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variational co-embedding learning for attributed network clustering

KNOWLEDGE-BASED SYSTEMS 2023年第1期270卷

作者： Yang, Shuiqiao Verma, Sunny Cai, Borui Jiang, Jiaojiao Yu, Kun Chen, Fang Yu, Shui Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia Deakin Univ Sch Informat Technol Burwood Vic 3125 Australia Macquarie Univ Sch Engn Macquarie Pk NSW 2109 Australia Univ Technol Sydney Data Sci Inst Ultimo NSW 2007 Australia Univ Technol Sydney Sch Comp Sci Ultimo NSW 2007 Australia

Recent developments in attributed network clustering combine graph neural networks and autoencoders for unsupervised learning. Although effective, these techniques suffer from either (a) clustering-unfriendly embedding spaces or (b) limited utilization of attribute information. To address these issues, we propose a novel model called variational Co-embedding Learning Model for Attributed Network Clustering (VCLANC), which utilizes much deeper information from the network by reconstructing both the network structure and the node attributes to perform self-supervised learning. Technically, VCLANC consists of dual variational autoencoders that co-embed nodes and attributes into the same latent space, along with a trainable Gaussian mixture prior that simultaneously performs representation learning and node clustering. To optimize the variational autoencoders and infer the latent variables of embeddings and clustering assignments, we derive a new variational lower bound that maximizes the joint likelihood of the observed network structure and node attributes. Furthermore, we also adopt a mutual distance loss on the cluster centers and a clustering assignment hardening loss on the node embeddings to strengthen clustering quality. Our experimental results on four real-world datasets demonstrate the outstanding performance of VCLANC for attributed network clustering.

关键词： Attributed network clustering Graph neural network variational autoencoder

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Experimental Validation of Machine Learning-Based Joint Failure Management and Quality of Transmission Estimation

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2023年第6期15卷 1页

作者： Kruse, Lars E. Kuehl, Sebastian Dochhan, Annika Pachnicke, Stephan Univ Kiel Chair Commun D-24143 Kiel Germany

The exponentially growing demand for high-speed data necessitates more complex and versatile networks. Optimization and reliability assurance of such high-complexity networks is getting increasingly important. In this article, we experimentally validate our a machine learning-based framework that combines quality of transmission (QoT) estimation with soft-failure detection, identification, and localization based on the same latent space of a variational autoencoder running on optical spectra obtained by optical spectrum analyzers at high priority nodes in the network. We further investigate the advantages of a variational autoencoder-based soft-failure detection mechanism over a QoT metric-based approach. We use data acquired from optical transmission experiments involving different modulation formats and channel configurations. The results demonstrate that the proposed framework achieves reliable QoT estimation in real world scenarios. Additionally, it effectively detects soft-failures, identifies specific failure types and accurately localizes the occurrence of failures.

关键词： Nonlinear optics Logic gates Optical fiber networks Estimation Optical noise Signal to noise ratio Adaptive optics Quality of transmission estimation optical performance monitoring soft-failures variational autoencoder recurrent neural networks

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Hybrid Learning Models for IMU-Based HAR with Feature Analysis and Data Correction

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SENSORS 2023年第18期23卷 7802页

作者： Tseng, Yu-Hsuan Wen, Chih-Yu Natl Chung Hsing Univ Dept Comp Sci & Engn Taichung 40227 Taiwan Natl Chung Hsing Univ Dept Elect Engn Taichung 40227 Taiwan Natl Chung Hsing Univ Smart Sustainable New Agr Res Ctr SMARTer Taichung 40227 Taiwan Natl Chung Hsing Univ Innovat & Dev Ctr Sustainable Agr IDCSA Taichung 40227 Taiwan

This paper proposes a novel approach to tackle the human activity recognition (HAR) problem. Four classes of body movement datasets, namely stand-up, sit-down, run, and walk, are applied to perform HAR. Instead of using vision-based solutions, we address the HAR challenge by implementing a real-time HAR system architecture with a wearable inertial measurement unit (IMU) sensor, which aims to achieve networked sensing and data sampling of human activity, data pre-processing and feature analysis, data generation and correction, and activity classification using hybrid learning models. Referring to the experimental results, the proposed system selects the pre-trained eXtreme Gradient Boosting (XGBoost) model and the Convolutional variational autoencoder (CVAE) model as the classifier and generator, respectively, with 96.03% classification accuracy.

