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ADVANCED PHYSICS RESEARCH 2025年第2期4卷

作者： Bhupati, Mahesh Mall, Abhishek Kumar, Anshuman Jha, Pankaj K. Indian Inst Technol Dept Phys Lab Opt Quantum Mat LOQM Mumbai 400076 India Max Planck Inst Struct & Dynam Matter D-22761 Hamburg Germany Ctr Free Electron Laser Sci D-22761 Hamburg Germany Indian Inst Technol Ctr Excellence Quantum Informat Computat Sci & Tec Mumbai 400076 India Syracuse Univ Dept Elect Engn & Comp Sci Quantum Technol Lab QLT Syracuse NY 13210 USA

Advancements in optical quantum technologies have been enabled by the generation, manipulation, and characterization of light, with identification based on its photon statistics. However, characterizing light and its sources through single photon measurements requires efficient detectors and longer measurement times to obtain high-quality photon statistics. Here, a deep learning-based variational autoencoder (VAE) method is introduced for classifying single photon added coherent state (SPACS), single photon added thermal state (SPATS), and mixed states between coherent and SPACS as well as between thermal and SPATS of light. The semi-supervised learning-based VAE efficiently maps the photon statistics features of light to a lower dimension, enabling quasi-instantaneous classification with low average photon counts. The proposed VAE method is robust and maintains classification accuracy in the presence of losses inherent in an experiment, such as finite collection efficiency, non-unity quantum efficiency, finite number of detectors, etc. Additionally, leveraging the transfer learning capabilities of VAE enables successful classification of data of any quality using a single trained model. It is envisioned that such a deep learning methodology will enable better classification of quantum light and light sources even in poor detection. A deep learning-assisted variational autoencoder (VAE) is used to classify quantum and classical states of light, such as single photon-added coherent source (SPACS), single photon-added thermal source (SPATS), and their mixtures. The semi-supervised VAE efficiently maps photon statistics to a low-dimensional latent space, allowing for rapid and accurate classification even with minimal photon counts and experimental losses. Transfer learning improves robustness and adaptability to varying data quality. image

关键词： deep learning spacs spats variational autoencoder

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Online nonlinear data reconciliation to enhance nonlinear dynamic process monitoring using conditional dynamic variational autoencoder networks with particle filters

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2024年 253卷

作者： Chiu, Kuanhsuan Chen, Junghui Zhang, Zhengjiang Chung Yuan Christian Univ Dept Chem Engn Taoyuan 32023 Taiwan Wenzhou Univ Natl Local Joint Engn Lab Digitalize Elect Design Wenzhou 325035 Peoples R China

In the chemical plants, data-driven process monitoring serves as a vital tool to ensure product quality and maintain production line safety. However, the accuracy of monitoring hinges directly upon the quality of process data. Given the inherently slow and complex nature of chemical processes, coupled with the potential for gross errors in process data leading to inaccuracies in model predictions, this paper proposes a method called Conditional Dynamic variational autoencoder combined with a Particle Filter (CDVAE-PF) for data reconciliation and subsequent process monitoring. CDVAE-PF leverages the capabilities of Conditional Dynamic variational autoencoder (CDVAE) to effectively model chemical process data in the presence of noise. This probabilistic model serves as the foundation for the Particle Filter (PF), which is employed for data reconciliation. Moreover, CDVAE-PF incorporates mechanisms to detect and rectify gross errors in process data, further enhancing its efficacy in data reconciliation. Subsequently, monitoring indices based on CDVAE are established to facilitate process monitoring. Through numerical simulations of a two-to-one variables Continuous Stirred Tank Reactor (CSTR) example and a fifteen-to-one variables dichloroethane distillation process from an actual chemical plant, CDVAE-PF demonstrates its effectiveness by reducing mean absolute error to 7.8 % and 12.8 % respectively in gross error data reconciliation. Moreover, in terms of monitoring performance, CDVAE-PF successfully mitigates misjudgments caused by gross errors, thereby significantly enhancing the reliability of process monitoring in chemical plants.

