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Φ-DVAE: Physics-informed dynamical variational autoencoders for unstructured data assimilation

JOURNAL OF COMPUTATIONAL PHYSICS 2024年 515卷

作者： Glyn-Davies, Alex Duffin, Connor Akyildiz, O. Deniz Girolami, Mark Univ Cambridge Dept Engn Trumpington St Cambridge CB2 1PZ England Imperial Coll London Dept Math Exhibit Rd London SW7 2AZ England Alan Turing Inst British Lib 96 Euston Rd London NW1 2DB England

Incorporating unstructured data into physical models is a challenging problem that is emerging in data assimilation. Traditional approaches focus on well-defined observation operators whose functional forms are typically assumed to be known. This prevents these methods from achieving a consistent model-data synthesis in configurations where the mapping from data-space to model- space is unknown. To address these shortcomings, in this paper we develop a physics-informed dynamical variational autoencoder (Phi-DVAE) to embed diverse data streams into time-evolving physical systems described by differential equations. Our approach combines a standard, possibly nonlinear, filter for the latent state-space model and a VAE, to assimilate the unstructured data into the latent dynamical system. Unstructured data, in our example systems, comes in the form of video data and velocity field measurements, however the methodology is suitably generic to allow for arbitrary unknown observation operators. A variational Bayesian framework is used for the joint estimation of the encoding, latent states, and unknown system parameters. To demonstrate the method, we provide case studies with the Lorenz-63 ordinary differential equation, and the advection and Korteweg-de Vries partial differential equations. Our results, with synthetic data, show that Phi-DVAE provides a data efficient dynamics encoding methodology which is competitive with standard approaches. Unknown parameters are recovered with uncertainty quantification, and unseen data are accurately predicted.

关键词： Data assimilation Nonlinear filtering variational autoencoder Inverse problem variational Bayes

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Multi-Objective Design of DNA-Stabilized Nanoclusters Using variational autoencoders With Automatic Feature Extraction

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ACS NANO 2024年第39期18卷 26997-27008页

作者： Sadeghi, Elham Mastracco, Peter Gonzalez-Rosell, Anna Copp, Stacy M. Bogdanov, Petko Univ Albany SUNY Dept Comp Sci Albany NY 12222 USA Univ Calif Irvine Dept Mat Sci & Engn Irvine CA 92697 USA Univ Calif Irvine Dept Chem Dept Chem & Biomol Engn Irvine CA 92697 USA Univ Calif Irvine Dept Phys & Astron Irvine CA 92697 USA

DNA-stabilized silver nanoclusters (Ag-N-DNAs) have sequence-tuned compositions and fluorescence colors. High-throughput experiments together with supervised machine learning models have recently enabled design of DNA templates that select for Ag-N-DNA properties, including near-infrared (NIR) emission that holds promise for deep tissue bioimaging. However, these existing models do not enable simultaneous selection of multiple Ag-N-DNA properties, and require significant expert input for feature engineering and class definitions. This work presents a model for multiobjective, continuous-property design of Ag-N-DNAs with automatic feature extraction, based on variational autoencoders (VAEs). This model is generative, i.e., it learns both the forward mapping from DNA sequence to Ag-N-DNA properties and the inverse mapping from properties to sequence, and is trained on an experimental data set of DNA sequences paired with Ag-N-DNA fluorescence properties. Experimental testing shows that the model enables effective design of Ag-N-DNA emission, including bright NIR Ag-N-DNAs with 4-fold greater abundance compared to training data. In addition, Shapley analysis is employed to discern learned nucleobase patterns that correspond to fluorescence color and brightness. This generative model can be adapted for a range of biomolecular systems with sequence-dependent properties, enabling precise design of emerging biomolecular nanomaterials.