关键词： human activity recognition variational autoencoder generative adversarial networks

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Reconstruction and generation of 3D realistic soil particles with metaball descriptor

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COMPUTERS AND GEOTECHNICS 2023年第1期161卷

作者： Zhao, Yifeng Gao, Xiangbo Zhang, Pei Lei, Liang Li, Stan Z. Galindo-Torres, S. A. Zhejiang Univ Coll Environm & Resources Sci 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China Westlake Univ Sch Engn Key Lab Coastal Environm & Resources Zhejiang Prov 18 Shilongshan Rd Hangzhou 310024 Zhejiang Peoples R China Westlake Univ Sch Engn 18 Shilongshan Rd Hangzhou 310024 Zhejiang Peoples R China

The morphology of soil particles is crucial in determining its granular characteristics and assembly responses. However, how to introduce accurate and various morphologies of realistic particles in modeling can be challenging, as it often requires time-consuming and costly X-ray Computed Tomography (XRCT). This has led to two prevalent problems in modeling: morphological reconstruction and generation. For reconstruction, we develop a geometric-based Metaball-Imaging algorithm. This algorithm is capable of accurately reconstructing the complex morphologies of realistic particles, including those with concave voids, which cannot be easily represented using other shape descriptors such as the spherical harmonic function. It employs a two-step approach, capturing the main contour of the particles using a series of non-overlapping spheres and then refining surface-texture details through gradient search. Four types of soil particles, hundreds of samples, are applied for evaluations. The result shows good matches on key morphological indicators (i.e., volume, surface area, sphericity, circularity, corey-shape factor, nominal diameter and surface-equivalent-sphere diameter), confirming its reconstruction precision. For generation, we propose the Metaball variational autoencoder. Assisted by deep neural networks, this method can generate new 3D particles in Metaball form, while retaining coessential morphological features with parental particles. Additionally, this method allows for control over the generated shapes through an arithmetic pattern, enabling the generation of particles with specific shapes. Two sets of XRCT images different in sample number and geometric features are chosen as parental data. On each training set, one thousand particles are generated for validations. The generation fidelity is demonstrated through comparisons of morphologies and shape-feature distributions between generated and parental particles. Examples are also provided to demonstrate contr

关键词： The metaball function Particle morphology Particle reconstruction Optimization method Particle generation variational autoencoder

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Labyrinth morphological modeling and its application on unreferenced segmentation assessment

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第1期85卷

作者： Li, Xiaoguang Zhu, Ziyao Yin, Hongxia Zhao, Pengfei Lv, Han Tang, Ruowei Qin, Yating Zhuo, Li Wang, Zhenchang Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China Capital Med Univ Beijing Friendship Hosp Dept Radiol Beijing 100050 Peoples R China

The labyrinth of the inner ear is an important auditory and balanced sensory organ and is closely related to tinnitus, hearing loss, vertigo, and Meniere diseases. Quantitative description and measurement of the labyrinth is a challenging task in both clinical practice and medical research. A data-driven-based labyrinth morphological modeling method is proposed for extracting simple and low-dimensional representations or feature vectors to quantify the normal and abnormal labyrinths in morphology. Firstly, a two-stage pose alignment strategy is introduced to align the segmented inner ear labyrinths. Then, an energy-adaptive spatial and inter-slice dimensionality reduction strategy is adopted to extract compact morphological features via a variational autoencoder (VAE). Finally, a statistical model of the compact feature in the latent space is established to represent the morphology distribution of the labyrinths. As one of an application of our model, a reference-free quality evaluation for the segmentation of the labyrinth is explored. The experimental results show that the consistency between the proposed method and the Dice similarity coefficient (DSC) reaches 0.78. Further analysis showed that the model also has a high potential to apply to morphological analysis, such as anomaly detection, of the labyrinths.