关键词： Data reconciliation variational autoencoder Particle filter Process monitoring

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An Invariant Representation of Coupler Curves Using a variational autoencoder: Application to Path Synthesis of Four-Bar Mechanisms

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2024年第1期24卷 011008-011008页

作者： Nurizada, Anar Purwar, Anurag SUNY Stony Brook Dept Mech Engn Comp Aided Design & Innovat Lab Stony Brook NY 11794 USA

This paper focuses on the representation and synthesis of coupler curves of planar mechanisms using a deep neural network. While the path synthesis of planar mechanisms is not a new problem, the effective representation of coupler curves in the context of neural networks has not been fully explored. This study compares four commonly used features or representations of four-bar coupler curves: Fourier descriptors, wavelets, point coordinates, and images. The results demonstrate that these diverse representations can be unified using a generative AI framework called variational autoencoder (VAE). This study shows that a VAE can provide a standalone representation of a coupler curve, regardless of the input representation, and that the compact latent dimensions of the VAE can be used to describe coupler curves of four-bar linkages. Additionally, a new approach that utilizes a VAE in conjunction with a fully connected neural network to generate dimensional parameters of four-bar linkage mechanisms is proposed. This research presents a novel opportunity for the automated conceptual design of mechanisms for robots and machines.

关键词： planar four-bar linkage path synthesis Fourier descriptors wavelets variational autoencoder neural networks machine learning deep learning

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Stochastic-Expert variational autoencoder for Collaborative Filtering 22

Stochastic-Expert Variational Autoencoder for Collaborative ...

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31st ACM Web Conference (WWW)

作者： Cho, Yoon-Sik Oh, Min-hwan Chung Ang Univ Seoul South Korea Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9781450390965

Motivated by the recent successes of deep generative models used for collaborative filtering, we propose a novel framework of VAE for collaborative filtering using multiple experts and stochastic expert selection, which allows the model to learn a richer and more complex latent representation of user preferences. In our method, individual experts are sampled stochastically at each user-item interaction which can effectively utilize the variability among multiple experts. While we propose this framework in the context of collaborative filtering, the proposed stochastic expert technique can be used to enhance VAEs in general beyond the application of collaborative filtering. Hence, this novel technique can be of independent interest. We comprehensively evaluate our proposed method, Stochastic-Expert variational autoencoder ( SE-VAE) on numerical experiments on the real-world benchmark datasets from MovieLens and Netflix and show that it consistently outperforms the existing state-of-the-art methods across all metrics. Our proposed stochastic expert framework is generic and adaptable to any VAE architecture. The experimental results show that the adaptations to various architectures provided performance gains over the existing methods.

关键词： Collaborative Filtering variational autoencoder Deep Generative Models Neural Networks Recommender Systems variational Inference

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ContrastVAE: Contrastive variational autoencoder for Sequential Recommendation 22

ContrastVAE: Contrastive Variational AutoEncoder for Sequent...

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31st ACM International Conference on Information and Knowledge Management (CIKM)

作者： Wang, Yu Zhang, Hengrui Liu, Zhiwei Yang, Liangwei Yu, Philip S. Univ Illinois Chicago IL 60607 USA Salesforce San Francisco CA USA

ISBN: (纸本)9781450392365

Aiming at exploiting the rich information in user behaviour sequences, sequential recommendation has been widely adopted in real-world recommender systems. However, current methods suffer from the following issues: 1) sparsity of user-item interactions, 2) uncertainty of sequential records, 3) long-tail items. In this paper, we propose to incorporate contrastive learning into the framework of variational autoencoders to address these challenges simultaneously. Firstly, we introduce ContrastELBO, a novel training objective that extends the conventional single-view ELBO to two-view case and theoretically builds a connection between VAE and contrastive learning from a two-view perspective. Then we propose Contrastive variational autoencoder (ContrastVAE in short), a two-branched VAE model with contrastive regularization as an embodiment of ContrastELBO for sequential recommendation. We further introduce two simple yet effective augmentation strategies named model augmentation and variational augmentation to create a second view of a sequence and thus making contrastive learning possible. Experiments on four benchmark datasets demonstrate the effectiveness of ContrastVAE and the proposed augmentation methods. Codes are available at https://***/YuWang1024/ContrastVAE

关键词： Sequential Recommendation variational autoencoder Contrastive Learning

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Noise-robust Early Detection of Cooling Fan Deterioration with a variational autoencoder-based Method 9

Noise-robust Early Detection of Cooling Fan Deterioration wi...

