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Multi-View Multi-Label Fine-Grained Emotion Decoding From Human Brain Activity

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第7期35卷 9026-9040页

作者： Fu, Kaicheng Du, Changde Wang, Shengpei He, Huiguang Chinese Acad Sci Res Ctr Brain Inspired Intelligence Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing 100190 Peoples R China

Decoding emotional states from human brain activity play an important role in the brain-computer interfaces. Existing emotion decoding methods still have two main limitations: one is only decoding a single emotion category from a brain activity pattern and the decoded emotion categories are coarse-grained, which is inconsistent with the complex emotional expression of humans;the other is ignoring the discrepancy of emotion expression between the left and right hemispheres of the human brain. In this article, we propose a novel multi-view multi-label hybrid model for fine-grained emotion decoding (up to 80 emotion categories) which can learn the expressive neural representations and predict multiple emotional states simultaneously. Specifically, the generative component of our hybrid model is parameterized by a multi-view variational autoencoder, in which we regard the brain activity of left and right hemispheres and their difference as three distinct views and use the product of expert mechanism in its inference network. The discriminative component of our hybrid model is implemented by a multi-label classification network with an asymmetric focal loss. For more accurate emotion decoding, we first adopt a label-aware module for emotion-specific neural representation learning and then model the dependency of emotional states by a masked self-attention mechanism. Extensive experiments on two visually evoked emotional datasets show the superiority of our method.

关键词： Decoding Brain modeling Functional magnetic resonance imaging Predictive models Emotion recognition Dimensionality reduction Pattern recognition Fine-grained emotion decoding multi-label learning multi-view learning product of experts (PoEs) variational autoencoder

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Characterizing Marine Magnetic Anomalies: A Machine Learning Approach to Advancing the Understanding of Oceanic Crust Formation

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JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH 2025年第2期130卷

作者： Wu, S. Thoram, S. Sun, J. Sager, W. W. Chen, J. Univ Houston Dept Earth & Atmospher Sci Houston TX 77004 USA Univ Houston Dept Elect & Comp Engn Houston TX USA

Linear magnetic anomalies (LMA), resulting from Earth's magnetic field reversals recorded by seafloor spreading serve as crucial evidence for oceanic crust formation and plate tectonics. Traditionally, LMA analysis relies on visual inspection and manual interpretation, which can be subject to biases due to the complexities of the tectonic history, uneven data coverage, and strong local anomalies associated with seamounts and fracture zones. In this study, we present a Machine learning (ML)-based framework to identify LMA, determine their orientations and distinguish spatial patterns across oceans. The framework consists of three stages and is semi-automated, scalable and unbiased. First, a generation network produces artificial yet realistic magnetic anomalies based on user-specified conditions of linearity and orientation, addressing the scarcity of the labeled training dataset for supervised ML approaches. Second, a characterization network is trained on these generated magnetic anomalies to identify LMA and their orientations. Third, the detected LMA features are clustered into groups based on predicted orientations, revealing underlying spatial patterns, which are directly related to propagating ridges and tectonic activity. The application of this framework to magnetic data from seven areas in the Atlantic and Pacific oceans aligns well with established magnetic lineations and geological features, such as the Mid-Atlantic Ridge, Reykjanes Ridge, Galapagos Spreading Center, Shatsky Rise, Juan de Fuca Ridge and even Easter Microplate and Galapagos hotspot. The proposed framework establishes a solid foundation for future data-driven marine magnetic analyses and facilitates objective and quantitative geological interpretation, thus offering the potential to enhance our understanding of oceanic crust formation.

关键词： marine magnetic anomalies machine learning linear magnetic anomaly oceanic crust formation variational autoencoder

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Light-Aware Contrastive Learning for Low-Light Image Enhancement

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2024年第9期20卷 1-24页

作者： Wu, Xu Lai, Zhihui Zhou, Jie Hou, Xianxu Pedrycz, Witold Shen, Linlin Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Guangdong Key Lab Intelligent Informat Proc Shenzhen Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen Peoples R China Xian Jiaotong Liverpool Univ Sch AI & Adv Comp Suzhou Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada Univ Nottingham Ningbo China Dept Comp Sci Ningbo Peoples R China

