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

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2021年 196卷

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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A Unified Federated Learning Framework for Wireless Communications: towards Privacy, Efficiency, and Security

A Unified Federated Learning Framework for Wireless Communic...

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39th IEEE International Conference on Computer Communications (IEEE INFOCOM)

作者： Wen, Hui Wu, Yue Yang, Chenming Duan, Hancong Yu, Shui Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Univ Elect Sci & Technol China Natl Key Lab Commun Chengdu Peoples R China Univ Technol Sydney Sch Comp Sci Sydney NSW Australia

ISBN: (数字)9781728186955

ISBN: (纸本)9781728186955

Training high-quality machine learning models on distributed systems is a critical issue to achieve edge intelligence in wireless communications. Conventional data-driven machine learning approaches are infeasible due to non-IID data caused by privacy issues and the limited communication resources in wireless networks. Besides, considering the complex user identities, the training process also faces the challenges of Byzantine devices, which can inject poisoning information into models. In this paper, we propose a two-step federated learning framework, robust federated augmentation and distillation (RFA-RFD), to enable privacy-preserving, communication-efficient, and Byzantine tolerant on-device machine learning in wireless communications. RFA is a method to tackle the problem of non-IID local data, which firstly trains local data generators on edge devices, then trains a global generator in the cloud server according to the IID dataset generated by the uploaded local generators, and finally, devices rectify non-IID dataset by downloading the global generator. After obtaining IID local data in edge devices, RFD is implemented to improve the performance of local models, in which devices only share the local information of models' outputs to reduce communication overhead. By employing a detection and discard mechanism in both RFA and RFD, our framework achieves robustness to the influence of Byzantine devices. Experiments show the effectiveness of RFA-RFD on preserving privacy, correcting non-IID data, reducing communication overhead, and resisting Byzantine devices, without much loss of accuracy compared with existing state-of-the-art methods.

关键词： Federated learning data augmentation distillation conditional variational autoencoder communication efficient privacy preserving Byzantine robustness

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Towards a system for automatic traffic sound event detection 28

Towards a system for automatic traffic sound event detection

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28th Telecommunications Forum (TELFOR)

作者： Chavdar, Marko Gerazov, Branislav Ivanovski, Zoran Kartalov, Tomislav ITEK Syst Gevgelija North Macedonia Ss Cyril & Methodius Univ Fac Elect Engn & Informat Technol Rugjer Boshkovikj 18 Skopje 1000 North Macedonia

ISBN: (纸本)9780738142432

Intelligent Traffic Surveillance systems have helped improve road safety through ensuring timely response to events such as traffic accidents and congestion. Our aim is to devise a robust system capable of traffic audio events detection in a real-life environment. At the core of this system is a deep learning model capable of detecting anomalous events and their classification based on their acoustic waveform. We present the results of a series of experiments designed to optimize the architecture of this model based on different algorithms for audio processing. The results show that the designed model has competitive performance to approaches published in literature.

关键词： Sound event detection ITS Traffic surveillance system conditional variational autoencoder Convolutional neural network

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Attributed Network Embedding with Community Preservation 7

Attributed Network Embedding with Community Preservation

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7th IEEE International Conference on Data Science and Advanced Analytics (DSAA)

作者： Huang, Tong Zhou, Lihua Wang, Lizhen Du, Guowang Lu, Kevin Yunnan Univ Sch Informat Kunming Yunnan Peoples R China Brunel Univ Uxbridge UB8 3PH Middx England

ISBN: (纸本)9781728182063

Network embedding (NE) is a method that maps nodes in a network into a low-dimensional and continuous vector space while maintains inherent features of the network. Most existing algorithms for NE focus on one or two of the aspects of topological structure, node attributes or community structure information, but without integrating the three in a unified framework. In this study, we develop a deep neural network-based framework for Attributed Network Embedding with Community Preservation (ANECP), which simultaneously incorporates the topological structure, node attributes as well as community structure together to obtain the low-dimensional distributed representations of nodes in the network. The use of deep neural networks captures the underlying high non-linearity in both topology and attribute information, while the incorporation of the community structure resolves the issues of data sparsity from microscopic perspective. Consequently, the obtained node representations can preserve proximity and discriminative. We conducted experimental studies using six real-world datasets. The experimental results show that proposed ANECP has superior performance over the existing methods.

关键词： Network embedding topological structure node attribute community structure conditional variational autoencoder

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Inference of electromagnetic system behavior in the presence of variability

Inference of electromagnetic system behavior in the presence...