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9th International Conference on Condition Monitoring and Diagnosis (CMD)

作者： Yanagihashi, Hiroyuki Sudo, Takashi Toshiba Co Ltd Corp Res & Dev Ctr Kawasaki Kanagawa Japan

ISBN: (纸本)9784886864314

Electrical equipment usually requires air cooling by fans, and it is important to detect early signs of fan deterioration. However, when using sensors to detect deterioration of fans in the real world, the sensor signal is often mixed with external noise. In this paper, we analyze the deterioration trends in the operating sounds of cooling fans actually used for 3 and 7 years in uninterruptible power supplies. Furthermore, we propose a deep learning-based method for noise-robust early detection of deterioration based on fan operating sounds called variational autoencoder-based deterioration estimation (VAE-DE). Existing anomaly detection methods based on autoencoders often react to external noise mixed into deterioration trend, which causes false positives. While another conventional method using variational autoencoders has the property of being robust against noise, it is not reactive to slight sound changes that accompany fan deterioration. If a deterioration trend is known, noise-robust early detection of deterioration can be achieved through learning using a loss function that includes a term for separation from normal sounds. Experiments investigating deterioration sounds in a fan showed changes in latent variables for early deterioration sounds and corresponding increases in the anomaly score. We also show that anomaly scores can be robustly estimated even in the presence of noise. The results reveal that the proposed method performs better than conventional methods.

关键词： deterioration estimation cooling fan anomaly detection neural network variational autoencoder

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UNSUPERVISED ADAPTATION FOR HIGH-DIMENSIONAL WITH LIMITED-SAMPLE DATA CLASSIFICATION USING variational autoencoder

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COMPUTING AND INFORMATICS 2021年第1期40卷 1-28页

作者： Mahmud, Mohammad Sultan Huang, Joshua Zhexue Fu, Xianghua Ruby, Rukhsana Wu, Kaishun Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China

High-dimensional with limited-sample size (HDLSS) datasets exhibit two critical problems: (1) Due to the insufficiently small-sample size, there is a lack of enough samples to build classification models. Classification models with a limited-sample may lead to overfitting and produce erroneous or meaningless results. (2) The 'curse of dimensionality' phenomena is often an obstacle to the use of many methods for solving the high-dimensional with limited-sample size problem and reduces classification accuracy. This study proposes an unsupervised framework for high-dimensional limited-sample size data classification using dimension reduction based on variational autoencoder (VAE). First, the deep learning method variational autoencoder is applied to project high-dimensional data onto lower-dimensional space. Then, clustering is applied to the obtained latent-space of VAE to find the data groups and classify input data. The method is validated by comparing the clustering results with actual labels using purity, rand index, and normalized mutual information. Moreover, to evaluate the proposed model strength, we analyzed 14 datasets from the Arizona State University Digital Repository. Also, an empirical comparison of dimensionality reduction techniques shown to conclude their applicability in the high-dimensional with limited-sample size data settings. Experimental results demonstrate that variational autoencoder can achieve more accuracy than traditional dimensionality reduction techniques in high-dimensional with limited-sample-size data analysis.

关键词： HDLSS problem dimensionality reduction unsupervised framework variational autoencoder deep learning

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Semi-Supervised Recurrent variational autoencoder Approach for Visual Diagnosis of Atrial Fibrillation

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IEEE ACCESS 2021年 9卷 40227-40239页

作者： Costa, Nahuel Sanchez, Luciano Couso, Ines Univ Oviedo Comp Sci Dept Gijon 33202 Spain Univ Oviedo Stat Dept Gijon 33202 Spain

In this work we propose a semi-supervised framework to visually assess the progression of time series. To this end, we present a recurrent version of the VAE to exploit the generative properties that lead it to learn in an unsupervised way a continuous compressed representation of the data. We introduce a classifier in the VAE training process to control the regulation of the latent space, allowing the network to learn latent variables that set the basis for creating an explainable evaluation of the data. We use the proposed framework to address the diagnosis of Atrial Fibrillation (AF) first validating it with simulated data with known properties and subsequently testing it with intracardiac data obtained from pacemakers and defibrillator systems.

