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43rd Chinese Control Conference, CCC 2024

作者： Tang, Xiaochu Tao, Na Zhang, Yi Li, Yuan Shenyang Aerospace University School of Automation Shenyang China Shenyang University of Chemical Technology College of Information Engineering Shenyang China

ISBN: (纸本)9789887581581

To ensure the safety and reliability of complex industrial processes are very important. Therefore, extracting multiple features of data effectively is a great significance to improve the accuracy of modeling for fault diagnosis. Dynamic, uncertainty and nonlinearity are main characteristics of industrial process data. However, it is challenging for modeling to extract multiple features of process data at the same time. In this paper, an integrated nonlinear dynamic system model is proposed for fault diagnosis based on variational autoencoder-linear dynamic system (VAE-LDS). First, the deep learning algorithm variational autoencoder (VAE) is used to extract the nonlinear data feature and learn the potential representation of data. Furthermore, the VAE model is embedded into the linear dynamic system (LDS) so that the dynamics and uncertainty underlying data can be extracted simultaneously. In this way, A comprehensive model integrating multiple features can be established. Finally, the proposed method is applied to the TE process for fault diagnosis comparing with other methods. The results show the proposed method has superior performance. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Data-driven Fault Detection Fault Diagnosis Linear Dynamic System variational autoencoder

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An Ultrasound-Based Surveillance System for Bathroom Posture and Location Estimation

An Ultrasound-Based Surveillance System for Bathroom Posture...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Sato, Shun Ohara, Ryotaro Kamarulzaman, M. Shahrul Amir Yasuda, Yuto Izumi, Shintaro Kawaguchi, Hiroshi Kobe University School of System Informatics Kobe Japan Kobe University School of Science Technology and Innovation Kobe Japan

ISBN: (纸本)9798350324136

Bathrooms can be slippery, increasing the risk of falling. In addition, because people enter the bathroom alone, it is difficult to detect accidents immediately when they occur. Therefore, a system is required to quickly detect accidents and call for assistance. In this study, the location and the posture of the bather are important attributes. We propose a method to estimate the location of a bather using ultrasound correlation values as input to a deep neural network and to estimate the bather's posture. Therefore, the mean absolute error achieved 11.7 cm for location estimation, and accuracy achieved 95.3% for posture estimation in the experiment involving an individual moving throughout the bathroom. © 2024 IEEE.

关键词： Imaging Location Estimation Posture Estimation Ultrasound variational autoencoder

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Hierarchical and Self-Attended Sequence autoencoder

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第9期44卷 4975-4986页

作者： Chien, Jen-Tzung Wang, Chun-Wei Natl Chiao Tung Univ Dept Elect & Comp Engn Hsinchu 30010 Taiwan

It is important and challenging to infer stochastic latent semantics for natural language applications. The difficulty in stochastic sequential learning is caused by the posterior collapse in variational inference. The input sequence is disregarded in the estimated latent variables. This paper proposes three components to tackle this difficulty and build the variational sequence autoencoder (VSAE) where sufficient latent information is learned for sophisticated sequence representation. First, the complementary encoders based on a long short-term memory (LSTM) and a pyramid bidirectional LSTM are merged to characterize global and structural dependencies of an input sequence, respectively. Second, a stochastic self attention mechanism is incorporated in a recurrent decoder. The latent information is attended to encourage the interaction between inference and generation in an encoder-decoder training procedure. Third, an autoregressive Gaussian prior of latent variable is used to preserve the information bound. Different variants of VSAE are proposed to mitigate the posterior collapse in sequence modeling. A series of experiments are conducted to demonstrate that the proposed individual and hybrid sequence autoencoders substantially improve the performance for variational sequential learning in language modeling and semantic understanding for document classification and summarization.

关键词： Decoding Stochastic processes Training Semantics Recurrent neural networks Natural languages Data models Sequence generation recurrent neural network variational autoencoder hierarchical model self attention

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Enhancing variational Generation Through Self-Decomposition

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IEEE ACCESS 2022年 10卷 67510-67520页

作者： Asperti, Andrea Bugo, Laura Filippini, Daniele Univ Bologna Dept Informat Sci & Engn DISI I-40126 Bologna Italy

In this article we introduce the notion of Split variational autoencoder (SVAE), whose output (x) over cap is obtained as a weighted sum sigma circle dot (x) over cap (1) + (1 - sigma) circle dot (x) over cap (2) of two generated images (x) over cap (1);(x) over cap (2), and sigma is a learned compositional map. The composing images (x) over cap (1);(x) over cap (2), as well as the sigma-map are automatically synthesized by the model. The network is trained as a usual variational autoencoder with a negative loglikelihood loss between training and reconstructed images. No additional loss is required for (x) over cap (1);(x) over cap (2) or sigma, neither any form of human tuning. The decomposition is nondeterministic, but follows two main schemes, that we may roughly categorize as either "syntactic'' or "semantic.'' In the first case, the map tends to exploit the strong correlation between adjacent pixels, splitting the image in two complementary high frequency sub-images. In the second case, the map typically focuses on the contours of objects, splitting the image in interesting variations of its content, with more marked and distinctive features. In this case, according to empirical observations, the Frechet Inception Distance (FID) of (x) over cap (1) and (x) over cap (2) is usually lower (hence better) than that of Ox, that clearly suffers from being the average of the former. In a sense, a SVAE forces the variational autoencoder to make choices, in contrast with its intrinsic tendency to average between alternatives with the aim to minimize the reconstruction loss towards a specific sample. According to the FID metric, our technique, tested on typical datasets such as Mnist, Cifar10 and CelebA, allows us to outperform all previous purely variational architectures (not relying on normalization flows).