关键词： Inner ear labyrinth modeling variational autoencoder Medical image analysis Temporal bone CT image

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Cycle consistent network for end-to-end style transfer TTS training

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NEURAL NETWORKS 2021年 140卷 223-236页

作者： Xue, Liumeng Pan, Shifeng He, Lei Xie, Lei Soong, Frank K. Northwestern Polytech Univ Sch Comp Sci Audio Speech & Language Proc Grp ASLP NPU Xian Peoples R China Microsoft Beijing Peoples R China Microsoft STC Asia Shanghai Peoples R China

In this paper, we propose a cycle consistent network based end-to-end TTS for speaking style transfer, including intra-speaker, inter-speaker, and unseen speaker style transfer for both parallel and unparallel transfers. The proposed approach is built upon a multi-speaker variational autoencoder (VAE) TTS model. The model is usually trained in a paired manner, which means the reference speech is totally paired with the output including speaker identity, text, and style. To achieve a better quality for style transfer, which for most cases is in an unpaired manner, we augment the model with an unpaired path with a separated variational style encoder. The unpaired path takes as input an unpaired reference speech and yields an unpaired output. The unpaired output, which lacks direct ground-truth target, is then successfully constrained by a delicately designed cycle consistent network. Specifically, the unpaired output of the forward transfer is fed into the model again as an unpaired reference input, and after the backward transfer yields an output expected to be the same as the original unpaired reference speech. Ablation study shows the effectiveness of the unpaired path, separated style encoders and cycle consistent network in the proposed model. The final evaluation demonstrates the proposed approach significantly outperforms the Global Style Token (GST) and VAE based systems for all the six style transfer categories, in metrics of naturalness, speech quality, similarity of speaker identity, and similarity of speaking style. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Speech synthesis End-to-end Style transfer variational autoencoder Cycle consistent

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Exploiting the sparse characteristics in probabilistic feature space for hyperspectral anomaly detection

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JOURNAL OF APPLIED REMOTE SENSING 2021年第4期15卷

作者： Yu, Shaoqi Li, Xiaorun Chen, Shuhan Zhao, Liaoying Zhejiang Univ Dept Elect Engn Hangzhou Zhejiang Peoples R China Hangzhou Dianzi Univ Dept Comp Sci Hangzhou Zhejiang Peoples R China

Nowadays, the low-rank representation (LRR) and deep learning-based methods have received much attention in anomaly detection for hyperspectral images (HSIs). However, most of these methods mainly focus on the powerful reconstruction capability of the neural networks while ignoring the potential probability distribution of both anomalies and background pixels. To solve the problem, we propose a sparse component extraction-based probability distribution representation detector (SC-PDRD) framework, which integrates the characteristic of the sparse component obtained by the LRR model with the powerful probability representation ability of the variational autoencoder (VAE) network. The LRR model effectively separates the anomaly component from the background, which also serves as the prior anomaly distribution for each pixel. Moreover, the VAE architecture tries to recover the potential anomaly distribution using the sparse detection map in the feature space. In addition, we employ the Chebyshev neighborhood to leverage spatial information. The modified Wasserstein distance measures the distance between the test pixel and its neighborhood. The final detection map is attained by combining the prior anomalous degree of the anomalies with the output of the VAE network. Experimental results on three real HSIs demonstrate the effectiveness and superiority of SCPDRD. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： hyperspectral anomaly detection hyperspectral imagery variational autoencoder probability distribution intrinsic structure

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Towards physical interaction-based sequential mobility assistance using latent generative model of movement state

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ADVANCED ROBOTICS 2021年第1期35卷 64-79页

作者： Itadera, Shunki Kobayashi, Taisuke Nakanishi, Jun Aoyama, Tadayoshi Hasegawa, Yasuhisa Nagoya Univ Dept Micronano Mech Sci & Engn Nagoya Aichi Japan Nara Inst Sci & Technol Div Informat Sci Nara Japan Meijo Univ Dept Mech Engn Nagoya Aichi Japan

In this paper, we address sequential mobility assistance for daily elderly care through physical human-robot interaction. The goal of this work is to develop a robotic assistive system to provide physical support in daily life such as movement transition, e.g. sit-to-stand and walking. Using a mobile human support robotic platform, we propose an unsupervised learning-based approach to providing desirable physical support through an adaptive impedance parameter selection strategy according to the recognized user's movement state in an online manner. Using a latent generative model with a long short-term memory-based variational autoencoder, we first estimate the probability of the user's current movement state based on the sensory information in a low dimensional latent space. Then, the desired impedance parameters are selected adaptively according to the estimated movement state. One of the benefits of such an unsupervised learning approach is that no labeling is necessary in the training phase. Furthermore, our proposed framework is capable of detecting possible novel states such as falling over based on the obtained latent space. In order to demonstrate the proof of concept of our proposed approach, we present the experimental results of performance evaluations of online movement state recognition as well as novel movement detection.

