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An attempt to construct the individual model of daily facial skin temperature using variational autoencoder

ARTIFICIAL LIFE AND ROBOTICS 2021年第4期26卷 488-493页

作者： Masaki, Ayaka Nagumo, Kent Iwashita, Yuki Oiwa, Kosuke Nozawa, Akio Aoyama Gakuin Univ Tokyo Japan

Facial skin temperature (FST) has also gained prominence as an indicator for detecting anomalies such as fever due to the COVID-19. When FST is used for engineering applications, it is enough to be able to recognize normal. We are also focusing on research to detect some anomaly in FST. In a previous study, it was confirmed that abnormal and normal conditions could be separated based on FST by using a variational autoencoder (VAE), a deep generative model. However, the simulations so far have been a far cry from reality. In this study, normal FST with a diurnal variation component was defined as a normal state, and a model of normal FST in daily life was individually reconstructed using VAE. Using the constructed model, the anomaly detection performance was evaluated by applying the Hotelling theory. As a result, the area under the curve (AUC) value in ROC analysis was confirmed to be 0.89 to 1.00 in two subjects.

关键词： Facial skin temperature Infrared thermography Deep learning Anomaly detection variational autoencoder Hotelling's theory

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Fragment-based deep molecular generation using hierarchical chemical graph representation and multi-resolution graph variational autoencoder

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MOLECULAR INFORMATICS 2023年第5期42卷 e2200215页

作者： Gao, Zhenxiang Wang, Xinyu Gaines, Blake Blumenfeld Shi, Xuetao Bi, Jinbo Song, Minghu Univ Connecticut Dept Comp Sci & Engn Storrs CT 06269 USA Case Western Reserve Univ Ctr Artificial Intelligence Drug Discovery Sch Med Cleveland OH 44106 USA Univ Connecticut Dept Biomed Engn Storrs CT 06269 USA Univ Connecticut Dept Biomed Engn Storrs CT 06269 USA

Graph generative models have recently emerged as an interesting approach to construct molecular structures atom-by-atom or fragment-by-fragment. In this study, we adopt the fragment-based strategy and decompose each input molecule into a set of small chemical fragments. In drug discovery, a few drug molecules are designed by replacing certain chemical substituents with their bioisosteres or alternative chemical moieties. This inspires us to group decomposed fragments into different fragment clusters according to their local structural environment around bond-breaking positions. In this way, an input structure can be transformed into an equivalent three-layer graph, in which individual atoms, decomposed fragments, or obtained fragment clusters act as graph nodes at each corresponding layer. We further implement a prototype model, named multi-resolution graph variational autoencoder (MRGVAE), to learn embeddings of constituted nodes at each layer in a fine-to-coarse order. Our decoder adopts a similar but conversely hierarchical structure. It first predicts the next possible fragment cluster, then samples an exact fragment structure out of the determined fragment cluster, and sequentially attaches it to the preceding chemical moiety. Our proposed approach demonstrates comparatively good performance in molecular evaluation metrics compared with several other graph-based molecular generative models. The introduction of the additional fragment cluster graph layer will hopefully increase the odds of assembling new chemical moieties absent in the original training set and enhance their structural diversity. We hope that our prototyping work will inspire more creative research to explore the possibility of incorporating different kinds of chemical domain knowledge into a similar multi-resolution neural network architecture.

关键词： deep molecular generation fragment-based molecular generation graph-based deep generative model variational autoencoder

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Reconstruction and fast prediction of 3D heat and mass transfer based on a variational autoencoder

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2023年 149卷

作者： Liu, Gongyan Li, Runze Zhou, Xiaozhou Sun, Tianrui Zhang, Yufei Tsinghua Univ Sch Aerosp Engn Beijing Peoples R China Siemens Ltd Beijing 100084 Peoples R China

Reconstruction and fast prediction of heat and mass transfer are important for the improvement of data center operations and energy savings. In this study, an artificial neural network (ANN) and variational autoencoder (VAE) composite model is proposed for the reconstruction and prediction of 3D flowfields with high accuracy and efficiency. The VAE model is trained to extract features of the problem and to realize 3D physical field reconstruction. The ANN is employed to achieve the constructability of the extracted features. A dataset of steady temperature/velocity fields is acquired by computational fluid dynamics and heat transfer (CFD/HT) and fed to train the deep learning model. The proposed ANN-VAE model is experimentally proven to achieve promising field prediction accuracy with a significantly reduced computational cost. CFD/HT method is often incapable of occasions that require instant knowledge of heat and mass transfer. The proposed ANN-VAE method convert the time-consuming simulations into the training stage. Driven by large amount of simulation data, the trained ANN-VAE model is capable of field prediction. Compared to the CFD/HT method, the ANN-VAE method speeds up the physical field prediction by approximately 380,000 times, with mean accuracies of 97.3% for temperature field prediction and 97.9% for velocity field prediction, making it feasible for real-time prediction of heat and mass transfer.