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作者： Ma, Xiao University of Illinois – Urbana-Champaign

学位级别：博士

This thesis contributes to and advances the state-of-the-art of the analysis of stochastic electromagnetic-circuit systems by applying novel statistical inference and generative modeling techniques to the extraction of statistical models of system responses. Machine learning based and physics based techniques are introduced to address the limitations of existing methods by incorporating dimensionality reduction into the model training procedure, enabling the enforcement of certain physical constraints, making the modeling procedure more flexible by accepting additional data to capture dependency, and taking advantage of universal statistics to reduce the sample requirement. Specifically, for the first time, a variational autoencoder based method is used for the generative modeling of high-dimensional S-parameters data. The generation accuracy is shown to be superior to that of existing methods. The passive variational autoencoder, a variational autoencoder with modified decoder architecture, is introduced to enforce physical constraints like passivity. The generated S-parameters are shown to be physically consistent. The generation accuracy and efficiency of the proposed method are assessed through comparison to those of existing methods. When a system is decomposed into multiple subsystems, the dependency between responses of subsystems needs to be captured but is often neglected. A conditional variational autoencoder is introduced for the capturing of such dependency. Additional control variables are used to account for the randomness external to a subsystem. The conditional variational autoencoder based generative model demonstrates an advantage in terms of generation accuracy as compared to its standard version, which ignores the dependency between subsystems. The random coupling model is a physics based generative model applicable to electromagnetic enclosures under wave chaos condition. The low-frequency limit of the random coupling model based generative mode

关键词： Stochastic electromagnetic-circuit analysis Statistical inference Generative modeling S-parameters variational autoencoder Physical constraints Passivity Dependency between subsystems conditional variational autoencoder Universal statistics Random coupling model Wave chaos

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Learning a Probabilistic Model for Diffeomorphic Registration

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2019年第9期38卷 2165-2176页

作者： Krebs, Julian Delingette, Herve Mailhe, Boris Ayache, Nicholas Mansi, Tommaso Univ Cote Azur INRIA Epione Team F-06902 Sophia Antipolis France Siemens Healthineers Digital Serv Digital Technol & Innovat Princeton NJ 08540 USA

We propose to learn a low-dimensional probabilistic deformation model from data which can be used for the registration and the analysis of deformations. The latent variable model maps similar deformations close to each other in an encoding space. It enables to compare deformations, to generate normal or pathological deformations for any new image, or to transport deformations from one image pair to any other image. Our unsupervised method is based on the variational inference. In particular, we use a conditional variational autoencoder network and constrain transformations to be symmetric and diffeomorphic by applying a differentiable exponentiation layer with a symmetric loss function. We also present a formulation that includes spatial regularization such as the diffusion-based filters. In addition, our framework provides multi-scale velocity field estimations. We evaluated our method on 3-D intra-subject registration using 334 cardiac cine-MRIs. On this dataset, our method showed the state-of-the-art performance with a mean DICE score of 81.2% and a mean Hausdorff distance of 7.3 mm using 32 latent dimensions compared to three state-of-the-art methods while also demonstrating more regular deformation fields. The average time per registration was 0.32 s. Besides, we visualized the learned latent space and showed that the encoded deformations can be used to transport deformations and to cluster diseases with a classification accuracy of 83% after applying a linear projection.

关键词： Deformable registration probabilistic encoding deep learning conditional variational autoencoder latent variable model deformation transport

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NVSRN: A Neural variational Scaling Reasoning Network for Initiative Response Generation 19

NVSRN: A Neural Variational Scaling Reasoning Network for In...

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19th IEEE International Conference on Data Mining (ICDM)

作者： Chang, Jinxin He, Ruifang Xu, Haiyang Han, Kun Wang, Longbiao Li, Xiangang Dang, Jianwu Tianjin Univ Tianjin Key Lab Cognit Comp & Applicat Tianjin Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Didi Chuxing AI Labs Beijing Peoples R China Japan Adv Inst Sci & Technol Nomi Ishikawa Japan

ISBN: (纸本)9781728146034

Open-domain multi-turn dialogue systems are booming in human-machine interactions, which encourage to chat actively and freely in an intelligent natural way. Previous generative conversational models usually employ a single and deterministic encoder-decoder framework to model the semantic consistency between the context and corresponding response. However, they neglect the various dialog patterns (we denote the regularity of topic shifting as dialog pattern) in the conversations, leading to uninformative, non-initiative yet plausible responses. Although the existing variational methods have improved the response diversity to some extent by introducing a global variability into the generative process, they fail to simulate the transfer between topics with directional information due to the weak interpretability of the Gaussian-distributed latent variables. In this paper, we propose a novel Neural variational Scaling Reasoning Network (NVSRN) for initiative response generation. To this end, our approach has two core ingredients: neural dialog pattern reasoner (reasoner) and topic scaling mechanism. Specifically, inspired by the advantage of von Mises-Fisher (vMF) distribution modeling the directional data (e.g., the topic transfer state), we employ it as the latent space of the reasoner to explore the regularity of topic shifting, which is then used to reason the topic of response. Based on this, a topic scaling mechanism is designed to control the transfer degree of topic in the response generator. The experimental results on two large dialog datasets demonstrate that the proposed model outperforms state-of-the-art baselines. The human evaluation shows the proposed model can produce more informative and initiative responses actively.

