addition, the compression rate of alarms using this method can reach 66.5% for alarm codes that are difficult to compress.

Published
2024
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165. SQUAB I: The first release of Strange QUasar candidates with ABnormal astrometric characteristics from Gaia EDR3 and SDSS

Author

Wu, Qi-Qi, Liao, Shi-Long, Ji, Xiang, Qi, Zhao-Xiang, Lin, Zhen-Ya Zheng Ru-Qiu, Zhang, Ying-Kang, and An, Tao

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Given their extremely large distances and small apparent sizes, quasars are generally considered as objects with near-zero parallax and proper motion. However, some special quasars may have abnormal astrometric characteristics, such as quasar pairs, lensed quasars, AGNs with bright parsec-scale optical jets, which are scientifically interesting objects, such as binary black holes. These quasars may come with astrometric jitter detectable with Gaia data, or significant changes in the position at different wavelengths. In this work, we aim to find these quasar candidates from Gaia EDR3 astrometric data combining with Sloan Digital Sky Survey (SDSS) spectroscopic data to provide a candidate catalog to the science community. We propose a series of criteria for selecting abnormal quasars based on Gaia astrometric data. We obtain two catalogs containing 155 sources and 44 sources, respectively. They are potential candidates of quasar pairs., Comment: 17 pages, 13 figures Accepted on 28 January 2022 Front. Astron. Space Sci

Published
2022
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168. Identification of Host–Protein Interaction Network of Canine Parvovirus Capsid Protein VP2 in F81 Cells

Author

Hongzhuan Zhou, Huanhuan Zhang, Xia Su, Fuzhou Xu, Bing Xiao, Jin Zhang, Qi Qi, Lulu Lin, Kaidi Cui, Qinqin Li, Songping Li, and Bing Yang

Subjects
CPV, VP2, protein interaction network, FHL2, Biology (General), QH301-705.5
Abstract

Canine Parvovirus (CPV) is a highly contagious virus that causes severe hemorrhagic enteritis and myocarditis, posing a major threat to the life and health of dogs. The molecular mechanism by which VP2, the major capsid protein of CPV, infects host cells and utilizes host cell proteins for self-replication remains poorly understood. In this study, 140 host proteins specifically binding to CPV VP2 protein were identified by immunoprecipitation combined with liquid chromatography–mass spectrometry (LC-MS/MS). Subsequently, the protein Interaction Network (PPI), the annotation of gene ontology (GO) and the database of Kyoto Encyclopedia of Genes and Genomes (KEGG) were constructed for in-depth analysis. The results showed that CPV VP2 protein participated mainly in cell metabolism, cell biosynthesis, protein folding and various signal transduction processes. According to the results of proteomics analysis, we randomly selected seven proteins for co-immunoprecipitation verification, and the experimental results were consistent with the LC-MS/MS data. In addition, our study found that the expression level of the VP2-interacting protein FHL2 mediated CPV replication. Preliminary studies have shown that knockdown of FHL2 promotes CPV replication by decreasing the expression of interferon β (IFN-β) and interferon-stimulated genes (ISGs), while overexpression of FHL2 can inhibit the replication of CPV by up-regulating the expression of IFN-β and related ISGs. This study lays the foundation for elucidating the potential function of CPV VP2 protein in the process of viral infection and proliferation which provides a theoretical basis for the design of antiviral agents and vaccines.

Published
2025
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170. SGUIE-Net: Semantic Attention Guided Underwater Image Enhancement with Multi-Scale Perception

Author

Qi, Qi, Li, Kunqian, Zheng, Haiyong, Gao, Xiang, Hou, Guojia, and Sun, Kun

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Due to the wavelength-dependent light attenuation, refraction and scattering, underwater images usually suffer from color distortion and blurred details. However, due to the limited number of paired underwater images with undistorted images as reference, training deep enhancement models for diverse degradation types is quite difficult. To boost the performance of data-driven approaches, it is essential to establish more effective learning mechanisms that mine richer supervised information from limited training sample resources. In this paper, we propose a novel underwater image enhancement network, called SGUIE-Net, in which we introduce semantic information as high-level guidance across different images that share common semantic regions. Accordingly, we propose semantic region-wise enhancement module to perceive the degradation of different semantic regions from multiple scales and feed it back to the global attention features extracted from its original scale. This strategy helps to achieve robust and visually pleasant enhancements to different semantic objects, which should thanks to the guidance of semantic information for differentiated enhancement. More importantly, for those degradation types that are not common in the training sample distribution, the guidance connects them with the already well-learned types according to their semantic relevance. Extensive experiments on the publicly available datasets and our proposed dataset demonstrated the impressive performance of SGUIE-Net. The code and proposed dataset are available at: https://trentqq.github.io/SGUIE-Net.html

Published
2022
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