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Deep neural network-based reduced-order modeling of ion-surface interactions combined with molecular dynamics simulation

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JOURNAL OF PHYSICS D-APPLIED PHYSICS 2023年第38期56卷 384005-384005页

作者： Kim, Byungjo Bae, Jinkyu Jeong, Hyunhak Hahn, Seung Ho Yoo, Suyoung Nam, Sang Ki Samsung Elect Co Ltd Mechatron Res Suwon 18448 Gyeonggi Do South Korea

With the advent of complex and sophisticated architectures in semiconductor device manufacturing, atomic-resolution accuracy and precision are commonly required for industrial plasma processing. This demands a comprehensive understanding of the plasma-material interactions-particularly for forming fine high-aspect ratio (HAR) feature patterns with sufficiently high yield in wafer-level processes. In particular, because the shape distortion in HAR pattern etching is attributed to the deviation of the energetic ion trajectory, the detailed ion-surface interactions need to be thoroughly investigated. In this study, molecular dynamics (MD) simulations were utilized to obtain a fundamental understanding of the collisional nature of accelerated Ar ions on the fluorinated Si surface that may appear on the sidewall of the HAR etched hole. High-fidelity data for ion-surface interaction features representing the energy and angle distributions (EADs) of sputtered atoms for varying degrees of surface F coverage and ion incident angles were obtained via extensive MD simulations. A deep learning-based reduced-order modeling (DL-ROM) framework was developed for efficiently predicting the characteristics of the ion-surface interactions. In the ROM framework, a conditional variational autoencoder (AE) was implemented to obtain regularized latent representations of the distributional data with the condition of the governing factors of the physical system. The proposed ROM framework accurately reproduced the MD simulation results and significantly outperformed various DL-ROMs, such as AE, sparse AE, contractive AE, denoising AE, and variational AE. From the inferred features of the sputtering yield and EADs of sputtered/scattered species, significant insights can be obtained regarding the ion interactions with the fluorinated surface. As the ion incident angle deviated from the glancing-angle range (incident angle >80 & DEG;), diffuse reflection behavior was observed, which can substa

关键词： plasma-material interaction molecular dynamics deep learning reduced-order modeling conditional variational autoencoder

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Unsupervised pathology detection in medical images using conditional variational autoencoders

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INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2019年第3期14卷 451-461页

作者： Uzunova, Hristina Schultz, Sandra Handels, Heinz Ehrhardt, Jan Univ Lubeck Inst Med Informat Lubeck Germany

PurposePathology detection in medical image data is an important but a rather complicated task. In particular, the big variability of the pathologies is a challenge to automatic detection methods and even to machine learning methods. Supervised algorithms would usually learn the appearance of a single pathological structure based on a large annotated dataset. As such data is not usually available, especially in large amounts, in this work we pursue a different unsupervised *** method is based on learning the entire variability of healthy data and detect pathologies by their differences to the learned norm. For this purpose, we use conditional variational autoencoders which learn the reconstruction and encoding distribution of healthy images and also have the ability to integrate certain prior knowledge about the data (condition).ResultsOur experiments on different 2D and 3D datasets show that the approach is suitable for the detection of pathologies and deliver reasonable Dice coefficients and AUCs. Also this method can estimate missing correspondences in pathological images and thus can be used as a pre-step to a registration method. Our experiments show improving registration results on pathological data when using this *** the presented approach is suitable for a rough pathology detection in medical images and can be successfully used as a preprocessing step to other image processing methods.

关键词： conditional variational autoencoder Unsupervised pathology detection Image registration

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SampleFix: Learning to Generate Functionally Diverse Fixes 21st

SampleFix: Learning to Generate Functionally Diverse Fixes

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21st Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Hajipour, Hossein Bhattacharyya, Apratim Staicu, Cristian-Alexandru Fritz, Mario CISPA Helmholtz Ctr Informat Secur Saarbrucken Germany Max Planck Inst Informat Saarbrucken Germany

