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Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2023年第4期4卷 045033页

作者： Ziatdinov, Maxim Wong, Chun Yin (Tommy) Kalinin, Sergei, V Oak Ridge Natl Lab Ctr Nanophase Mat Sci Oak Ridge TN 37831 USA Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN 37831 USA Univ Tennessee Bredesen Ctr Interdisciplinary Res & Grad Educ Knoxville TN 37996 USA Univ Tennessee Dept Mat Sci & Engn Knoxville TN 37996 USA

Recent advances in scanning tunneling and transmission electron microscopies (STM and STEM) have allowed routine generation of large volumes of imaging data containing information on the structure and functionality of materials. The experimental data sets contain signatures of long-range phenomena such as physical order parameter fields, polarization, and strain gradients in STEM, or standing electronic waves and carrier-mediated exchange interactions in STM, all superimposed onto scanning system distortions and gradual changes of contrast due to drift and/or mis-tilt effects. Correspondingly, while the human eye can readily identify certain patterns in the images such as lattice periodicities, repeating structural elements, or microstructures, their automatic extraction and classification are highly non-trivial and universal pathways to accomplish such analyses are absent. We pose that the most distinctive elements of the patterns observed in STM and (S)TEM images are similarity and (almost-) periodicity, behaviors stemming directly from the parsimony of elementary atomic structures, superimposed on the gradual changes reflective of order parameter distributions. However, the discovery of these elements via global Fourier methods is non-trivial due to variability and lack of ideal discrete translation symmetry. To address this problem, we explore the shift-invariant variational autoencoders (shift-VAEs) that allow disentangling characteristic repeating features in the images, their variations, and shifts that inevitably occur when randomly sampling the image space. Shift-VAEs balance the uncertainty in the position of the object of interest with the uncertainty in shape reconstruction. This approach is illustrated for model 1D data, and further extended to synthetic and experimental STM and STEM 2D data. We further introduce an approach for training shift-VAEs that allows finding the latent variables that comport to known physical behavior. In this specific case, t

关键词： variational autoencoder scanning tunneling microscopy scanning transmission electron microscopy unsupervised learning latent representation

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Using shapley values and variational autoencoders to explain predictive models with dependent mixed features

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 9553-9603页

作者： Lars Henry Berge Olsen Ingrid Kristine Glad Martin Jullum Kjersti Aas Department of Mathematics University of Oslo Oslo Norway Norwegian Computing Center Oslo Norway

Shapley values are today extensively used as a model-agnostic explanation framework to explain complex predictive machine learning models. Shapley values have desirable theoretical properties and a sound mathematical foundation in the field of cooperative game theory. Precise Shapley value estimates for dependent data rely on accurate modeling of the dependencies between all feature combinations. In this paper, we use a variational autoencoder with arbitrary conditioning (VAEAC) to model all feature dependencies simultaneously. We demonstrate through comprehensive simulation studies that our VAEAC approach to Shapley value estimation outperforms the state-of-the-art methods for a wide range of settings for both continuous and mixed dependent features. For high-dimensional settings, our VAEAC approach with a non-uniform masking scheme significantly outperforms competing methods. Finally, we apply our VAEAC approach to estimate Shapley value explanations for the Abalone data set from the UCI Machine Learning Repository.

关键词： explainable artificial intelligence prediction explanation Shapley values variational autoencoder feature dependence

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Combining variational autoencoders and physical bias for improved microscopy data analysis

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2023年第4期4卷 045004页

作者： Biswas, Arpan Ziatdinov, Maxim Kalinin, Sergei, V Ctr Nanophase Mat Sci Oak Ridge Natl Lab Oak Ridge TN 37831 USA Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN 37831 USA Univ Tennessee Mat Sci & Engn Knoxville TN 37996 USA

Electron and scanning probe microscopy produce vast amounts of data in the form of images or hyperspectral data, such as electron energy loss spectroscopy or 4D scanning transmission electron microscope, that contain information on a wide range of structural, physical, and chemical properties of materials. To extract valuable insights from these data, it is crucial to identify physically separate regions in the data, such as phases, ferroic variants, and boundaries between them. In order to derive an easily interpretable feature analysis, combining with well-defined boundaries in a principled and unsupervised manner, here we present a physics augmented machine learning method which combines the capability of variational autoencoders to disentangle factors of variability within the data and the physics driven loss function that seeks to minimize the total length of the discontinuities in images corresponding to latent representations. Our method is applied to various materials, including NiO-LSMO, BiFeO3, and graphene. The results demonstrate the effectiveness of our approach in extracting meaningful information from large volumes of imaging data.

