Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data

Time series anomaly detection (TSAD) is an important data mining task with numerous applications in the IoT era. In recent years, a large number of deep neural network-based methods have been proposed, demonstrating significantly better performance than conventional methods on addressing challenging TSAD problems in a variety of areas. Nevertheless, these deep TSAD methods typically rely on a clean training dataset that is not polluted by anomalies to learn the "normal profile" of the underlying dynamics. This requirement is nontrivial since a clean dataset can hardly be provided in practice. Moreover, without the awareness of their robustness, blindly applying deep TSAD methods with potentially contaminated training data can possibly incur significant performance degradation in the detection phase. In this work, to tackle this important challenge, we firstly investigate the robustness of commonly used deep TSAD methods with contaminated training data which provides a guideline for applying these methods when the provided training data are not guaranteed to be anomaly-free. Furthermore, we propose a model-agnostic method which can effectively improve the robustness of learning mainstream deep TSAD models with potentially contaminated data. Experiment results show that our method can consistently prevent or mitigate performance degradation of mainstream deep TSAD models on widely used benchmark datasets

StackVAE-G: An efficient and interpretable model for time series anomaly detection

Recent studies have shown that autoencoder-based models can achieve superior performance on anomaly detection tasks due to their excellent ability to fit complex data in an unsupervised manner. In this work, we propose a novel autoencoder-based model, named StackVAE-G that can significantly bring the efficiency and interpretability to multivariate time series anomaly detection. Specifically, we utilize the similarities across the time series channels by the stacking block-wise reconstruction with a weight-sharing scheme to reduce the size of learned models and also relieve the overfitting to unknown noises in the training data. We also leverage a graph learning module to learn a sparse adjacency matrix to explicitly capture the stable interrelation structure among multiple time series channels for the interpretable pattern reconstruction of interrelated channels. Combining these two modules, we introduce the stacking block-wise VAE (variational autoencoder) with GNN (graph neural network) model for multivariate time series anomaly detection. We conduct extensive experiments on three commonly used public datasets, showing that our model achieves comparable (even better) performance with the state-of-the-art modelsand meanwhile requires much less computation and memory cost. Furthermore, we demonstrate that the adjacency matrix learned by our model accurately captures the interrelation among multiple channels, and can provide valuable information for failure diagnosis applications.Comment: Accepted to AI Ope

Bioactive polydimethylsiloxane surface for optimal human mesenchymal stem cell sheet culture

Human mesenchymal stem cell (hMSC) sheets hold great potential in engineering three-dimensional (3D) completely biological tissues for diverse applications. Conventional cell sheet culturing methods employing thermoresponsive surfaces are cost ineffective, and rely heavily on available facilities. In this study, a cost-effective method of layer-by-layer grafting was utilized for covalently binding a homogenous collagen I layer on a commonly used polydimethylsiloxane (PDMS) substrate surface in order to improve its cell adhesion as well as the uniformity of the resulting hMSC cell sheet. Results showed that a homogenous collagen I layer was obtained via this grafting method, which improved hMSC adhesion and attachment through reliable collagen I binding sites. By utilizing this low-cost method, a uniform hMSC sheet was generated. This technology potentially allows for mass production of hMSC sheets to fulfill the demand of thick hMSC constructs for tissue engineering and biomanufacturing applications

Parallel Computing for LURR of Earthquake Prediction

The LURR theory is a new approach for earthquake prediction, which achieves a good result within China mainland and some regions in America, Japan, and Australia. However, the expansion of the prediction region leads to the refinement of its longitude and latitude and the increase of the time period. This requires more and more computations and volume of data reaching the order of GB, which will be very difficult for a single CPU. In this paper, adopting the technology of domain decomposition and parallelizing using MPI, we developed a new parallel tempospatial scanning program

A Diffusion Weighted Graph Framework for New Intent Discovery

New Intent Discovery (NID) aims to recognize both new and known intents from unlabeled data with the aid of limited labeled data containing only known intents. Without considering structure relationships between samples, previous methods generate noisy supervisory signals which cannot strike a balance between quantity and quality, hindering the formation of new intent clusters and effective transfer of the pre-training knowledge. To mitigate this limitation, we propose a novel Diffusion Weighted Graph Framework (DWGF) to capture both semantic similarities and structure relationships inherent in data, enabling more sufficient and reliable supervisory signals. Specifically, for each sample, we diffuse neighborhood relationships along semantic paths guided by the nearest neighbors for multiple hops to characterize its local structure discriminately. Then, we sample its positive keys and weigh them based on semantic similarities and local structures for contrastive learning. During inference, we further propose Graph Smoothing Filter (GSF) to explicitly utilize the structure relationships to filter high-frequency noise embodied in semantically ambiguous samples on the cluster boundary. Extensive experiments show that our method outperforms state-of-the-art models on all evaluation metrics across multiple benchmark datasets. Code and data are available at https://github.com/yibai-shi/DWGF.Comment: EMNLP 2023 Mai