ISBN: (纸本)9783030937331;9783030937324

Automatic program repair holds the potential of dramatically improving the productivity of programmers during the software development process and correctness of software in general. Recent advances in machine learning, deep learning, and NLP have rekindled the hope to eventually fully automate the process of repairing programs. However, previous approaches that aim to predict a single fix are prone to fail due to uncertainty about the true intend of the programmer. Therefore, we propose a generative model that learns a distribution over potential fixes. Our model is formulated as a deep conditional variational autoencoder that can efficiently sample fixes for a given erroneous program. In order to ensure diverse solutions, we propose a novel regularizer that encourages diversity over a semantic embedding space. Our evaluations on common programming errors show for the first time the generation of diverse fixes and strong improvements over the state-of-the-art approaches by fixing up to 45% of the erroneous programs. We additionally show that for the 65% of the repaired programs, our approach was able to generate multiple programs with diverse functionalities.

关键词： Program repair Generative models conditional variational autoencoder

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PoseCVAE: Anomalous Human Activity Detection 25

PoseCVAE: Anomalous Human Activity Detection

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25th International Conference on Pattern Recognition (ICPR)

作者： Jain, Yashswi Sharma, Ashvini Kumar Velmurugan, Rajbabu Banerjee, Biplab Indian Inst Technol Dept Elect Engn Bombay 400076 Maharashtra India Indian Inst Technol Ctr Studies Resources Engn Bombay 400076 Maharashtra India

ISBN: (纸本)9781728188089

Anomalous human activity detection is the task of identifying human activities that differ from the usual. Existing techniques, in general, try to deploy some samples from an open-set (anomalous activities can not be represented as a closed set) to define the discriminator. However, it is non-trivial to obtain novel activity instances. To this end, we propose PoseCVAE, a novel anomalous human activity detection strategy using the notion of generative modeling. We adopt a hybrid training strategy comprising of self-supervised and unsupervised learning. The self-supervised learning helps the encoder and decoder to learn better latent space representation of human pose trajectories. We train our framework to predict future pose trajectory given a normal track of past poses, i.e., the goal is to learn a conditional posterior distribution that represents normal training data. To achieve this we use a novel adaptation of a conditional variational autoencoder (CVAE) and refer it as PoseCVAE. Future pose prediction will be erroneous if the given poses are sampled from a distribution different from the learnt posterior, which is indeed the case with abnormal activities. To further separate the abnormal class, we imitate abnormal poses in the encoded space by sampling from a distinct mixture of Gaussians (MoG). We use a binary cross-entropy (BCE) loss as a novel addition to the standard CVAE loss function to achieve this. We test our framework on three publicly available datasets and achieve comparable performance to existing unsupervised methods that exploit pose information.

关键词： Stochastic Generative Models conditional variational autoencoder Pose Trajectory

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Intelligent Condition-Based Monitoring of Rotary Machines With Few Samples

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IEEE SENSORS JOURNAL 2020年第23期20卷 14337-14346页

作者： Dixit, Sonal Verma, Nishchal K. Indian Inst Technol Kanpur Dept Elect Engn Kanpur 208016 Uttar Pradesh India

Recently, intelligent condition based monitoring systems build on deep learning methods have gained popularity. The success of these methods relies upon the large labeled training datasets, which are crucial to collect in industries. Therefore, building an effective fault diagnosis system becomes challenging. In this paper, a novel fault diagnosis framework is proposed to tackle the issue of limited samples in the training dataset. In the proposed framework, firstly, new training samples termed as synthetic samples are generated to increase the size of the dataset. After that, both original and synthetic samples are stacked, and the classifier model is trained. This study proposes a modified conditional variational autoencoder (CVAE) to generate synthetic samples. In the proposed CVAE, centroid loss is added to the standard CVAE objective function. This loss directs generated samples to remain close with the centroid of their respective class, which helps in generating synthetic samples quite similar to the original samples. This paper also investigates the performance of proposed model in the presence of noise and effect of transformed data and original data. To verify the effectiveness of the proposed approach, the Air compressor and Case Western Reserve University datasets have been investigated. For the CWRU dataset with only 80 samples, accuracy of 96.39%, 99.58%, 98.33% was obtained using multilayer neural network, support vector machine, and RF classifiers respectively. Classification accuracy increased to 63.33% when modified CVAE is used instead of standard CVAE. Finally, a comparative analysis between proposed methods and other state-of-the-art methods has been presented.