关键词： ferroic variants physics driven loss variational autoencoder latent space unsupervised learning

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Integrating Adversarial Generative Network with variational autoencoders towards Cross-Modal Alignment for Zero-Shot Remote Sensing Image Scene Classification

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REMOTE SENSING 2022年第18期14卷 4533页

作者： Ma, Suqiang Liu, Chun Li, Zheng Yang, Wei Henan Univ Sch Comp & Informat Engn Kaifeng 475000 Peoples R China Henan Univ Henan Key Lab Big Data Anal & Proc Kaifeng 475004 Peoples R China Henan Univ Henan Engn Lab Spatial Informat Proc Kaifeng 475004 Peoples R China Henan Univ Henan Ind Technol Acad Spatio Temporal Big Data Zhengzhou 450046 Peoples R China

Remote sensing image scene classification takes image blocks as classification units and predicts their semantic descriptors. Because it is difficult to obtain enough labeled samples for all classes of remote sensing image scenes, zero-shot classification methods which can recognize image scenes that are not seen in the training stage are of great significance. By projecting the image visual features and the class semantic features into the latent space and ensuring their alignment, the variational autoencoder (VAE) generative model has been applied to address remote-sensing image scene classification under a zero-shot setting. However, the VAE model takes the element-wise square error as the reconstruction loss, which may not be suitable for measuring the reconstruction quality of the visual and semantic features. Therefore, this paper proposes to augment the VAE models with the generative adversarial network (GAN) to make use of the GAN's discriminator in order to learn a suitable reconstruction quality metric for VAE. To promote feature alignment in the latent space, we have also proposed cross-modal feature-matching loss to make sure that the visual features of one class are aligned with the semantic features of the class and not those of other classes. Based on a public dataset, our experiments have shown the effects of the proposed improvements. Moreover, taking the ResNet models of ResNet18, extracting 512-dimensional visual features, and ResNet50 and ResNet101, both extracting 2048-dimensional visual features for testing, the impact of the different visual feature extractors has also been investigated. The experimental results show that better performance is achieved by ResNet18. This indicates that more layers of the extractors and larger dimensions of the extracted features may not contribute to the image scene classification under a zero-shot setting.

关键词： zero-shot learning remote sensing image scene classification cross-modal feature alignment variational autoencoder generative adversarial network

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Towards inverse microstructure-centered materials design using generative phase-field modeling and deep variational autoencoders

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ACTA MATERIALIA 2023年第1期259卷

作者： Attari, Vahid Khatamsaz, Danial Allaire, Douglas Arroyave, Raymundo Texas A&M Univ Mat Sci & Engn Dept College Stn TX 77840 USA Texas A&M Univ Mech Engn Dept College Stn TX 77840 USA

The field of Integrated Computational Materials Engineering (ICME) combines a broad range of methods to study materials' responses over a spectrum of length scales. A relatively unexplored aspect of microstructure -sensitive materials design is uncertainty propagation and quantification (UP/UQ) of materials' microstructure, as well as establishing process-structure-property (PSP) relationships for inverse material design. In this study, an efficient UP technique built on the idea of changing probability measures and a deep generative unsupervised representative machine learning method for microstructure-based design of thermal conductivity of materials is proposed. Probability measures are used to represent microstructure space, and Wasserstein metrics are used to test the efficiency of the UP method. By using deep variational autoencoder (VAE), we identify the correlations between the material/process parameters and the thermal conductivity of heterogeneous dual-phase microstructures. Through high-throughput screening, UP, and the deep-generative VAE method, PSP relationships that are too complex can be revealed by exploiting the materials' design space with an emphasis on microstructures. As a last point, we demonstrate generative machine learning serves as a useful tool for inverse microstructure-centered materials design, and we demonstrate this by examining the inverse design of thermal conductivity in nano-structured materials. The results reveal the effects of morphology, volume fraction, characteristic length scale, and the individual thermal diffusivity of phases on the thermal conductivity of dual-phase alloys. Our findings emphasize the advantages of high-throughput phase -field modeling and generative deep learning for linking PSP and inverse microstructure-centered materials design.