关键词： Physical human– robot interaction mobility assistance movement detection latent generative model variational autoencoder

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Disentangling cobionts and contamination in long-read genomic data using sequence composition

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G3-GENES GENOMES GENETICS 2024年第11期14卷 jkae187页

作者： Weber, Claudia C. Wellcome Genome Campus Tree Life Wellcome Sanger Inst Hinxton CB10 1SA England

The recent acceleration in genome sequencing targeting previously unexplored parts of the tree of life presents computational challenges. Samples collected from the wild often contain sequences from several organisms, including the target, its cobionts, and contaminants. Effective methods are therefore needed to separate sequences. Though advances in sequencing technology make this task easier, it remains difficult to taxonomically assign sequences from eukaryotic taxa that are not well represented in databases. Therefore, reference-based methods alone are insufficient. Here, I examine how we can take advantage of differences in sequence composition between organisms to identify symbionts, parasites, and contaminants in samples, with minimal reliance on reference data. To this end, I explore data from the Darwin Tree of Life project, including hundreds of high-quality HiFi read sets from insects. Visualizing two-dimensional representations of read tetranucleotide composition learned by a variational autoencoder can reveal distinct components of a sample. Annotating the embeddings with additional information, such as coding density, estimated coverage, or taxonomic labels allows rapid assessment of the contents of a dataset. The approach scales to millions of sequences, making it possible to explore unassembled read sets, even for large genomes. Combined with interactive visualization tools, it allows a large fraction of cobionts reported by reference-based screening to be identified. Crucially, it also facilitates retrieving genomes for which suitable reference data are absent.

关键词： variational autoencoder data visualization unsupervised learning long-read sequencing

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Sentiment Analysis-Based Automatic Stress and Emotion Recognition using Weighted Fused Fusion-Based Cascaded DTCN with Attention Mechanism from EEG Signal

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JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2024年第5期23卷

作者： Kathole, Atul B. Lonare, Savita Dharmale, Gulbakshee Katti, Jayashree Vhatkar, Kapil Kimbahune, Vinod V. Dr DY Patil Inst Technol Pune Comp Engn Pimpri 411018 Maharashtra India Pimpri Chinchwad Coll Engn Nigdi IT Dept Pimpri 411044 Maharashtra India

When loaded with difficulties in fulfilling daily requirements, a lot of people in today's world experience an emotional pressure known as stress. Stress that lasts for a short duration of time has more advantages as they are good for mental health. But, the persistence of stress for a long duration of time may lead to serious health impacts in individuals, such as high blood pressure, cardiovascular disease, stroke and so on. Long-term stress, if unidentified and not treated, may also result in personality disorder, depression and anxiety. The initial detection of stress has become more important to prevent the health issues that arise due to stress. Detection of stress based on brain signals for analysing the emotion in humans leads to accurate detection outcomes. Using EEG-based detection systems and disease, disability and disorders can be identified from the brain by utilising the brain waves. Sentiment Analysis (SA) is helpful in identifying the emotions and mental stress in the human brain. So, a system to accurately and precisely detect depression in human based on their emotion through the utilisation of SA is of high necessity. The development of a reliable and precise Emotion and Stress Recognition (ESR) system in order to detect depression in real-time using deep learning techniques with the aid of Electroencephalography (EEG) signal-based SA is carried out in this paper. The essentials needed for performing stress and emotion detection are gathered initially from benchmark databases. Next, the pre-processing procedures, like the removal of artifacts from the gathered EEG signal, are carried out on the implemented model. The extraction of the spectral attributes is carried out from the pre-processed. The extracted spectral features are considered the first set of features. Then, with the aid of a Conditional variational autoencoder (CVA), the deep features are extracted from the pre-processed signals forming a second set of features. The weights are opti

关键词： Stress recognition emotion recognition spectral feature extraction variational autoencoder weighted feature fusion adaptive egret swarm optimisation algorithm cascaded deep temporal convolution network with attention mechanism

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