关键词： Heat and mass transfer simulation variational autoencoder Artificial neural network Physical field prediction Data center

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Anomaly detection in facial skin temperature using variational autoencoder

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ARTIFICIAL LIFE AND ROBOTICS 2021年第1期26卷 122-128页

作者： Masaki, Ayaka Nagumo, Kent Lamsal, Bikash Oiwa, Kosuke Nozawa, Akio Aoyama Gakuin Univ Sagamihara Kanagawa Japan Kajima Corp Chofu Tokyo Japan

Facial skin temperature is a physiological index that varies with skin blood flow controlled by autonomic nervous system activity. The facial skin temperature can be remotely measured using infrared thermography, and it has recently attracted attention as a remote biomarker. For example, studies have been reported to estimate human emotions, drowsiness, and mental stress on facial skin temperature. However, it is impossible to make a machine that can discriminate all infinite physiological and psychological states. Considering the practicality of skin temperature, a machine that can determine the normal state of facial skin temperature may be sufficient. In this study, we propose a completely new approach to incorporate the concept of anomaly detection into the analysis of physiological and psychological states by facial skin temperature. In this paper, the method for separating normal and anomaly facial thermal images using an anomaly detection model was investigated to evaluate the applicability of variational autoencoder (VAE) to facial thermal images.

关键词： Facial skin temperature Thermal face image Infrared thermography Deep learning Anomaly detection variational autoencoder

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Hybrid variational autoencoder for time series forecasting

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KNOWLEDGE-BASED SYSTEMS 2023年 281卷

作者： Cai, Borui Yang, Shuiqiao Gao, Longxiang Xiang, Yong Deakin Univ Sch Informat Technol Burwood Vic 3125 Australia Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2032 Australia Qilu Univ Technol Shandong Acad Sci Jinan Peoples R China

variational autoencoders (VAE) are powerful generative models that learn the latent representations of input data as random variables. Recent studies show that VAE can flexibly learn the complex temporal dynamics of time series and achieve more promising forecasting results than deterministic models. However, a major limitation of existing works is that they fail to jointly learn the local patterns (e.g., seasonality and trend) and temporal dynamics of time series for forecasting. Accordingly, we propose a novel hybrid variational autoencoder (HyVAE) to integrate the learning of local patterns and temporal dynamics by variational inference for time series forecasting. Experimental results on four real-world datasets show that the proposed HyVAE achieves better forecasting results than various counterpart methods, as well as two HyVAE variants that only learn the local patterns or temporal dynamics of time series, respectively.

关键词： Time series forecasting variational autoencoder Deep learning

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variational autoencoder based receiver for orthogonal time frequency space modulation

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DIGITAL SIGNAL PROCESSING 2021年 117卷 103170-103170页

作者： Li, Qiao Xiang, Zheng Ren, Peng Li, Wanlu Xidian Univ Sch Telecommun Engn Xian 710071 Shaanxi Peoples R China

The emerging orthogonal time frequency space (OTFS) modulation is demonstrated to offer reliable communication performance advantages over orthogonal frequency division multiplexing (OFDM) in doubly-dispersive fading channel. However, the existing embedded pilot-aided method to estimate channel impulse response (CIR) requires enormous spectral overhead to avoid the contamination of pilot symbols. In this paper, we present a variational autoencoder (VAE) based receiver for OTFS modulation that achieve a joint estimation and detection without pilot in delay-Doppler (DD) domain. The variational approach is considered to simplify the problem, and evidence lower bound (ELBO) is derived as loss function. In encoder step, an approximate posterior probability is introduced and utilized to minimize the Kullback-Leibler (KL) distance. Then we estimate CIR in decoder step and maximize the ELBO at last. From our simulation results, the proposed VAE based receiver for OTFS modulation enjoys a promising performance with other methods. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Orthogonal time frequency space modulation variational autoencoder Delay-Doppler domain Doubly dispersion

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Initial Study of Batik Generation using variational autoencoder

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Procedia Computer Science 2023年 227卷 785-794页

作者： Aditya Firman Ihsan School of Computing Telkom University Bandung 40257 Indonesia

One of the most promising architectures for generative models is the variational autoencoder (VAE). To reconstruct Batik patterns for this work, we used a deep convolutional VAE architecture. Reconstruction outcomes from various batik motifs are mapped and contrasted using some criteria. As another crucial component of the convolutional network, batch normalization's impact on the model's performance was also examined. The dataset is used to study some learned latent space features. Through these findings, we laid the framework for next research on Batik generation utilizing VAE.