Engineering stem cell cardiac patch with microvascular features representative of native myocardium

The natural myocardium is a highly aligned tissue with an oriented vasculature. Its characteristic cellular as well as nanoscale extracellular matrix (ECM) organization along with an oriented vascular network ensures appropriate blood supply and functional performance. Although significant efforts have been made to develop anisotropic cardiac structure, currently neither an ideal biomaterial nor an effective vascularization strategy to engineer oriented and high-density capillary-like microvessels has been achieved for clinical cardiovascular therapies. A naturally derived oriented ECM nanofibrous scaffold mimics the physiological structure and components of tissue ECM and guides neovascular network formation. The objective of this study was to create an oriented and dense microvessel network with physiological myocardial microvascular features. Methods: Highly aligned decellularized human dermal fibroblast sheets were used as ECM scaffold to regulate physiological alignment of microvascular networks by co-culturing human mesenchymal stem cells (hMSCs) and endothelial cells (ECs). The influence of topographical features on hMSC and EC interaction was investigated to understand underlying mechanisms of neovasculature formation. Results: Results demonstrate that the ECM topography can be translated to ECs via CD166 tracks and significantly improved hMSC-EC crosstalk and vascular network formation. The aligned ECM nanofibers enhanced structure, length, and density of microvascular networks compared to randomly organized nanofibrous ECM. Moreover, hMSC-EC co-culture promoted secretion of pro-angiogenic growth factors and matrix remodeling via metalloprotease-2 (MMP-2) activation, which resulted in highly dense vascular network formation with intercapillary distance (20 μm) similar to the native myocardium. Conclusion: HMSC-EC co-culture on the highly aligned ECM generates physiologically oriented and dense microvascular network, which holds great potential for cardiac tissue engineering

Revealing the time lag between slope stability and reservoir water fluctuation from InSAR observations and wavelet tools— a case study in Maoergai Reservoir (China)

Reservoir water fluctuation in supply and storage cycle have strong triggering effects on landslides on both sides of reservoir banks. Early identification of reservoir landslides and revealing the relationship between slope stability and the triggering factors including reservoir level and rainfall, are of great significance in further protecting nearby residents’ lives and properties. In this paper, based on the small baseline subset time series method (SBAS-InSAR), the potential landslides with active displacements in the river bank of Maoergai hydropower station in Heishui County from 2018 to 2020 were monitored with Sentinel-1 data. As a result, a total of 20 unstable slopes were detected. Subsequently, it was found through a gray correlation analysis that the fluctuation of the reservoir water level is the main triggering factor for the displacement on unstable slopes. This paper applied wavelet tools to quantify the time lag between slope stability and reservoir water fluctuation, revealing that the displacement exhibits a seasonal trend, whose high-frequency signal displacement has an interannual period (1 year). Based on the Cross Wavelet Transform (XWT) analysis, under the interannual scale of one year, the reservoir water fluctuation and nonlinear displacement show a clear common power in wavelet. Additionally, a time lag of 65–120 days between slope stability and reservoir water fluctuations has been found, indicating that the non-linear displacements were behind the water level changes. Among the factors affecting the time lag, the elevation of the points and their distance to the bank shore show Pearson’s correlation coefficients of 0.69 and 0.70, respectively. The observed time lag and correlations could be related to the gradual saturation/drainage processes of the slope and the drainage path. This paper demonstrates the technical support to quantitatively reveal the time lag between slope stability and reservoir water fluctuation by InSAR and wavelet tools, providing strong support for the analysis of the mechanisms of landslides in Maoergai reservoir area.The work was supported by the National Natural Science Foundation of China (Grant No. 41801391), ESA-MOST China DRAGON-5 project (ref. 59339) and the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project (SKLGP2020Z012) and Sichuan Science Foundation for Outstanding Youth (23NSFJQ0167)