关键词： variational autoencoder silver nanocluster DNA interpretable machinelearning fluorescence near-infrared

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Disease Progression Score Estimation From Multimodal Imaging and MicroRNA Data Using Supervised variational autoencoders

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第12期26卷 6024-6035页

作者： Kmetzsch, Virgilio Becker, Emmanuelle Saracino, Dario Rinaldi, Daisy Camuzat, Agnes Le Ber, Isabelle Colliot, Olivier Sorbonne Univ Paris Brain Inst ICM AP HPCNRSInriaInsermInst Cerveau F-75013 Paris France Univ Rennes Inria CNRS IRISA F-35000 Rennes France Sorbonne Univ Paris Brain Inst ICM AP HPInst CerveauCNRSInserm F-75013 Paris France Sorbonne Univ Paris Brain Inst Hop Pitie Salpetriere AP HPInst Memory & Alzheimers Dis IM2AInst Cerv F-75013 Paris France

Frontotemporal dementia and amyotrophic lateral sclerosis are rare neurodegenerative diseases with no effective treatment. The development of biomarkers allowing an accurate assessment of disease progression is crucial for evaluating new therapies. Concretely, neuroimaging and transcriptomic (microRNA) data have been shown useful in tracking their progression. However, no single biomarker can accurately measure progression in these complex diseases. Additionally, large samples are not available for such rare disorders. It is thus essential to develop methods that can model disease progression by combining multiple biomarkers from small samples. In this paper, we propose a new framework for computing a disease progression score (DPS) from cross-sectional multimodal data. Specifically, we introduce a supervised multimodal variational autoencoder that can infer a meaningful latent space, where latent representations are placed along a disease trajectory. A score is computed by orthogonal projections onto this path. We evaluate our framework with multiple synthetic datasets and with a real dataset containing 14 patients, 40 presymptomatic genetic mutation carriers and 37 controls from the PREV-DEMALS study. There is no ground truth for the DPS in real-world scenarios, therefore we use the area under the ROC curve (AUC) as a proxy metric. Results with the synthetic datasets support this choice, since the higher the AUC, the more accurate the predicted simulated DPS. Experiments with the real dataset demonstrate better performance in comparison with state-of-the-art approaches. The proposed framework thus leverages cross-sectional multimodal datasets with small sample sizes to objectively measure disease progression, with potential application in clinical trials.

关键词： Disease progression score deep learning microRNA multimodal data neurodegenerative disease neuroimaging variational autoencoder

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Convolutional variational autoencoders and Resampling Techniques with Generative Adversarial Network for Enhancing Internet of Thing Security

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PATTERN RECOGNITION AND IMAGE ANALYSIS 2024年第3期34卷 562-569页

作者： Dong, Huiyao Kotenko, I. V. Russian Acad Sci St Petersburg Fed Res Ctr St Petersburg 199178 Russia

The Internet of Things is a pivotal constituent of the contemporary technological revolution and has experienced expeditious expansion in recent times. The proliferation of Internet of Things devices has led to enhanced convenience and automation. However, the extensive deployment of Internet of Things devices has also engendered concerns regarding data privacy and security. Among various detection and prevention methodologies, deep learning is emerging as a prominent trend. This paper ultilizes convolutional variational autoencoders and resampling techniques for network attacks detection. The proposed methodology employs a hybrid data resampling technique to tackle the issue of imbalanced classes, followed by the implementation of a convolutional variational autoencoder classification model with a weighted loss function. The experiments demonstrate that the light-weighted convolutional variational autoencoder outperforms the baseline models. Therefore, it possesses the capability to effectively detect intrusive activities in real-world settings and strengthen the Internet of Things security.

关键词： Internet of Things cyber-attacks variational autoencoder

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Health Indicator for Low-Speed Axial Bearings Using variational autoencoders

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IEEE ACCESS 2020年 8卷 35842-35852页

作者： Hemmer, Martin Klausen, Andreas Huynh Van Khang Robbersmyr, Kjell G. Waag, Tor, I Univ Agder Dept Engn Sci N-4877 Grimstad Norway MHWirth AS N-4639 Kristiansand Norway NORCE Norwegian Res Ctr AS N-4877 Grimstad Norway

This paper proposes a method for calculating a health indicator (HI) for low-speed axial rolling element bearing (REB) health assessment by utilizing the latent representation obtained by variational inference using variational autoencoders (VAEs), trained on each speed reference in the dataset. Further, versatility is added by conditioning on the speed, extending the VAE to a conditional VAE (CVAE), thereby incorporating all speeds in a single model. Within the framework, the coefficients of autoregressive (AR) models are used as features. The dimensionality reduction inherent in the proposed method lowers the need of expert knowledge to design good condition indicators. Moreover, the suggested methodology allows for setting the probability of false alarms when encoding new data points to the latent variable space using the trained model. The effectiveness of the proposed method is validated based on two different datasets: from a workshop test of an offshore drilling machine and from an in-house test rig for axial bearings. In both datasets, the HI is exceeding the warning and alarm levels with a probability of false alarm (PFA) of 10(-6), and the method is most effective at lower shaft speeds.

