59 research outputs found

    ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection

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    Anomaly detection in multivariate time series data is of paramount importance for ensuring the efficient operation of large-scale systems across diverse domains. However, accurately detecting anomalies in such data poses significant challenges. Existing approaches, including forecasting and reconstruction-based methods, struggle to address these challenges effectively. To overcome these limitations, we propose a novel anomaly detection framework named ImDiffusion, which combines time series imputation and diffusion models to achieve accurate and robust anomaly detection. The imputation-based approach employed by ImDiffusion leverages the information from neighboring values in the time series, enabling precise modeling of temporal and inter-correlated dependencies, reducing uncertainty in the data, thereby enhancing the robustness of the anomaly detection process. ImDiffusion further leverages diffusion models as time series imputers to accurately capturing complex dependencies. We leverage the step-by-step denoised outputs generated during the inference process to serve as valuable signals for anomaly prediction, resulting in improved accuracy and robustness of the detection process. We evaluate the performance of ImDiffusion via extensive experiments on benchmark datasets. The results demonstrate that our proposed framework significantly outperforms state-of-the-art approaches in terms of detection accuracy and timeliness. ImDiffusion is further integrated into the real production system in Microsoft and observe a remarkable 11.4% increase in detection F1 score compared to the legacy approach. To the best of our knowledge, ImDiffusion represents a pioneering approach that combines imputation-based techniques with time series anomaly detection, while introducing the novel use of diffusion models to the field.Comment: To appear in VLDB 2024.Code: https://github.com/17000cyh/IMDiffusion.gi

    Metabolic profiling of Apostichopus japonicus body wall exposed to a typical type of PBDEs: potential health risks and impact on sea cucumber health

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    IntroductionSea cucumbers are cultivated mainly for their valuable body wall. Polybrominated diphenyl ethers are common persistent pollutants in sea waters with known impacts on aquatic animals nonetheless not yet studied for the body wall of sea cucumbers.MethodsUsing ltra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Triple-TOF-MS), we investigated the metabolic impact of 2,2',4,4'-tetrabromodiphenyl ether (BDE-47) on the body wall of Apostichopus japonicus. etabolite changes and metabolic pathway alterations were assessed in response to three distinct concentrations of BDE-47: 0.1 µg/L, 1.0 µg/L, and 10.0 µg/L.REsultsExposure to BDE-47 led to notable alterations in the metabolic profiles of the body wall. A total of 95~102 metabolites in the 0.1 ~ 10.0 µg/L BDE-47 treated group were altered significantly, and various disrupted metabolic pathways were identified and characterized. These metabolites and metabolic pathways were mainly involved in lipid metabolism, energy metabolism, immunity, oxidative stress, inflammation, and neurotoxicity.DiscussionThe findings of our study shed light on the potential health risks that polybrominated diphenyl ethers present to sea cucumbers. This underscores the imperative for both researchers and policymakers to delve deeper into further investigations and studies. These results indicate the necessity for enhanced monitoring and management practices within the sea cucumber aquaculture industry to mitigate the impact of these persistent organic pollutants and protect the health and safety of this valuable resource

    The Association Between Quadriceps Strength and Synovitis in Knee Osteoarthritis : An Exploratory Study From the Osteoarthritis Initiative

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    OBJECTIVE: The aim of this study was to explore the association between quadriceps strength and synovitis in knee osteoarthritis (KOA). METHODS: This study was derived from the Osteoarthritis Initiative (OAI), which recruited adults from the OAI cohort with or at risk of KOA. Knees with complete records of isometric quadriceps strength and effusion-synovitis and Hoffa-synovitis assessments were included. Quadriceps strength was measured isometrically at baseline. Effusion-synovitis and Hoffa-synovitis were measured using the Magnetic Resonance Imaging Osteoarthritis Knee Score at baseline and at 1-year and 2-year follow-ups. Generalized estimating equations were used to analyze the associations of baseline quadriceps strength with changes in effusion-synovitis and Hoffa-synovitis in multivariable analyses. Additionally, analyses were stratified by synovitis-driven inflammatory phenotypes. RESULTS: A total of 1513 knees were included in this study. In total, 61% of the subjects were female; subjects had an average age of 61.9 (SD 8.8) years and a mean BMI of 29.4 (SD 4.7). Regarding the whole population, baseline quadriceps strength was negatively associated with baseline effusion-synovitis and follow-up changes in effusion-synovitis (odds ratio [OR] 0.77-0.86), but no significant association was observed in terms of Hoffa-synovitis. Stratified by synovitis-driven inflammatory phenotype, baseline quadriceps strength was significantly associated with follow-up changes in effusion-synovitis-but not in Hoffa-synovitis-in the population with existing effusion-synovitis (OR 0.75-0.79). CONCLUSION: Higher baseline quadriceps strength was negatively associated with changes in effusion-synovitis-but not in Hoffa-synovitis-especially in the population with existing effusion-synovitis. Our findings suggested a potential protective role of the quadriceps in effusion-synovitis