关键词： Deep learning generative modeling multi-layer neural networks representation learning unsupervised learning variational autoencoder

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Reinforcement Learning With Vision-Proprioception Model for Robot Planar Pushing

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FRONTIERS IN NEUROROBOTICS 2022年第0期16卷 829437页

作者： Cong, Lin Liang, Hongzhuo Ruppel, Philipp Shi, Yunlei Goerner, Michael Hendrich, Norman Zhang, Jianwei Univ Hamburg Dept Informat TAMS Grp Hamburg Germany

We propose a vision-proprioception model for planar object pushing, efficiently integrating all necessary information from the environment. A variational autoencoder (VAE) is used to extract compact representations from the task-relevant part of the image. With the real-time robot state obtained easily from the hardware system, we fuse the latent representations from the VAE and the robot end-effector position together as the state of a Markov Decision Process. We use Soft Actor-Critic to train the robot to push different objects from random initial poses to target positions in simulation. Hindsight Experience replay is applied during the training process to improve the sample efficiency. Experiments demonstrate that our algorithm achieves a pushing performance superior to a state-based baseline model that cannot be generalized to a different object and outperforms state-of-the-art policies which operate on raw image observations. At last, we verify that our trained model has a good generalization ability to unseen objects in the real world.

关键词： reinforcement learning robot manipulation planar pushing multimodal variational autoencoder Soft Actor-Critic

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Generative Feature Extraction From Sentinel 1 and 2 Data for Prediction of Forest Aboveground Biomass in the Italian Alps

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 4755-4771页

作者： Naik, Parth Dalponte, Michele Bruzzone, Lorenzo Univ Trento Dept Informat Engn & Comp Sci I-38123 Trento Italy Fdn Edmund Mach Res & Innovat Ctr I-38098 San Michele All Adige Italy

Aboveground biomass (AGB) is an important forest attribute directly linked to the forest carbon pool. The use of satellite remote sensing (RS) data has increased for AGB prediction due to their large footprint and low-cost availability. However, they have been limited due to saturation effect that leads to low prediction precision. In this article, we propose an innovative and dynamic architecture based on generative neural network that extracts target oriented generative features for predicting forest AGB using satellite RS data. These features are more resilient to mixed forest types and geographical conditions as compared to the traditional features and models. The effectiveness of the proposed features was assessed by experiments performed using multispectral, synthetic aperture radar, and combined dual-source datasets. The proposed model achieved best performance in terms of precision, model agreement, and overfitting as compared to the other conventional models for all analyzed datasets. The t-distributed stochastic neighbor embedding scatterplots of the generative features clearly show one dimension of the feature space associated with the target AGB. Feature importance analysis indicated that the produced generative features were more significant than the conventional analytical features. Also, the model provided a robust framework for homogeneous fusion of multisensor features from satellite RS data for predicting AGB.

关键词： Forestry Feature extraction Synthetic aperture radar Data models Vegetation Satellite broadcasting Mathematical models Aboveground biomass (AGB) feature extraction feature fusion generative features variational autoencoder

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Investigating Deep Learning Based Breast Cancer Subtyping Using Pan-Cancer and Multi-Omic Data

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第1期19卷 121-134页

作者： Cristovao, Francisco Cascianelli, Silvia Canakoglu, Arif Carman, Mark Nanni, Luca Pinoli, Pietro Masseroli, Marco Politecn Milan Dipartimento Elettron Informaz & Bioingn I-20133 Milan Italy

Breast Cancer comprises multiple subtypes implicated in prognosis. Existing stratification methods rely on the expression quantification of small gene sets. Next Generation Sequencing promises large amounts of omic data in the next years. In this scenario, we explore the potential of machine learning and, particularly, deep learning for breast cancer subtyping. Due to the paucity of publicly available data, we leverage on pan-cancer and non-cancer data to design semi-supervised settings. We make use of multi-omic data, including microRNA expressions and copy number alterations, and we provide an in-depth investigation of several supervised and semi-supervised architectures. Obtained accuracy results show simpler models to perform at least as well as the deep semi-supervised approaches on our task over gene expression data. When multi-omic data types are combined together, performance of deep models shows little (if any) improvement in accuracy, indicating the need for further analysis on larger datasets of multi-omic data as and when they become available. From a biological perspective, our linear model mostly confirms known gene-subtype annotations. Conversely, deep approaches model non-linear relationships, which is reflected in a more varied and still unexplored set of representative omic features that may prove useful for breast cancer subtyping.