关键词： dialogue generation topic shifting von MisesFisher distribution conditional variational autoencoder topic scaling mechanism

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CVAE-Attention: CVAE based Semi-Supervised Sentiment Classification using Attention 19

CVAE-Attention: CVAE based Semi-Supervised Sentiment Classif...

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International Conference on Pattern Recognition and Artificial Intelligence (PRAI)

作者： Yu, Jifang Wu, Jiangqin Wei, Baogang Liu, Yuanyuan Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Shandong Univ Sch Comp Sci & Technol Jinan Peoples R China

ISBN: (纸本)9781450372312

Text sentiment classification is an important domain in NLP, and the related technical research has been mature. The sentiment classification of text with the "but" contrastive marker is a challenging problem. In this paper, a semi-supervised framework based on conditional variational autoencoder using attention, called CVAE-Attention, is proposed for sentiment classification. In the CVAE-Attention framework, the attention mechanism is introduced to cope with the contrastive structure. The latent semantic information of the clause after "but" (but-clause) is extracted through the attention model, and is incorporated into the generative model to enlarge the effect of the but-clause. Experiments show that the proposed method is effective compared with other state-of-the-art semi-supervised methods.

关键词： conditional variational autoencoder Semi-supervised Learning Sentiment Classification Attention Mechanism

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variational Shape Completion for Virtual Planning of Jaw Reconstructive Surgery 22nd

Variational Shape Completion for Virtual Planning of Jaw Rec...

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10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Abdi, Amir H. Pesteie, Mehran Prisman, Eitan Abolmaesumi, Purang Fels, Sidney Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9783030322540;9783030322533

The premorbid geometry of the mandible is of significant relevance in jaw reconstructive surgeries and occasionally unknown to the surgical team. In this paper, an optimization framework is introduced to train deep models for completion (reconstruction) of the missing segments of the bone based on the remaining healthy structure. To leverage the contextual information of the surroundings of the dissected region, the voxel-weighted Dice loss is introduced. To address the non-deterministic nature of the shape completion problem, we leverage a weighted multi-target probabilistic solution which is an extension to the conditional variational autoencoder (CVAE). This approach considers multiple targets as acceptable reconstructions, each weighted according to their conformity with the original shape. We quantify the performance gain of the proposed method against similar algorithms, including CVAE, where we report statistically significant improvements in both deterministic and probabilistic paradigms. The probabilistic model is also evaluated on its ability to generate anatomically relevant variations for the missing bone. As a unique aspect of this work, the model is tested on real surgical cases where the clinical relevancy of its reconstructions and their compliance with surgeon's virtual plan are demonstrated as necessary steps towards clinical adoption.

关键词： conditional variational autoencoder 3D shape completion V-Net Mandible reconstruction

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Topic-Aware Automatic Snippet Generation for Resolving Multiple Meaning on Web Search Result 9

Topic-Aware Automatic Snippet Generation for Resolving Multi...

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9th International Conference on Awareness Science and Technology (iCAST)

作者： Abe, Hiroyuki Matsuhara, Masafumi Chakraborty, Goutam Mabuchi, Hiroshi Iwate Prefectural Univ Grad Sch Software & Informat Sci Takizawa Iwate Japan Iwate Prefectural Univ Fac Software & Informat Sci Takizawa Iwate Japan

ISBN: (纸本)9781538658260

In recent years, the amount of information on the Web is growing exponentially with the spread of the Internet. We generally use search engines to search for the intended information. However, the search engine displays the Web pages including the entered search query in list format. It is difficult for the user to find out the intended information if the entered search query is a word whose meaning depends on the situation and location of the user. It needs the intended information to the multiple hidden topics. In this research, we classify Web search results based on each topic. The topic is defined as the latent meaning, and the contents included in the word. Moreover, our method displays automatically generated snippets for each topic with the Web search results to the user. It is easy to find required information from snippets, even though the intended information is ambiguous. It first classifies the Web search results by Latent Dirichlet Allocation (LDA) which is a major topic model method. It then generates the snippets using conditional variational autoencoder (conditional VAE) based on the clustering of We search results. It is expected that using LDA for the clustering will group the Web search result according to the latent meanings of the search query. Also, we expect that proper snippets will be generated for each topic by conditional VAE. In this paper, we show that LDA is effective for the clustering of Web search results. Moreover, the snippets generated by conditional VAE is able to generate sentences considered each topic.

关键词： Information Retrieval Web Search Result Clustering Latent Dirichlet Allocation conditional variational autoencoder Long Short-Term Memory

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