Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detection

This paper presents a novel framework, named Global-Local Correspondence Framework (GLCF), for visual anomaly detection with logical constraints. Visual anomaly detection has become an active research area in various real-world applications, such as industrial anomaly detection and medical disease diagnosis. However, most existing methods focus on identifying local structural degeneration anomalies and often fail to detect high-level functional anomalies that involve logical constraints. To address this issue, we propose a two-branch approach that consists of a local branch for detecting structural anomalies and a global branch for detecting logical anomalies. To facilitate local-global feature correspondence, we introduce a novel semantic bottleneck enabled by the visual Transformer. Moreover, we develop feature estimation networks for each branch separately to detect anomalies. Our proposed framework is validated using various benchmarks, including industrial datasets, Mvtec AD, Mvtec Loco AD, and the Retinal-OCT medical dataset. Experimental results show that our method outperforms existing methods, particularly in detecting logical anomalies.Comment: Submission to IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOG

New Insights Into the Response of Metabolome of Escherichia coli O157:H7 to Ohmic Heating

The objective of this study was to investigate the effects of ohmic heating and water bath heating (WB) on the metabolome of Escherichia coli O157:H7 cells at the same inactivation levels. Compared to low voltage long time ohmic heating (5 V/cm, 8.50 min, LVLT) and WB (5.50 min), the high voltage short time ohmic heating (10 V/cm, 1.75 min, HVST) had much shorter heating time. Compared to the samples of control (CT), there were a total of 213 differential metabolites identified, among them, 73, 78, and 62 were presented in HVST, LVLT, and WB samples, revealing a stronger metabolomic response of E. coli cells to HVST and LVLT than WB. KEGG enrichment analysis indicated that the significantly enriched pathways were biosynthesis and metabolism of amino acids (alanine, arginine, aspartate, and glutamate, etc.), followed by aminoacyl-tRNA biosynthesis among the three treatments. This is the first metabolomic study of E. coli cells in response to ohmic heating and presents an important step toward understanding the mechanism of ohmic heating on microbial inactivation, and can serve as a theoretical basis for better application of ohmic heating in food products

Pre-pandemic psychiatric disorders and risk of COVID-19 : a UK Biobank cohort analysis

This work is supported by the National Natural Science Foundation of China (81971262 to HS), West China Hospital COVID-19 Epidemic Science and Technology Project (HX-2019-nCoV-014 to HS), Sichuan University Emergency Grant (2020scunCoVyingji10002 to HS), and EU Horizon 2020 Research and Innovation Action Grant (847776 to UAV and FF). We thank the team members involved in West China Biomedical Big Data Center for Disease Control and Prevention for their support. Publisher Copyright: © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 licenseBackground: Psychiatric morbidities have been associated with a risk of severe infections through compromised immunity, health behaviours, or both. However, data are scarce on the association between multiple types of pre-pandemic psychiatric disorders and COVID-19. We aimed to assess the association between pre-pandemic psychiatric disorders and the subsequent risk of COVID-19 using UK Biobank. Methods: For this cohort analysis, we included participants from UK Biobank who were registered in England and excluded individuals who died before Jan 31, 2020, (the start of the COVID-19 outbreak in the UK) or had withdrawn from UK Biobank. Participants diagnosed with a psychiatric disorder before Jan 31 were included in the group of individuals with pre-pandemic psychiatric disorders, whereas participants without a diagnosis before the outbreak were included in the group of individuals without pre-pandemic psychiatric disorders. We used the Public Health England dataset, UK Biobank hospital data, and death registers to collect data on COVID-19 cases. To examine the relationship between pre-pandemic psychiatric disorders and susceptibility to COVID-19, we used logistic regression models to estimate odds ratios (ORs), controlling for multiple confounders and somatic comorbidities. Key outcomes were all COVID-19, COVID-19 specifically diagnosed in inpatient care, and COVID-19-related deaths. ORs were also estimated separately for each psychiatric disorder and on the basis of the number of pre-pandemic psychiatric disorders. As a positive disease control, we repeated analyses for hospitalisation for other infections. Findings: We included 421 014 UK Biobank participants in our study and assessed their COVID-19 status between Jan 31 and July 26, 2020. 50 809 participants were diagnosed with psychiatric disorders before the outbreak, while 370 205 participants had no psychiatric disorders. The mean age at outbreak was 67·80 years (SD 8·12). We observed an elevated risk of COVID-19 among individuals with pre-pandemic psychiatric disorders compared with that of individuals without such conditions. The fully adjusted ORs were 1·44 (95% CI 1·28–1·62) for All COVID-19 cases, 1·55 (1·34–1·78) for Inpatient COVID-19 cases, and 2·03 (1·59–2·59) for COVID-19-related deaths. We observed excess risk, defined as risk that increased with the number of pre-pandemic psychiatric disorders, across all diagnostic categories of pre-pandemic psychiatric disorders. We also observed an association between psychiatric disorders and elevated risk of hospitalisation due to other infections (OR 1·74, 95% CI 1·58–1·93). Interpretation: Our findings suggest that pre-existing psychiatric disorders are associated with an increased risk of COVID-19. These findings underscore the need for surveillance of and care for populations with pre-existing psychiatric disorders during the COVID-19 pandemic. Funding: National Natural Science Foundation of China.Peer reviewe