Low-Light Image Enhancement (LLIE) presents challenges due to texture information loss and uneven illumination, which can distort feature distribution and reduce the quality of the enhanced images. However, current deep learning methods for LLIE only use supervised information from clear images to extract low-light image features, while disregarding the negative information in low-light images (i.e., low illumination and noise). To address these challenges, we propose a novel LLIE method, LACR-VAE, by leveraging the negative information and considering the uneven illumination. In particular, a Light-Aware Contrastive Regularization (LACR) based on contrastive learning is designed to exploit information from both clear and low-light images. The LACR aims to align latent variables of enhanced images with clear images, away from those of low-light images. This allows the method to prioritize essential elements for LLIE and minimize noise and lighting variations. Furthermore, considering the uneven illumination with diverse region sizes and shapes, a Region-CAlibrated Module (RCAM) is present to learn local and global illumination relations among image regions, and an Attention-guided Multi-Scale Module (AMSM) is designed to extract multi-scale features that improve the model's representation capability. Extensive experiments show that our method achieves superior performance than previous works. Specifically, our method yields a significant enhancement in the National Aeronautics and Space Administration (NASA) testset, achieving an improvement of at least 0.99 in PSNR and 0.0409 in SSIM. Codes and datasets are available at https://***/csxuwu/LACR-VAE.

关键词： Low-light image enhancement variational autoencoder contrastive learning transformer

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Integration of scRNA-seq data by disentangled representation learning with condition domain adaptation

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BMC BIOINFORMATICS 2024年第1期25卷 1页

作者： Liu, Renjing Qian, Kun He, Xinwei Li, Hongwei China Univ Geosci Wuhan Sch Math & Phys Wuhan 430074 Peoples R China

BackgroundThe integration of single-cell RNA sequencing data from multiple experimental batches and diverse biological conditions holds significant importance in the study of cellular *** expedite the exploration of systematic disparities under various biological contexts, we propose a scRNA-seq integration method called scDisco, which involves a domain-adaptive decoupling representation learning strategy for the integration of dissimilar single-cell RNA data. It constructs a condition-specific domain-adaptive network founded on variational autoencoders. scDisco not only effectively reduces batch effects but also successfully disentangles biological effects and condition-specific effects, and further augmenting condition-specific representations through the utilization of condition-specific Domain-Specific Batch Normalization layers. This enhancement enables the identification of genes specific to particular conditions. The effectiveness and robustness of scDisco as an integration method were analyzed using both simulated and real datasets, and the results demonstrate that scDisco can yield high-quality visualizations and quantitative outcomes. Furthermore, scDisco has been validated using real datasets, affirming its proficiency in cell clustering quality, retaining batch-specific cell types and identifying condition-specific *** is an effective integration method based on variational autoencoders, which improves analytical tasks of reducing batch effects, cell clustering, retaining batch-specific cell types and identifying condition-specific genes.

关键词： ScRNA-seq Cellular heterogeneity Integration variational autoencoder Disentangled representation learning Condition domain adaptation

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A Novel CVAE-Based Sequential Monte Carlo Framework for Dynamic Soft Sensor Applications

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第3期20卷 3789-3800页

作者： Sun, Wenxin Xiong, Weili Chen, Hongtian Chiplunkar, Ranjith Huang, Biao Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 1H9 Canada

In industrial processes, quality variables are typically sampled at a considerably lower frequency than system inputs due to technical or cost constraints. Dynamic soft sensors utilize temporal prediction to bridge these sampling gaps, thus enabling real-time closed-loop control. However, existing approaches primarily focus on one-step prediction accuracy, potentially leading to significant deviations in long-term predictions. In addition, these methods are incapable of evaluating the reliability of prediction results, subsequently increasing the potential risk of closed-loop systems. To tackle these challenges, this study presents a novel regression modeling approach based on the conditional variational autoencoder (CVAE) framework. In contrast to traditional regression approaches, this method focuses on modeling the transition probability distribution of the system, allowing the model to produce a range of credible quality variable predictions via Monte Carlo (MC) sampling. Based on the CVAEs, the sequential MC method is further employed to simulate diverse potential system state trajectories, thereby achieving multistep soft measurement prediction. Compared with traditional soft measurement techniques, the proposed method demonstrates lower prediction biases and the capacity to assess the credibility of prediction results from a probabilistic standpoint. When online quality variables are assessed by the laboratory, this method can update predictions utilizing the resampling scheme. Two case studies are offered to validate the effectiveness of the proposed scheme.

