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Anomaly detection for data accountability of Mars telemetry data

EXPERT SYSTEMS WITH APPLICATIONS 2022年 189卷 116060-116060页

作者： Lakhmiri, Dounia Alimo, Ryan Le Digabel, Sebastien GERAD Montreal PQ Canada Polytech Montreal Montreal PQ Canada Jet Prop Lab Pasadena CA USA CALTECH Pasadena CA 91125 USA

The Mars Curiosity rover is frequently sending engineering and science data that goes through a pipeline of systems before reaching its final destination at the mission operations center making it prone to volume loss and data corruption. A ground data system analysis (GDSA) team is charged with the monitoring of this flow of information and the detection of anomalies in that data to request a re-transmission when necessary. So far, this task relied primarily on the expertise of GDSA analysts who, especially with new missions launching, require the assistance of an autonomous and effective detection tool. variational autoencoders are powerful deep neural networks that can learn to isolate such anomalies yet, as any deep network, they require an architectural search and fine-tuning to yield exploitable performance. Furthermore, this process needs to be repeated periodically to adjust to the changing flow of transmissions. This work presents Delta-MADS, a hybrid derivative-free optimization method designed to quickly produce efficient variational autoencoders in order to assist the GDSA team in their mission.

关键词： Anomaly detection variational autoencoder Hyperparameter optimization Architecture search Derivative-free optimization

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Spatio-Temporal Hourly and Daily Ozone Forecasting in China Using a Hybrid Machine Learning Model: autoencoder and Generative Adversarial Networks

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JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS 2022年第3期14卷

作者： Cheng, Meiling Fang, Fangxin Navon, Ionel M. Zheng, Jie Tang, Xiao Zhu, Jiang Pain, Christopher Imperial Coll London Dept Earth Sci & Engn Appl Modelling & Computat Grp London England Florida State Univ Dept Sci Comp Tallahassee FL 32306 USA Chinese Acad Sci Ctr Excellence Reg Atmospher Environm Inst Urban Environm Xiamen Peoples R China Chinese Acad Sci Int Ctr Climate & Environm Sci Inst Atmospher Phys Beijing Peoples R China

Efficient and accurate real-time forecasting of national spatial ozone distribution is critical to the provision of effective early warning. Traditional numerical air quality models require a high computational cost associated with running large-scale numerical simulations. In this work, we introduce a hybrid model (VAE-GAN) combining a generative adversarial network (GAN) with a variational autoencoder (VAE) to learn the dynamic ozone distributions in spatial and temporal spaces. The VAE-GAN model can not only decipher the complex nonlinear relationship between the inputs (the past states/ozone and meteorological factors) and outputs (ozone), but also provide ozone forecasts for a long lead-time beyond the training period. The performance of VAE-GAN is demonstrated in hourly and daily spatio-temporal ozone forecasts over China. The training datasets from 2013 to 2017 and validation datasets from 2018 to 2019 are the collection of data from the air quality reanalysis datasets. With the use of VAE, large dataset sizes are decreased by three orders of magnitude, enabling hourly and daily forecasts to be computed in seconds. Results show that the VAE-GAN achieves a reasonable accuracy in the prediction of both the spatial and temporal evolution patterns of hourly and daily ozone fields, as compared to the Nested Air Quality Prediction Modeling System (commonly used in China), the reanalysis data and observations during the validation period. Thus, the VAE-GAN is a cost-effective tool for large data-driven predictions, which can potentially reinforce air pollution prediction efforts in providing risk assessment and management in a timely manner.