Public awareness, emotional reactions and human mobility in response to the COVID-19 outbreak in China- A population-based ecological study

This work is supported by the West China Hospital COVID-19 Epidemic Science and Technology Project (No. HX-2019-nCoV-014 to Dr Song, No. HX-2019-nCoV-019 to Dr Zhang), Sichuan University Emergency Grant (No. 2020scunCoVyingji1002 to Dr Song, No. 2020scunCoVyingji1005 to Dr Zhang), and Emergency Grant form Science & Technology Department of Sichuan Providence (No. 2020YFS0007 to Dr Zhang). Publisher Copyright: © 2020 Cambridge University Press. All rights reserved.Background: The outbreak of COVID-19 generated severe emotional reactions, and restricted mobility was a crucial measure to reduce the spread of the virus. This study describes the changes in public emotional reactions and mobility patterns in the Chinese population during the COVID-19 outbreak. Methods: We collected data on public emotional reactions in response to the outbreak through Weibo, the Chinese Twitter, between January 1st and March 31st, 2020. Using anonymized location-tracking information, we analyzed the daily mobility patterns of approximately 90% of Sichuan residents. Results: There were three distinct phases of the emotional and behavioral reactions to the COVID-19 outbreak. The alarm phase (January 19th�26th) was a restriction-free period, characterized by few new daily cases, but enormous public negative emotions (the number of negative comments per Weibo post increased by 246.9 per day, 95%CI: 122.5�371.3), and a substantial increase in self-limiting mobility (from 45.6% to 54.5%, changing by 1.5% per day, 95%CI: 0.7%�2.3%). The epidemic phase (January 27th�February 15th) exhibited rapidly increasing numbers of new daily cases, decreasing expression of negative emotions (a decrease of 27.3 negative comments per post per day, 95%CI:-40.4�-14.2), and a stabilized level of self-limiting mobility. The relief phase (February 16th�March 31st) had a steady decline in new daily cases and decreasing levels of negative emotion and self-limiting mobility. Conclusions: During the COVID-19 outbreak in China, the public�s emotional reaction was strongest before the actual peak of the outbreak and declined thereafter. The change in human mobility patterns occurred before the implementation of restriction orders, suggesting a possible link between emotion and behavior.Peer reviewe

A survey on network data collection

Networks have dramatically changed our daily life and infiltrated all aspects of human society. At the same time when we enjoy the convenience and benefits brought by the networks, we also suffer from a great amount of intelligent attacks and malicious intrusions. As a fundamental procedure of network security measurement, network data collection executes real time network monitoring, supports network performance evaluation, assists network billing, and helps traffic testing and filtering. Thus, it plays a crucial and essential role for dealing with network intrusion detection and unwanted traffic control. But an adaptive and effective data collection mechanism that can be pervasively applied into heterogeneous networks is still lacked. The literature we have hunted rarely comments and compares the performance of existing data collection mechanisms. In this paper, we conduct a survey on existing data collection methods, mechanisms and architectures. According to a number of proposed assessment criteria, we evaluate the performance of existing data collection mechanisms and summarize their characteristics. Furthermore, we figure out some open issues based our investigation and forecast future research directions.Peer reviewe