关键词： Predictive models Real-time systems Adaptation models Time measurement Soft sensors Monte Carlo methods Estimation Dynamic soft sensor (SS) prediction calibration sequential Monte Carlo (SMC) variational autoencoder

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Stego-Image Synthesis Employing Data-Driven Continuous Variable Representations of Cover Images

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IEEE ACCESS 2024年 12卷 146749-146770页

作者： Almuayqil, Saleh Naif Fadel, Magdy M. Hassan, Mohammed K. Hagras, Esam A. A. Said, Wael Jouf Univ Coll Comp & Informat Sci Informat Syst Dept Sakaka 72314 Saudi Arabia Mansoura Univ Fac Engn Comp Engn & Syst Dept Mansoura 35516 Egypt Horus Univ EGYPT HUE Fac Engn Mechatron Dept New Damietta 34517 Egypt Delta Univ Sci & Technol Fac Engn Elect & Commun Dept Gamasa 35712 Egypt Zagazig Univ Fac Comp & Informat Comp Sci Dept Zagazig 44511 Egypt Taibah Univ Coll Comp Sci & Engn Comp Sci Dept Madinah 42353 Saudi Arabia

The security of stego-images is a crucial foundation for analyzing steganography algorithms. Recently, steganography has made significant strides in ongoing conflicts with steganalysis. In order to increase the security of stego-images, steganography must be able to evade detection using steganalysis methods. Secret information is typically hidden using traditional embedding-based steganography, which inevitably leaves traces of the modifications that can be found using more sophisticated machine-learning-based steganalysis techniques. Steganography without embedding (SWE) outperforms machine-learning-based steganalysis techniques because it does not require alteration of the data of the cover image. A novel image SWE method based on deep convolutional generative adversarial networks (GANs) is proposed to synthesize stego-images led by embedded text. The variational autoencoder (VAE) in the GAN model is utilized to synthesize the stego-image, based on interpolating the secret text in a continuous variable representation of the cover image. To further improve the framework's performance and shorten processing times, the whale optimization algorithm (WOA) is used to identify the optimal VAE structure. When creating a stego-image, no embedding or modification procedures are required, and after training, a different convolutional neural network (CNN) known as the extractor can correctly extract the data from the image. The experimental results revealed that this approach has the advantages of evading detection using innovative deep learning (DL) steganalysis architecture and accurate information extraction.

关键词： Convolutional neural networks Convolutional neural network data hiding generative adversarial network generative artificial intelligence generative image synthesis steganography variational autoencoder Convolutional neural networks Convolutional neural network data hiding generative adversarial network generative artificial intelligence generative image synthesis steganography variational autoencoder

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Bayesian Inference for Non-Linear Forward Model by Using a VAE-Based Neural Network Structure

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 1400-1411页

作者： Zhang, Yechuan Zheng, Jian-Qing Chappell, Michael Univ Oxford Inst Biomed Engn Dept Engn Sci Oxford OX1 3PJ England Univ Oxford Kennedy Inst Rheumatol Nuffield Dept Orthopaed Rheumatol & Musculoskeleta Oxford OX3 7FY England Univ Nottingham Sir Peter Mansfield Imaging Ctr Sch Med Nottingham NG7 2RD England Univ Nottingham Sch Med Mental Hlth & Clin Neurosci Nottingham NG7 2RD England Univ Oxford Wellcome Ctr Integrat Neuroimaging Nuffield Dept Clin Neurosci FMRIB Oxford OX3 9DU England

In this paper, a variational autoencoder (VAE) based framework is introduced to solve parameter estimation problems for non-linear forward models. In particular, we focus on applications in the field of medical imaging where many thousands of model-based inference analyses might be required to populate a single parametric map. We adopt the concept from variational Bayes (VB) of using an approximate representation of the posterior, and the concept from the VAE of using the latent space representation to encode the parameters of a forward model. Our work develops the idea of mapping between time-series data and latent parameters using a neural network in variational way. A loss function that differs from the classic VAE formulation and a new sampling strategy are proposed to enable uncertainty estimation as part of the forward model inference. The VAE-based structure is evaluated using simulation experiments on a simple example and two perfusion MRI forward models. Compared with analytical VB (aVB) and Markov Chain Monte Carlo (MCMC), our VAE-based model achieves comparable accuracy, and hundredfold improvement in computational time (100ms/image). We believe this VAE-like framework can be generalized to imaging modularities with higher complexity and thus benefit clinical adoption where otherwise long processing time associated with conventional inference methods is prohibitive.