关键词： Training Fault diagnosis Intelligent sensors Microsoft Windows Monitoring Data models Sensor data conditional variational autoencoder condition based monitoring synthetic data

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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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Generative population synthesis for joint household and individual characteristics

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COMPUTERS ENVIRONMENT AND URBAN SYSTEMS 2022年 96卷

作者： Aemmer, Zack MacKenzie, Don Univ Washington Dept Civil & Environm Engn 201 More HallBox 352700 Seattle WA 98195 USA

Household surveys provide immense value in the fields of transportation and urban planning. However, even the most well-funded surveying agencies rely on sampling methods to estimate the nature of the true population, and the collected microdata is frequently aggregated, or limited in volume and detail to protect the privacy of respondents. Population synthesis provides a means to scale this microdata to represent larger regions for use in microsimulation. Despite their accuracy and widespread adoption, traditional synthesis algorithms for reweighting microdata samples scale poorly with the number of variables and geographic regions being modeled, and can suffer from non-convergence with smaller sample sizes. Several generative models have been proposed to address these shortcomings, but lack features such as sub-region modeling, and the ability to simultaneously generate both individuals and households. This work proposes an extension to recent generative approaches capable of generating synthetic populations consisting of both individual and household-level variables, that uses a two-part variational autoencoder (VAE) and conditional-VAE (CVAE) to learn a distribution of latent variables in the general population, and use them to generate new samples. This can help in synthesizing smaller, traditionally under-sampled groups. This approach is benchmarked against a state of the art open source population synthesizer. In addition, the VAE/CVAE model is tested under increasingly minimal training data. Findings indicate the VAE/CVAE model creates more accurate populations, using less time than the traditional synthesizer under small to medium dimensional datasets (4-16 variables). The VAE/CVAE also performs well with few (n = 100) training samples, with diminishing returns for additional training samples.

关键词： Population synthesis conditional variational autoencoder Generative Machine learning

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Unsupervised PM_2.5 anomalies in China induced by the COVID-19 epidemic

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SCIENCE OF THE TOTAL ENVIRONMENT 2021年 795卷 148807-148807页

作者： Zhao, Yuan Wang, Li Huang, Tao Tao, Shu Liu, Junfeng Gao, Hong Luo, Jinmu Huang, Yufei Liu, Xinrui Chen, Kaijie Wang, Linfei Ma, Jianmin Lanzhou Univ Coll Earth & Environm Sci Key Lab Environm Pollut Predict & Control Lanzhou 730000 Gansu Peoples R China Peking Univ Coll Urban & Environm Sci Lab Earth Surface Proc Beijing 100871 Peoples R China Lanzhou Univ Collaborat Innovat Ctr Western Ecol Safety Lanzhou 730000 Peoples R China

To stop the spread of COVID-19 (2019 novel coronavirus), China placed lockdown on social activities across China since mid-January 2020. The government actions significantly affected emissions of atmospheric pollutants and unintentionally created a nationwide emission reduction scenario. In order to assess the impacts of COVID-19 on fine particular matter (PM2.5) levels, we developed a "conditional variational autoencoder" (CVAE) algorithm based on the deep learning to discern unsupervised PM2.5 anomalies in Chines cities during the COVID-19 epidemic. We show that the timeline of changes in number of cities with unsupervised PM2.5 anomalies is consistent with the timeline of WHO's response to COVID-19. Using unsupervised PM2.5 anomaly as a time node, we exam-ine changes in PM2.5 before and after the time node to assess the response of PM2.5 to the COVID-19 lockdown. The rate of decrease of PM2.5 around the time node in northern China is 3.5 times faster than southern China, and decreasing PM2.5 levels in southern China is 3.5 times of that in northern China. Results were also compared with anomalous PM2.5 occurring in Chinese's Spring Festival from 2017 to 2019, PM2.5 anomalies during around Chinese New Year in 2020 differ significantly from 2017 to 2019. We demonstrate that this method could be used to detect the response of air quality to sudden changes in social activities. (C) 2021 Elsevier B.V. All rights reserved.