关键词： Computational materials design Process-structure-property linkage Microstructure Generative modeling Phase-field modeling Uncertainty quantification variational autoencoder

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Learning Subject-Generalized Topographical EEG Embeddings Using Deep variational autoencoders and Domain-Adversarial Regularization

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SENSORS 2021年第5期21卷 1792-1792页

作者： Hagad, Juan Lorenzo Kimura, Tsukasa Fukui, Ken-ichi Numao, Masayuki Osaka Univ Grad Sch Informat Sci & Technol Suita Osaka 5650871 Japan Osaka Univ Inst Sci & Ind Res Ibaraki Osaka 5670047 Japan

Two of the biggest challenges in building models for detecting emotions from electroencephalography (EEG) devices are the relatively small amount of labeled samples and the strong variability of signal feature distributions between different subjects. In this study, we propose a context-generalized model that tackles the data constraints and subject variability simultaneously using a deep neural network architecture optimized for normally distributed subject-independent feature embeddings. variational autoencoders (VAEs) at the input level allow the lower feature layers of the model to be trained on both labeled and unlabeled samples, maximizing the use of the limited data resources. Meanwhile, variational regularization encourages the model to learn Gaussian-distributed feature embeddings, resulting in robustness to small dataset imbalances. Subject-adversarial regularization applied to the bi-lateral features further enforces subject-independence on the final feature embedding used for emotion classification. The results from subject-independent performance experiments on the SEED and DEAP EEG-emotion datasets show that our model generalizes better across subjects than other state-of-the-art feature embeddings when paired with deep learning classifiers. Furthermore, qualitative analysis of the embedding space reveals that our proposed subject-invariant bi-lateral variational domain adversarial neural network (BiVDANN) architecture may improve the subject-independent performance by discovering normally distributed features.

关键词： electroencephalography emotion modeling subject independence domain generalization variational autoencoder domain adversarial network

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Two-step residual-error based approach for anomaly detection in engineering systems using variational autoencoders

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 101卷

作者： Gonzalez-Muniz, Ana Diaz, Ignacio Cuadrado, Abel A. Garcia-Perez, Diego Perez, Daniel Univ Oviedo Elect Engn Dept Edif Dept Oeste 2Campus Viesques S-N Gijon 33204 Spain Univ Leon Escuela Ingn SUPPRESS Res Grp Campus Vegazana Leon 24007 Spain

Anomaly detection is a crucial task in the engineering systems field. However, there is usually little or no information about all possible abnormal modes in systems. Hence, a common approach is to build a model of healthy behaviour, based on normal operation data, so that anomaly detection would depend on how well new data fit this model. According to this idea, we propose a residual-error based approach consisting of: a variational autoencoder, used to model the probability density function of the system's healthy behaviour;and a two-step classification algorithm, which classifies the incoming samples based on their residuals, and reports not only their normal/anomalous nature but also that of their components. We have tested this proposal in three different engineering contexts and we have compared its performance with that of state-of-the-art approaches, demonstrating its capability to successfully detect and characterize anomalies.

关键词： Anomaly detection Novelty detection Deep autoencoder variational autoencoder Engineering systems

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Enhancing monitoring performance of data sparse nonlinear processes through information sharing among different grades using Gaussian mixture prior variational autoencoders

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2021年 208卷 104219-104219页

作者： Lee, Yi Shan Chen, Junghui Chung Yuan Christian Univ Chung Li Dept Chem Engn Taoyuan 320 Taiwan