关键词： Training Decoding Dimensionality reduction Drilling machines Fault detection Industries Rolling bearings Bearing fault detection condition monitoring unsupervised learning variational autoencoder conditional variational autoencoder generative models

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An entangled mixture of variational autoencoders approach to deep clustering

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NEUROCOMPUTING 2023年第1期529卷 182-189页

作者： Caciularu, Avi Goldberger, Jacob Bar Ilan Univ Comp Sci Dept Ramat Gan Israel Bar Ilan Univ Engn Fac Ramat Gan Israel

We present a novel deep clustering algorithm that utilizes a variational autoencoder (VAE) framework with an entangled multi encoder-decoder neural architecture. Our model enforces a complementary structure that guides the learned latent representations towards a better space arrangement. It differs from previous VAE-based clustering algorithms by employing a new generative model that uses multiple encoder-decoders that are entangled to provide a joint clustering decision. The optimal clustering is found by optimizing a lower bound of the model likelihood function. Both the reconstruction component and the regularization component of the ELBO objective function are explicitly involved in the clustering procedure. We show that this modeling results in both better clustering capabilities and improved data generation. The proposed method is evaluated on standard datasets and is shown to significantly outper-form state-of-the-art deep clustering methods.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Deep clustering variational autoencoder Mixture model

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Discovering web services in social web service repositories using deep variational autoencoders

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INFORMATION PROCESSING & MANAGEMENT 2020年第4期57卷 102231-102231页

作者： Lizarralde, Ignacio Mateos, Cristian Zunino, Alejandro Majchrzak, Tim A. Gronli, Tor-Morten UNICEN CONICET Tandil ISISTAN Buenos Aires DF Argentina Univ Agder Kristiansand Norway Kristiania Univ Coll Oslo Norway

Web Service registries have progressively evolved to social networks-like software repositories. Users cooperate to produce an ever-growing, rich source of Web APIs upon which new value-added Web applications can be built. Such users often interact in order to follow, comment on, consume and compose services published by other users. In this context, Web Service discovery is a core functionality of modern registries as needed Web Services must be discovered before being consumed or composed. Many efforts to provide effective keyword-based service discovery mechanisms are based on Information Retrieval techniques as services are described using structured or unstructured textdocuments that specify the provided functionality. However, traditional techniques suffer from term-mismatch, which means that only the terms that are contained in both user queries and descriptions are exploited to perform service retrieval. Early feature learning techniques such as LSA or LDA tried to solve this problem by finding hidden or latent features in text documents. Recently, alternative feature learning based techniques such as Word Embeddings achieved state of the art results for Web Service discovery. In this paper, we propose to learn features from service descriptions by using variational autoencoders, a special kind of autoencoder which restricts the encoded representation to model latent variables. autoencoders in turn are deep neural networks used for unsupervised learning of efficient codings. We train our autoencoder using a real 17 113-service dataset extracted from the *** API social repository. We measure discovery efficacy by using both Recall and Precision metrics, achieving significant gains compared to both Word Embeddings and classic latent features modelling techniques. Also, performance-oriented experiments show that the proposed approach can be readily exploited in practice.