    Towards Open Vocabulary Learning: A Survey

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    In the field of visual scene understanding, deep neural networks have made impressive advancements in various core tasks like segmentation, tracking, and detection. However, most approaches operate on the close-set assumption, meaning that the model can only identify pre-defined categories that are present in the training set. Recently, open vocabulary settings were proposed due to the rapid progress of vision language pre-training. These new approaches seek to locate and recognize categories beyond the annotated label space. The open vocabulary approach is more general, practical, and effective compared to weakly supervised and zero-shot settings. This paper provides a thorough review of open vocabulary learning, summarizing and analyzing recent developments in the field. In particular, we begin by comparing it to related concepts such as zero-shot learning, open-set recognition, and out-of-distribution detection. Then, we review several closely related tasks in the case of segmentation and detection, including long-tail problems, few-shot, and zero-shot settings. For the method survey, we first present the basic knowledge of detection and segmentation in close-set as the preliminary knowledge. Next, we examine various scenarios in which open vocabulary learning is used, identifying common design elements and core ideas. Then, we compare the recent detection and segmentation approaches in commonly used datasets and benchmarks. Finally, we conclude with insights, issues, and discussions regarding future research directions. To our knowledge, this is the first comprehensive literature review of open vocabulary learning. We keep tracing related works at https://github.com/jianzongwu/Awesome-Open-Vocabulary.Comment: Project page at https://github.com/jianzongwu/Awesome-Open-Vocabular

    Exploring Seasonal and Circadian Rhythms in Structural Traits of Field Maize from LiDAR Time Series

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    International audiencePlant growth rhythm in structural traits is important for better understanding plant response to the ever-changing environment. Terrestrial laser scanning (TLS) is a well-suited tool to study structural rhythm under field conditions. Recent studies have used TLS to describe the structural rhythm of trees, but no consistent patterns have been drawn. Meanwhile, whether TLS can capture structural rhythm in crops is unclear. Here, we aim to explore the seasonal and circadian rhythms in maize structural traits at both the plant and leaf levels from time-series TLS. The seasonal rhythm was studied using TLS data collected at four key growth periods, including jointing, bell-mouthed, heading, and maturity periods. Circadian rhythms were explored by using TLS data acquired around every 2 hours in a whole day under standard and cold stress conditions. Results showed that TLS can quantify the seasonal and circadian rhythm in structural traits at both plant and leaf levels. (1) Leaf inclination angle decreased significantly between the jointing stage and bell-mouthed stage. Leaf azimuth was stable after the jointing stage. (2) Some individual-level structural rhythms (e.g., azimuth and projected leaf area/PLA) were consistent with leaf-level structural rhythms. (3) The circadian rhythms of some traits (e.g., PLA) were not consistent under standard and cold stress conditions. (4) Environmental factors showed better correlations with leaf traits under cold stress than standard conditions. Temperature was the most important factor that significantly correlated with all leaf traits except leaf azimuth. This study highlights the potential of time-series TLS in studying outdoor agricultural chronobiology

    Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

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    In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

    Large-Scale Malicious Software Classification with Fuzzified Features and Boosted Fuzzy Random Forest

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    Multi-Granularity Retrieval Model for Bridging Gaps between Biomedical Concepts and Entities: THUIR at TREC 2007 Genomics Track

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    Abstract General concepts are always used to describe query requirement (In the example "What tumor types are associated with Rb1 mutations?", "Tumor types" is a general concept, and its entity in a relevant documents can be "brain tumor"). To bridge the gaps between concepts in user queries and entities in relevant documents, we proposed a multi-granularity retrieval model in TREC 2007 Genomics task. The model consists of three components: (1) Paragraph retrieval is employed to retrieve candidate paragraph initially; (2) Dictionary-based NER is utilized to recognize named entities of given types; (3) Passage ranking is used to rank retrieved candidate passages. Our proposed model achieve promising result (Passage MAP=0.1023, with NER bottleneck eliminated)
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