关键词： Neural networks Computational modeling Bayes methods Data models Estimation Biomedical imaging Optimization variational autoencoder variational Bayes parameter estimation problems medical imaging

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Bayesian Hierarchical Sparse autoencoder for Massive MIMO CSI Feedback

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 3213-3227页

作者： Guo, Huayan Lau, Vincent K. N. Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Kowloon Hong Kong 999077 Peoples R China

Efficient channel state information (CSI) compression and feedback from user equipment to the base station (BS) are crucial for achieving the promised capacity gains in massive multiple-input multiple-output (MIMO) systems. Deep autoencoder (AE)-based schemes have been proposed to improve the efficiency of CSI compression and feedback. However, existing AE-based schemes suffer from critical issues in both CSI dimensionality reduction and latent feature quantization. In this paper, we propose a novel hierarchical sparse AE for efficient CSI compression and feedback for the 5G-NR fixed-length CSI feedback mechanism. Our approach employs a two-tier AE structure to jointly compress the sparse CSI latent feature and its side information. Additionally, we utilize a model-assisted Bayesian Rate-Distortion approach to train the weights of the AE. Specifically, the training loss function is formulated based on the variational Bayesian inference framework given a parametric Bernoulli Laplace Mixture prior model and a sparsity-inducing likelihood model. Furthermore, we propose a model-assisted adaptive coding algorithm to quantize the latent feature under the fixed codeword bit length constraint. Our experimental results demonstrate that the proposed solution outperforms existing AE-based schemes under various feedback budgets.

关键词： Adaptation models Quantization (signal) Training Bayes methods Dimensionality reduction Decoding Rate-distortion Massive MIMO deep learning CSI feedback variational autoencoder

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TransDiff: medical image segmentation method based on Swin Transformer with diffusion probabilistic model

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APPLIED INTELLIGENCE 2024年第8期54卷 6543-6557页

作者： Liu, Xiaoxiao Zhao, Yan Wang, Shigang Wei, Jian Jilin Univ Sch Commun Engn Changchun 130012 Peoples R China

Medical image segmentation can provide a reliable basis for clinical analysis and diagnosis. However, this task is challenging due to the low contrast, boundary ambiguity between organs or lesions and surrounding tissues, and noise interference of images. To address this challenge, which is unique to medical images, and further improve the segmentation accuracy and precision, a medical image segmentation model (TransDiff) is proposed from the perspective of improving model robustness and enriching semantic information. TransDiff comprises three parts: a variational autoencoder (VAE), a diffusion transformer model and a Swin Transformer. The VAE constructs a latent space to provide an environment for fully extracting and fusing features. The diffusion model predicts and removes noise by inferring semantics through the propagation of information between nodes. The Swin Transformer enriches discriminative features as a conditional part. TransDiff inherits the robustness to noise and missing data of the diffusion model and the feature enrichment of the Swin Transformer, thus exhibiting a higher understanding of semantic information. It performs well on medical datasets with three different image modalities, outperforms existing medical image segmentation methods in terms of segmentation precision and accuracy, and has good generalizability. The codes and trained models will be publicly available at https://***/xiaoxiao1997/TransDiff.

关键词： Diffusion probabilistic model variational autoencoder Transformer Medical image segmentation Deep learning

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Business Process Anomaly Detection and Root Cause Analysis Using BLSTM-VAE With Attention

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IEEE ACCESS 2024年 12卷 101390-101406页

作者： El-Aziz, Eman Abd Fathalla, Radwa Ismail, Yasser Shaheen, Mohamed Arab Acad Sci Technol & Maritime Transport Coll Comp & IT Alexandria 21625 Egypt Southern Univ & A&M Coll Elect Engn Dept Baton Rouge LA 70807 USA

Detecting anomalous executions in business process data is crucial for safeguarding the efficiency and success of an organization. Unsupervised approaches are commonly used for business process anomaly detection because of the scarcity of labeled anomaly data. However, these approaches often encounter a notable decline in performance because they lack prior knowledge about the anomalies. Additionally, most of them do not perform root cause analysis on the detected anomalies. This study proposes a variational autoencoder-based approach to overcome the performance limitations of existing unsupervised methods and determine the root causes of the detected anomalies. The learning of the variational autoencoder from unlabeled business process data is enhanced in the proposed approach by leveraging different architectural components, namely, the entity embedding technique, the bidirectional long short-term memory network, and the self-attention mechanism. Combining these architectural components in the variational autoencoder architecture leads to learning high-level representations from the business process data and thus improving the reconstruction capability of the variational autoencoder. Furthermore, this study suggests feeding the reconstruction error provided by the variational autoencoder into the logistic regression classifier to improve the accuracy of anomaly detection. The performance of the proposed model was evaluated on real-life and synthetic datasets. The experimental findings indicate that the proposed model outperforms six existing anomaly detection models in terms of precision, recall, and F1-score metrics.

关键词： Anomaly detection Root cause analysis Data models Analytical models Long short term memory Information systems Business process management bidirectional long short-term memory business process root cause analysis entity embedding logistic regression process-aware information systems self-attention variational autoencoder

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