关键词： Deep learning genomics multi-omics semi supervised learning variational autoencoder

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Latent User Intent Modeling for Sequential Recommenders

Latent User Intent Modeling for Sequential Recommenders

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32nd World Wide Web Conference (WWW)

作者： Chang, Bo Karatzoglou, Alexandros Wang, Yuyan Xu, Can Chi, Ed H. Chen, Minmin Google Inc Mountain View CA 94043 USA

ISBN: (纸本)9781450394161

Sequential recommender models are essential components of modern industrial recommender systems. These models learn to predict the next items a user is likely to interact with based on his/her interaction history on the platform. Most sequential recommenders however lack a higher-level understanding of user intents, which often drive user behaviors online. Intent modeling is thus critical for understanding users and optimizing long-term user experience. We propose a probabilistic modeling approach and formulate user intent as latent variables, which are inferred based on user behavior signals using variational autoencoders (VAE). The recommendation policy is then adjusted accordingly given the inferred user intent. We demonstrate the efectiveness of the latent user intent modeling via ofine analyses as well as live experiments on a large-scale industrial recommendation platform.

关键词： recommender systems intent modeling variational autoencoder

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Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representation 23

Assisting Clinical Decisions for Scarcely Available Treatmen...

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29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

作者： Xue, Bing Said, Ahmed Sameh Xu, Ziqi Liu, Hanyang Shah, Neel Yang, Hanqing Payne, Philip Lu, Chenyang Washington Univ McKelvey Sch Engn St Louis MO 63110 USA Washington Univ Sch Med St Louis MO USA

ISBN: (纸本)9798400701030

Extracorporeal membrane oxygenation (ECMO) is an essential life-supporting modality for COVID-19 patients who are refractory to conventional therapies. However, the proper treatment decision has been the subject of significant debate and it remains controversial about who benefits from this scarcely available and technically complex treatment option. To support clinical decisions, it is a critical need to predict the treatment need and the potential treatment and no-treatment responses. Targeting this clinical challenge, we propose Treatment variational autoencoder (TVAE), a novel approach for individualized treatment analysis. TVAE is specifically designed to address the modeling challenges like ECMO with strong treatment selection bias and scarce treatment cases. TVAE conceptualizes the treatment decision as a multi-scale problem. We model a patient's potential treatment assignment and the factual and counterfactual outcomes as part of their intrinsic characteristics that can be represented by a deep latent variable model. The factual and counterfactual prediction errors are alleviated via a reconstruction regularization scheme together with semi-supervision, and the selection bias and the scarcity of treatment cases are mitigated by the disentangled and distribution-matched latent space and the label-balancing generative strategy. We evaluate TVAE on two real-world COVID-19 datasets: an international dataset collected from 1651 hospitals across 63 countries, and a institutional dataset collected from 15 hospitals. The results show that TVAE outperforms state-of-the-art treatment effect models in predicting both the propensity scores and factual outcomes on heterogeneous COVID-19 datasets. Additional experiments also show TVAE outperforms the best existing models in individual treatment effect estimation on the synthesized IHDP benchmark dataset.

关键词： Machine Learning for Healthcare Causal Inference Representation Learning Semi-supervised Learning Treatment Effect Estimation COVID Analysis Generative AI Deep Latent Variable Models variational autoencoder

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Cross-Situational Word Learning in Disentangled Latent Space

Cross-Situational Word Learning in Disentangled Latent Space

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IEEE International Conference on Development and Learning (ICDL)

作者： Matsui, Yuta Taniguchi, Akira Hagiwara, Yoshinobu Taniguchi, Tadahiro Ritsumeikan Univ Grad Sch Informat Sci & Engn Kusatsu Shiga Japan Ritsumeikan Univ Coll Informat Sci & Engn Kusatsu Shiga Japan Ritsumeikan Univ Res Org Sci & Technol Kusatsu Shiga Japan

ISBN: (纸本)9781665470759

Cross-situational word learning (CSL) is a fast and efficient method for humans to acquire word meanings. Many studies have replicated human CSL using computational models. Among these, cross-situational learning with Bayesian probabilistic generative model (CSL-PGM) can estimate word meanings from observations that include multiple attributes, such as color and shape. However, as CSL-PGM receives observations for each attribute on a separate channel, it cannot perform CSL for images with multiple attributes. Therefore, we introduce a disentangled representation that captures the attributes within an image. Additionally, we propose CSL+VAE, which integrates CSL-PGM and a beta-VAE to obtain a disentangled representation in an unsupervised manner. CSL+VAE can discover attributes hidden in images and word sequences and infer the meanings of words. Additionally, it can obtain a more disentangled representation using a learning framework wherein both models share parameters. During experiments, the model was trained on a set of images comprising five attributes and one to five words describing them. The results showed that 99.9% of the words correctly estimated the attributes of the words and correctly estimated the correspondence between the image features and the words. The proposed model also outperformed existing multimodal models in inferring images from word sequences, achieving an accuracy of 0.870.

关键词： cross-situational word learning disentanglement variational autoencoder

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