In the chemical industry, manufacturers produce multiple products with different grades constantly by changing the operating conditions and raw materials in the same production line to meet the customers' needs and market demands. Thus, establishing a model for monitoring such a process is inadequate because the data available in an operating region is often scarce. To deal with the hardship in monitoring the process that produces various products with multi-grades, the data sparsity problem in the target grade is addressed by simultaneously extracting and sharing common knowledge of the available grades with the target grade. In fact, multi-grade processes have similar operating conditions but they may have different ratios of ingredients. In this paper, the Gaussian mixture prior variational autoencoder (GMPVAE) is proposed to perform information sharing to enhance the target model. Complementary encoder and decoder networks represent the mapping of the source and target input data to extract the common features among the grades from the same production line. In contrast with an ordinary variational autoencoder, a Gaussian mixture model instead of a unit Gaussian distribution is set as the prior distribution of GMPVAE so that the unique characteristics of the target and source grades can be obtained. In addition, the objective function is designed on the basis of the goals to increase the reliability of the target model and to do information sharing and modeling in one step to reduce information loss during the information transferring procedure. Accordingly, the probability density estimates of latent variables and residuals rather than point estimates are then given so that distribution-based monitoring indices of the target grade can be designed and the fault detection decisions can be made opportunely. A numerical example and an industrial ammonia synthesis example are presented to show the effectiveness of the proposed method.

关键词： Data sparse Multi-grade Nonlinear process Process monitoring variational autoencoder

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Developing semi-supervised latent dynamic variational autoencoders to enhance prediction performance of product quality

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CHEMICAL ENGINEERING SCIENCE 2023年 265卷

作者： Lee, Yi Shan Chen, Junghui Chung Yuan Christian Univ Dept Chem Engn Taoyuan 32023 Taiwan

The online quality variables of soft sensors contribute greatly to obtaining immediate process information. The complex correlations between a large number of process variables inherited from the dynamic and nonlinear characteristics of chemical processes put more challenges on constructing soft-sensor models. Past developed steady-state soft sensors are not reliable for dynamic operating systems. Unequal sampling rates for the process and quality data cause missing values of quality data at some time points. This paper proposes a semi-supervised latent dynamic variational autoencoder to learn features between the process and quality data. A prediction network is constructed to generate artificial quality values for model training. Then the process and quality data are compressed into the latent space and the temporal relation is modeled in the clean latent space. The proposed method is compared with the conventional method for quality prediction in a numerical case and an industrial case. CO 2022 Elsevier Ltd. All rights reserved.

关键词： Bayes inference Dynamic process Quality prediction Semi -supervised State -space model variational autoencoder

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Anomaly detection on household appliances based on variational autoencoders

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2022年 32卷

作者： Castangia, Marco Sappa, Riccardo Girmay, Awet Abraha Camarda, Christian Macii, Enrico Patti, Edoardo Politecn Torino Dept Control & Comp Engn I-10129 Turin Italy Politecn Torino Interuniv Dept Reg & Urban Studies & Planning I-10129 Turin Italy

Electrical anomalies in residential buildings represent a serious problem that can unpredictably change the power profiles of end-users, causing a sub-optimal energy distribution. In addition, electrical faults can cause unnoticed energy wastages and higher energy bills, or even severe damages for properties and people in the most critical cases. In this paper, we introduce a novel anomaly detection method for detecting electrical faults in household appliances based on the analysis of their power signatures with unsupervised deep learning techniques. For this purpose, we trained a variational autoencoder to reconstruct the power signatures of three commonly used devices: the dishwasher, the washing machine and the dryer. For each use case, we injected several randomly generated anomalies that simulate to our best realistic electrical faults in these devices. To demonstrate the effectiveness of our method, we compared the accuracy of the variational autoencoder with the classification performance of a one-class support vector machine (OC-SVM) trained with two manual features: the energy consumption and the duration of the appliance's operations. The variational autoencoder showed higher classification accuracy with respect to the OC-SVM, reporting an F1-score greater than 90% in all the use cases. Most importantly, the results demonstrate that deep anomaly detection methods outperform traditional algorithms based on handcrafted features, allowing to better characterize the set of normal cycles and produce more precise alerts for the monitored devices. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Smart grids Household appliance Anomaly detection Unsupervised learning variational autoencoder Deep learning

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