关键词： Service-oriented computing Web Services Service discovery Deep neural network variational autoencoder

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Analog ensemble data assimilation and a method for constructing analogs with variational autoencoders

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QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 2021年第734期147卷 139-149页

作者： Grooms, Ian Univ Colorado Dept Appl Math Boulder CO 80309 USA

It is proposed to use analogs of the forecast mean to generate an ensemble of perturbations for use in ensemble optimal interpolation (EnOI) or ensemble variational (EnVar) methods. A new method of constructing analogs using variational autoencoders (VAEs;a machine learning method) is proposed. The resulting analog methods using analogs from a catalog (AnEnOI), and using constructed analogs (cAnEnOI), are tested in the context of a multiscale Lorenz-'96 model, with standard EnOI and an ensemble square root filter for comparison. The use of analogs from a modestly-sized catalog is shown to improve the performance of EnOI, with limited marginal improvements resulting from increases in the catalog size. The method using constructed analogs (cAnEnOI) is found to perform as well as a full ensemble square root filter, and to be robust over a wide range of tuning parameters.

关键词： analogs Ensemble Optimal Interpolation machine learning variational autoencoder

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Hybrid Unsupervised Time-Series Anomaly Detection for Industrial IoT Based on Spatio-Temporal Feature 18th

Hybrid Unsupervised Time-Series Anomaly Detection for Indus...

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18th China Conference on Wireless Sensor Networks, CWSN 2024

作者： Dong, Yu Yao, Muyan Qi, Peng Tao, Dan School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819621880

The rapid development of the Industrial Internet of Things (IIoT) has enabled the communication of numerous devices, resulting in the generation of massive time-series data. The goal of time-series anomaly detection algorithms is to quickly detect anomalous behavior and trigger warnings. We propose a Hybrid Unsupervised Time-series Anomaly Detection method (HUTAD) that fully leverages two different anomaly detection strategies: spatial feature extraction by a variational autoencoder and temporal feature extraction by a variational autoencoder and a recurrent feature extraction LSTM structure with predictive capability. Through a dynamic fusion module based on the self-attention mechanism, we integrate the two methods to obtain the output, analyze the spatio-temporal patterns of the data, and detect potential anomalies. We incorporate adversarial training in our model to further improve its accuracy and robustness. Finally, we use three widely-used large-scale datasets to validate our model. The experimental results show that our method outperforms the baseline methods in F1-Score. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial training Anomaly detection Industrial loT Self-attention variational autoencoder

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Joint variational autoencoders for multimodal imputation and embedding

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NATURE MACHINE INTELLIGENCE 2023年第6期5卷 631-+页

作者： Kalafut, Noah Cohen Huang, Xiang Wang, Daifeng Univ Wisconsin Madison Dept Comp Sci Madison WI 53706 USA Univ Wisconsin Madison Waisman Ctr Madison WI 53706 USA Univ Wisconsin Madison Dept Biostat & Med Informat Madison WI 53706 USA

While single-cell multimodal datasets allow for the measurement of individual cells to understand cellular and molecular mechanisms, generating multimodal data for many cells is costly and challenging. Cohen Kalafut and colleagues develop a machine learning model capable of imputing single-cell modalities and prioritizing multimodal features, such as gene expression, chromatin accessibility and electrophysiology. Single-cell multimodal datasets have measured various characteristics of individual cells, enabling a deep understanding of cellular and molecular mechanisms. However, multimodal data generation remains costly and challenging, and missing modalities happen frequently. Recently, machine learning approaches have been developed for data imputation but typically require fully matched multimodalities to learn common latent embeddings that potentially lack modality specificity. To address these issues, we developed an open-source machine learning model, Joint variational autoencoders for multimodal Imputation and Embedding (JAMIE). JAMIE takes single-cell multimodal data that can have partially matched samples across modalities. variational autoencoders learn the latent embeddings of each modality. Then, embeddings from matched samples across modalities are aggregated to identify joint cross-modal latent embeddings before reconstruction. To perform cross-modal imputation, the latent embeddings of one modality can be used with the decoder of the other modality. For interpretability, Shapley values are used to prioritize input features for cross-modal imputation and known sample labels. We applied JAMIE to both simulation data and emerging single-cell multimodal data including gene expression, chromatin accessibility, and electrophysiology in human and mouse brains. JAMIE significantly outperforms existing state-of-the-art methods in general and prioritized multimodal features for imputation, providing potentially novel mechanistic insights at cellular resolution.

关键词： Data imputation Data integration Deep learning Multimodal Single-cell variational autoencoder

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