关键词： ozone forecasting spatio-temporal generative adversarial network variational autoencoder deep learning

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Regularizing variational autoencoders for Molecular Graph Generation 1

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26th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS)

作者： Li, Xin Lyu, Xiaoqing Zhang, Hao Hu, Keqi Tang, Zhi Peking Univ Beijing Peoples R China Beijing Inst Technol Beijing Peoples R China China Univ Min & Technol Beijing Peoples R China

ISBN: (数字)9783030368029

ISBN: (纸本)9783030368029;9783030368012

Deep generative models for graphs are promising for being able to sidestep expensive search procedures in the huge space of chemical compounds. However, incorporating complex and non-differentiable property metrics into a generative model remains a challenge. In this work, we formulate a differentiable objective to regularize a variational autoencoder model that we design for graphs. Experiments demonstrate that the regularization performs excellently when used for generating molecules since it can not only improve the performance of objectives optimization task but also generate molecules with high quality in terms of validity and novelty.

关键词： variational autoencoder Regularization Molecule generation Graph generation

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Deep generative modelling of aircraft trajectories in terminal maneuvering areas

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MACHINE LEARNING WITH APPLICATIONS 2023年 11卷

作者： Krauth, Timothe Lafage, Adrien Morio, Jerome Olive, Xavier Waltert, Manuel Zurich Univ Appl Sci Ctr Aviat Winterthur Switzerland Univ Toulouse ONERA DTIS F-31055 Toulouse France Tech str 71 CH-8400 Winterthur Switzerland

Airspace design is subject to a multitude of constraints, which are mainly driven by the concern to keep the risk of mid-air collision below a target level of safety. For that purpose, Monte Carlo simulation methods can be applied to estimate aircraft conflict probability but require the accurate generation of artificial trajectories. Generative models allow to generate an infinite number of trajectories for air traffic procedures where only few observations are available. The generated trajectories must not only resemble observed trajectories in terms of statistical distributions but they should stay flyable and consider uncertainty due to weather, air traffic control, aircraft performances, or human factors. This paper focuses on the generation problem, and its main contribution lies in the adaptation of the variational autoencoder structure to the problem of 4 -dimensional aircraft trajectories modelling using Temporal Convolutional Networks and a prior distribution composed of a variational Mixture of Posteriors (VampPrior). The proposed model has been trained on trajectories in the Terminal Manoeuvre Area of Zurich airport, which have a particularly high degree of variability as air traffic controllers often take actions that deviate aircraft from the nominal approach procedure. The model has demonstrated great abilities to take into account such amount of uncertainty. Regarding metrics that evaluate the estimation of the statistical distribution of the observed trajectories, and the flyability of the generated ones, the proposed method outperforms traditional statistical methods by being able to generate more complex and realistic trajectories.

关键词： Air traffic management Deep Generative Models Multivariate time-series generation variational autoencoder Temporal Convolutional Networks

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Near-Real-Time Identification of Seismic Damage Using Unsupervised Deep Neural Network

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JOURNAL OF ENGINEERING MECHANICS 2022年第3期148卷

作者： Kim, Minkyu Song, Junho Seoul Natl Univ Dept Civil & Environm Engn Seoul 08826 South Korea

Prompt identification of structural damage is essential for effective postdisaster responses. To this end, this paper proposes a deep neural network (DNN)-based framework to identify seismic damage based on structural response data recorded during an earthquake event. The DNN in the proposed framework is constructed by variational autoencoder, which is one of the self-supervised DNNs that can construct the continuous latent space of the input data by learning probabilistic characteristics. The DNN is trained using the flexibility matrices obtained by operational modal analysis (OMA) of simulated structural responses of the target structure under the undamaged state. To consider the load-dependency of OMA results, the undamaged state of the structure is represented by the flexibility matrix, which is closest to that obtained from the measured seismic response in the latent space. The seismic damage of each member is then estimated based on the difference between the two matrices using the flexibility disassembly method. As a numerical example, the proposed method is applied to a 5-story, 5-bay steel frame structure for which structural analyses are first performed under artificial ground motions to create train and test datasets. The proposed framework is verified with the near-real-time simulation using ground motions of El Centro and Kobe earthquakes. The example demonstrates that the proposed DNN-based method can identify seismic damage accurately in near-real-time.