关键词： Time series analysis Diseases Heart beat Electrocardiography Data models Pacemakers Task analysis Graphical Analysis heart disease recurrent neural networks time series variational autoencoder

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variational autoencoder-enhanced deep neural network-based detection for MIMO systems

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2023年 6卷

作者： Omondi, Gevira Olwal, Thomas O. Department of Electrical Engineering/F'SATI Tshwane University of Technology Pretoria South Africa School of Engineering University of Nairobi Nairobi Kenya

Lately, there has been a substantial surge of interest in artificial intelligence (AI) as a promising technology to tremendously elevate the efficiency of multiple-input multiple-output (MIMO) detection within wireless communication networks. AI-aided methodologies like deep neural networks (DNNs) have empowered MIMO receivers to understand intricate channel dynamics and interference contexts, ultimately leading to noteworthy enhancements in throughput and the performance of error rates. However, the techniques in existing literature face challenges in effectively reconciling accuracy and efficiency across a spectrum of channel conditions. Therefore, this study presents a state-of-the-art DNN-centric architecture for MIMO signal detection called variational autoencoder-enhanced DNN-based detection (VAE-DNN-Det), which harnesses the power of variational autoencoders (VAEs) to efficiently capture underlying data distributions, thereby enhancing the DNN's ability to adapt to complex channel scenarios and achieve near-optimal performance. Simulations are carried out to contrast the bit error rate (BER) performance of the proposed scheme with traditional methods, where the proposed VAE-DNN-Det method attains a signal-to-noise ratio gain of nearly 1 dB compared to the traditional detection methods. © 2023 The Author(s)

关键词： Artificial intelligence Deep learning Deep neural network Emerging wireless communication MIMO detection variational autoencoder

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Unsupervised classification of multi-contrast magnetic resonance histology of peripheral arterial disease lesions using a convolutional variational autoencoder with a Gaussian mixture model in latent space: A technical feasibility study

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COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 2024年 115卷 102372-102372页

作者： Csore, Judit Roy, Trisha L. Wright, Graham Karmonik, Christof Houston Methodist Hosp DeBakey Heart & Vasc Ctr 6565 Fannin St Houston TX 77030 USA Semmelweis Univ Heart & Vasc Ctr 68 Varosmajor St H-1122 Budapest Hungary Sunnybrook Res Inst 2075 Bayview Ave Toronto ON M4N 3M5 Canada Houston Methodist Res Inst Translat Imaging Ctr MRI Core 6670 Bertner Ave Houston TX 77030 USA

Purpose: To investigate the feasibility of a deep learning algorithm combining variational autoencoder (VAE) and two-dimensional (2D) convolutional neural networks (CNN) for automatically quantifying hard tissue presence and morphology in multi-contrast magnetic resonance (MR) images of peripheral arterial disease (PAD) occlusive lesions. Methods: Multi-contrast MR images (T2-weighted and ultrashort echo time) were acquired from lesions harvested from six amputated legs with high isotropic spatial resolution (0.078 mm and 0.156 mm, respectively) at 9.4 T. A total of 4014 pseudo-color combined images were generated, with 75% used to train a VAE employing custom 2D CNN layers. A Gaussian mixture model (GMM) was employed to classify the latent space data into four tissue classes: I) concentric calcified (c), II) eccentric calcified (e), III) occluded with hard tissue (h) and IV) occluded with soft tissue (s). Test image probabilities, encoded by the trained VAE were used to evaluate model performance. Results: GMM component classification probabilities ranged from 0.92 to 0.97 for class (c), 1.00 for class (e), 0.82-0.95 for class (h) and 0.56-0.93 for the remaining class (s). Due to the complexity of soft-tissue lesions reflected in the heterogeneity of the pseudo-color images, more GMM components (n=17) were attributed to class (s), compared to the other three (c, e and h) (n=6). Conclusion: Combination of 2D CNN VAE and GMM achieves high classification probabilities for hard tissuecontaining lesions. Automatic recognition of these classes may aid therapeutic decision-making and identifying uncrossable lesions prior to endovascular intervention.

关键词： magnetic resonance imaging artificial intelligence variational autoencoder computational neural networks Gaussian mixture model peripheral arterial disease

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