关键词： variational autoencoder Batik convolutional neural network generative model

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variational autoencoder BASED UNSUPERVISED DOMAIN ADAPTATION FOR SEMANTIC SEGMENTATION

VARIATIONAL AUTOENCODER BASED UNSUPERVISED DOMAIN ADAPTATION...

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IEEE International Conference on Image Processing (ICIP)

作者： Li, Zongyao Togo, Ren Ogawa, Takahiro Haseyama, Miki Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido Japan Hokkaido Univ Educ & Res Ctr Math & Data Sci Sapporo Hokkaido Japan Hokkaido Univ Fac Informat Sci & Technol Sapporo Hokkaido Japan

ISBN: (纸本)9781728163956

Unsupervised domain adaptation, which transfers supervised knowledge from a labeled domain to an unlabeled domain, remains a tough problem in the field of computer vision, especially for semantic segmentation. Some methods inspired by adversarial learning and semi-supervised learning have been developed for unsupervised domain adaptation in semantic segmentation and achieved outstanding performances. In this paper, we propose a novel method for this task. Like adversarial learning-based methods using a discriminator to align the feature distributions from different domains, we employ a variational autoencoder to get to the same destination but in a non-adversarial manner. Since the two approaches are compatible, we also integrate an adversarial loss into our method. By further introducing pseudo labels, our method can achieve state-of-the-art performances on two benchmark adaptation scenarios, GTA5-to-CITYSCAPES and SYNTHIA-to-CITYSCAPES.

关键词： Unsupervised domain adaptation semantic segmentation variational autoencoder adversarial learning

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MAVGAE: a multimodal framework for predicting asymmetric drug-drug interactions based on variational graph autoencoder

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2024年第7期28卷 1-13页

作者： Deng, Zengqian Xu, Jie Feng, Yinfei Dong, Liangcheng Zhang, Yuanyuan Qingdao Univ Technol Sch Informat & Control Engn Qingdao Peoples R China Qufu Normal Univ Sch Informat Sci & Engn Rizhao Peoples R China

Drug-drug interactions refer to the phenomena wherein the potency, duration, or effectiveness of one or multiple drugs undergo alterations of varying degrees as a result of their concurrent or sequential usage. The accurate identification of potential drug interactions plays a pivotal role in mitigating the risks associated with drug administration in patients, it also helps in minimizing the likelihood of hazardous situations arising during a patient's course of treatment. However, researchers have found that there is a problem of asymmetric drug interactions, where one drug may affect another but not vice versa. This adds to the difficulty of prediction, so in polypharmacy, the order of drug administration is critical to efficacy and safety, and few current studies predict asymmetric DDIs. Aiming at the above problems, we propose a framework based on multimodal data and a variational graph autoencoder named MAVGAE for predicting asymmetric drug interactions. The framework initially encodes multimodal data into low-dimensional representations and then utilizes a variational graph autoencoder for encoding and decoding. During the model training process, supervised learning is employed for the classification task with the incorporation of heterogeneity information, ensuring accurate prediction of drug interactions. Experimental validation on a large-scale drug dataset demonstrates the framework's high accuracy and reliability in predicting non-symmetrical drug interactions, offering effective support and guidance for drug research.

关键词： Artificial intelligence asymmetric drug-drug interaction multimodal variational autoencoder

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Contextual anomaly detection for high-dimensional data using Dirichlet process variational autoencoder

A I I E Transactions

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A I I E Transactions 2023年第5期55卷 433-444页

作者： Hyojoong Kim Heeyoung Kim Department of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea

Due to recent advances in sensing technologies, response measurements of various sensors are frequently used for system monitoring purposes. However, response data are often affected by some contextual variables, such as equipment settings and time, resulting in different patterns, even when the system is in the normal state. In this case, anomaly detection methods that do not consider contextual variables may be unable to distinguish between abnormal and normal patterns of the response data affected by the contextual variables. Motivated by this problem, we propose a method for contextual anomaly detection, particularly in the case where the response and contextual variables are both high-dimensional and complex. The proposed method is based on variational autoencoders (VAEs), which are neural-network-based generative models suitable for modeling high-dimensional and complex data. The proposed method combines two VAEs: one for response variables and the other for contextual variables. Specifically, in the latent space of the VAE for contextual variables, we model the latent variables using a Dirichlet process Gaussian mixture model. Consequently, the effects of the contextual variables can be modeled using several clusters, each representing a different contextual environment. The latent contextual variables are then used as additional inputs to the other VAE’s decoder for reconstructing response data from their latent representations. We then detect the anomalies based on the negative reconstruction loss of a new response observation. The effectiveness of the proposed method is demonstrated using several benchmark datasets and a case study based on a global tire company.

关键词： Contextual anomaly detection Dirichlet process mixture model high-dimensional data variational autoencoder

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