关键词： Seismic response Deep neural network (DNN) variational autoencoder Near-real-time Damage identification Operational modal analysis (OMA) Flexibility matrix Flexibility disassembly method

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Handling information loss of graph convolutional networks in collaborative filtering

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INFORMATION SYSTEMS 2022年 109卷

作者： Xiong, Xin Li, XunKai Hu, YouPeng Wu, YiXuan Yin, Jian Nanjing Univ Sch Artificial Intelligence 163 Xianlin Ave Nanjing Jiangsu Peoples R China Shandong Univ Sch Mech Elect & Informat Engn 180 Wenhua West Rd Weihai Shandong Peoples R China Zhejiang Univ Polytech Inst 269 Shixiang Rd Hangzhou Zhejiang Peoples R China

Collaborative filtering (CF) methods based on graph convolutional network (GCN) and autoencoder (AE) achieve outstanding performance. But the GCN-based CF methods suffer from information loss problems, which are caused by information lossy initialization and using low-order Chebyshev Polynomial to fit the graph convolution kernel. And the AE-based CF methods obtain the prediction results by reconstructing the user-item interaction matrix, which does not conduct deep excavation of the behavior patterns, resulting in the limited-expression ability. To solve the above problems, we propose variational autoencoder-Enhanced Graph Convolutional Network (VE-GCN) for CF. Specifically, we use a variational autoencoder (VAE) to compress the interactive behavior patterns as the prior information of GCN to achieve sufficient learning, thus alleviating the information lossy initialization problem. And then the generalized graph Laplacian convolution kernel is proposed in GCN to handle the high-frequency information loss problem caused by Chebyshev Polynomial fitting in the GCN-based CF. To the best of our knowledge, VE-GCN is a feasible method to handle the information loss problems mentioned above in GCN-based CF for the first time. Meanwhile, the structure of GCN is optimized by removing redundant feature transformation and nonlinear activation function, and using DenseGCN to complete multi-level information interaction. Experiments on four real-world datasets show that the VE-GCN achieves state-of-the-art performance. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Collaborative filtering Graph convolutional network Recommender system variational autoencoder

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Graph Regularized variational Ladder Networks for Semi-Supervised Learning

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IEEE ACCESS 2020年 8卷 206280-206288页

作者： Hu, Cong Song, Xiao-Ning Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi 214122 Jiangsu Peoples R China Minjiang Univ Fujian Prov Key Lab Informat Proc & Intelligent C Fuzhou 350121 Peoples R China

To tackle the problem of semi-supervised learning (SSL), we propose a new autoencoder-based deep model. Ladder networks (LN) is an autoencoder-based method for representation learning which has been successfully applied on unsupervised learning and semi-supervised learning. However, It ignores the manifold information of high-dimensional data and usually achieves unmeaning features which are very difficult to use in the subsequent tasks, such as prediction and recognition. To these issues, we proposed Graph Regularized variational Ladder Networks (GRVLN), which explicitly and implicitly employs the manifold structure of data. Our contributions can be summarized as two folds: (1) Graph regularization is used to build all decoder layers, which explicitly promotes the manifold learning via graph laplacian matrixs;(2) variational autoencoder is used as the backbone instead of traditional autoencoder in the encoder layers for implicitly learning the manifold structure of data distribution. Compared with ladder networks and other autoencoder-based methods, GRVLN achieves superior performance in semi-supervised classification tasks. Experimental results show that our method also has a comparable performance with state-of-the-art methods on several benchmark data sets.