关键词： COVID-19 PM2.5 conditional variational autoencoder Emission reduction

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Discriminative and generative models for anatomical shape analysis on point clouds with deep neural networks

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MEDICAL IMAGE ANALYSIS 2021年 67卷 101852-101852页

作者： Gutierrez-Becker, Benjamin Sarasua, Ignacio Wachinger, Christian Ludwig Maximilians Univ Munchen Univ Hosp Dept Child & Adolescent Psychiat Psychosomat & Ps Lab Artificial Intelligence Med Imaging AI Med Munich Germany

We introduce deep neural networks for the analysis of anatomical shapes that learn a low-dimensional shape representation from the given task, instead of relying on hand-engineered representations. Our framework is modular and consists of several computing blocks that perform fundamental shape processing tasks. The networks operate on unordered point clouds and provide invariance to similarity transformations, avoiding the need to identify point correspondences between shapes. Based on the framework, we assemble a discriminative model for disease classification and age regression, as well as a generative model for the accruate reconstruction of shapes. In particular, we propose a conditional generative model, where the condition vector provides a mechanism to control the generative process. For instance, it enables to assess shape variations specific to a particular diagnosis, when passing it as side information. Next to working on single shapes, we introduce an extension for the joint analysis of multiple anatomical structures, where the simultaneous modeling of multiple structures can lead to a more compact encoding and a better understanding of disorders. We demonstrate the advantages of our framework in comprehensive experiments on real and synthetic data. The key insights are that (i) learning a shape representation specific to the given task yields higher performance than alternative shape descriptors, (ii) multi-structure analysis is both more efficient and more accurate than single-structure analysis, and (iii) point clouds generated by our model capture morphological differences associated to Alzheimer's disease, to the point that they can be used to train a discriminative model for disease classification. Our framework naturally scales to the analysis of large datasets, giving it the potential to learn characteristic variations in large populations. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Shape analysis Deep neural networks conditional variational autoencoder Neuroanatomy

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Application of conditional generative model for sonic log estimation considering measurement uncertainty

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2021年 196卷 108028-108028页

作者： Jeong, Jina Park, Eungyu Emelyanova, Irina Pervukhina, Marina Esteban, Lionel Yun, Seong-Taek Kyungpook Natl Univ Dept Geol Daegu South Korea Commonwealth Sci & Ind Res Org CSIRO Energy Perth WA Australia Korea Univ Earth & Environm Sci 145 Anam Ro Seoul 02841 South Korea

Well-log data is a cost-effective means to characterize the petrophysical properties of a geological formation. Among the data, compressional- and shear-slowness (DTC and DTS, respectively) are the most reliable and have been widely applied in the interpretations. However, the availability of DTS data tends to be limited because of its high acquisition cost. This study proposes a method to reproduce or reconstruct the DTS data using other well-log data, such as gamma ray, neutron porosity, bulk density, and DTC. The developed method is based on the conditional variational autoencoder (CVAE) and effectively considers uncertainty associated with the variability of the measured data. The performance of this developed method is validated by applying the well-log data acquired from Satyr-5 and Callihoe-1 wells in the Northern Carnarvon Basin, Western Australia, and the prediction accuracy of the developed method is compared to recently developed data-driven methods (i.e., long short-term memory (LSTM) and bidirectional LSTM (bi-LSTM)). The results reveal that the developed method produces a better DTS estimation than LSTM and bi-LSTM. Furthermore, the effectiveness of the proposed method remains unaltered regardless of whether the data contain a specific trend over the depth or amount of training data are insufficient. As a further application of the developed method, an uncertainty relative to DTS estimation is quantitatively obtained from Monte-Carlo estimation, which uses a trained probability model of the developed method. Sensitivity analysis reveals the high effectiveness of DTC in improving the performance of the CVAE method. From our results, we can conclude that the proposed CVAE-based method is an effective tool for improving the efficiency and accuracy of DTS estimation.

关键词： Well-log estimation Long short-term memory (LSTM) Bi-direction LSTM (Bi-LSTM) conditional variational autoencoder Probabilistic estimation Sensitivity analysis

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