关键词： Semisupervised learning Decoding Manifolds Deep learning Data models Encoding Training Semi-supervised learning ladder network manifold regularization graph laplacian variational autoencoder

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MDMaaS: Medical-Assisted Diagnosis Model as a Service With Artificial Intelligence and Trust

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第3期16卷 2102-2114页

作者： Guo, Kehua Ren, Sheng Bhuiyan, Md Zakirul Alam Li, Ting Liu, Dengchao Liang, Zhonghe Chen, Xiang Cent South Univ Sch Engn & Comp Sci Changsha 410083 Peoples R China Fordham Univ Dept Comp & Informat Sci Bronx NY 10458 USA Cent South Univ Dept Dermatol Xiangya Hosp Changsha 410083 Peoples R China

Artificial intelligence has achieved great success in the field of medical-assisted diagnosis, and a deep learning technology plays a very important role in medical image recognition. However, it usually takes medical institutions extra time, energy, and cost to obtain a credible and efficient deep learning model, which is not conducive to a wide range of applications, including medical image recognition and medical decision making. In this article, we propose a novel medical-assisted diagnosis model as a service (MDMaaS). Medical institutions can obtain and use the medical-assisted diagnosis models from the service providers directly;a model training and a model application in machine learning are assigned to a service provider and a consumer, respectively. We have designed a model acquisition method based on the conventional samples and small samples for MDMaaS providers, and we have also developed a trustworthy model-based recommendation method for MDMaaS consumers, which would help the medical institutions to obtain the reliable medical-assisted diagnosis models quickly and efficiently. Based on the MDMaaS, extensive experiments are performed to verify the effectiveness of the proposed method.

关键词： Medical diagnostic imaging Computational modeling Deep learning Cloud computing Training Medical services Deep learning medical-assisted diagnosis trust recommendation variational autoencoder

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variational Domain Adversarial Learning With Mutual Information Maximization for Speaker Verification

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2020年 28卷 2013-2024页

作者： Tu, Youzhi Mak, Man-Wai Chien, Jen-Tzung Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China Natl Chiao Tung Univ Dept Elect & Comp Engn Hsinchu 30010 Taiwan

Domain mismatch is a common problem in speaker verification (SV) and often causes performance degradation. For the system relying on the Gaussian PLDA backend to suppress the channel variability, the performance would be further limited if there is no Gaussianity constraint on the learned embeddings. This paper proposes an information-maximized variational domain adversarial neural network (InfoVDANN) that incorporates an InfoVAE into domain adversarial training (DAT) to reduce domain mismatch and simultaneously meet the Gaussianity requirement of the PLDA backend. Specifically, DAT is applied to produce speaker discriminative and domain-invariant features, while the InfoVAE performs variational regularization on the embedded features so that they follow a Gaussian distribution. Another benefit of the InfoVAE is that it avoids posterior collapse in VAEs by preserving the mutual information between the embedded features and the training set so that extra speaker information can be retained in the features. Experiments on both SRE16 and SRE18-CMN2 show that the InfoVDANN outperforms the recent VDANN, which suggests that increasing the mutual information between the embedded features and input features enables the InfoVDANN to extract extra speaker information that is otherwise not possible.

关键词： Speaker verification (SV) domain adaptation domain adversarial training variational autoencoder mutual information

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Convolutional auto encoders for sentence representation generation

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2020年第2期28卷 1135-+页

作者： Ceylan, Ali Mert Aytac, Vecdi Ege Univ Fac Engn Dept Comp Engn Izmir Turkey

In this study, we have proposed an alternative approach for sentence modeling problem. The difficulty of the choice of answer, the semantically related questions and the lack of syntactic closeness of the answers give rise to the difficulty of selecting the answer. The deep learning field has recently achieved a pivotal success in semantic analysis, machine translation, and text summaries. The essence of this work, inspired by the human orthographic processing mechanism and using multiple convolution filters with pre-rendered 2-Dimension (2D) representations of sentences, input or output size is to learn the basic features of the language without concerns. For this reason, the semantic relations in the sentence structure are learned by the convolutional variational auto-encoders first, and then the question and answer spaces learned by the auto-encoders are linked with proposed intermediate models. We have benchmarked five variations of our proposed model, which is based on variational Auto-Encoder with multiple latent spaces and able to achieve lower error rates than the baseline model, which is the base Convolutional LSTM.

关键词： Convolutional networks bi-gram n-gram question answering problem deep learning variational autoencoder sentence modeling

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