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Prediction of microstructure evolution at the atomic scale by deep generative model in combination with recurrent neural networks

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ACTA MATERIALIA 2023年第1期259卷

作者： Sase, Kohei Shibuta, Yasushi Univ Tokyo Dept Mat Engn 7-3-1 HongoBunkyo Ku Tokyo 1138656 Japan

A novel method to predict multi-atom cooperative phenomena at atomic scale is proposed based on a deep generative model in combination with recurrent neural network. The variational autoencoder (VAE) model successfully identifies three different crystal orientations in the polycrystalline nickel by using 10-dimensional latent variables and restores the image of atomic configurations reflecting the crystal orientation of each grain. Moreover, microstructure evolution of the polycrystalline iron is successfully predicted through three steps: dimensionality reduction of atomic coordinates from the MD simulation using the encoder, time evolution of latent variables using the long short-term memory (LSTM) model, and data restoration using the decoder. We successfully predict the microstructure that cannot be reproduced on the time scale of MD simulations by decoding latent variables in the future time from the LSTM model. This is a new attempt of acceleration of the MD simulation that differs significantly from conventional acceleration methods.

关键词： Molecular dynamics Microstructure Deep generative model Recurrent neural networks variational autoencoder Long short-term memory

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Synthesis of Synthetic Hyperspectral Images with Controllable Spectral Variability Using a Generative Adversarial Network

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REMOTE SENSING 2023年第16期15卷 3919-3919页

作者： Palsson, Burkni Ulfarsson, Magnus O. Sveinsson, Johannes R. Univ Iceland Fac Elect & Comp Engn IS-105 Reykjavik Iceland

In hyperspectral unmixing (HU), spectral variability in hyperspectral images (HSIs) is a major challenge which has received a lot of attention over the last few years. Here, we propose a method utilizing a generative adversarial network (GAN) for creating synthetic HSIs having a controllable degree of realistic spectral variability from existing HSIs with established ground truth abundance maps. Such synthetic images can be a valuable tool when developing HU methods that can deal with spectral variability. We use a variational autoencoder (VAE) to investigate how the variability in the synthesized images differs from the original images and perform blind unmixing experiments on the generated images to illustrate the effect of increasing the variability.

关键词： hyperspectral unmixing synthetic hyperspectral images generative adversarial network variational autoencoder deep learning neural network

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BioDSNN: a dual-stream neural network with hybrid biological knowledge integration for multi-gene perturbation response prediction

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BRIEFINGS IN BIOINFORMATICS 2024年第1期26卷 bbae617页

作者： Tan, Yuejun Xie, Linhai Yang, Hong Zhang, Qingyuan Luo, Jinyuan Zhang, Yanchun Guangzhou Univ Cyberspace Inst Adv Technol Guangzhou 510000 Peoples R China Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321000 Peoples R China Natl Ctr Prot Sci Beijing State Key Lab Prote Beijing 100000 Peoples R China Int Acad Phronesis Med Guangzhou 510000 Peoples R China

Studying the outcomes of genetic perturbation based on single-cell RNA-seq data is crucial for understanding genetic regulation of cells. However, the high cost of cellular experiments and single-cell sequencing restrict us from measuring the full combination space of genetic perturbations and cell types. Consequently, a bunch of computational models have been proposed to predict unseen combinations based on existing data. Among them, generative models, e.g. variational autoencoder and diffusion models, have the superiority in capturing the perturbed data distribution, but lack a biologically understandable foundation for generalization. On the other side of the spectrum, Gene Regulation Networks or gene pathway knowledge have been exploited for more reasonable generalization enhancement. Unfortunately, they do not reach a balanced processing of the two data modalities, leading to a degraded fitting ability. Hence, we propose a dual-stream architecture. Before the information from two modalities are merged, the sequencing data are learned with a generative model while three types of knowledge data are comprehensively processed with graph networks and a masked transformer, enforcing a deep understanding of single-modality data, respectively. The benchmark results show an approximate 20% reduction in terms of mean squared error, proving the effectiveness of the model.

关键词： genetic perturbation predict biological knowledge masked attention variational autoencoder

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Separating group- and individual-level brain signatures in the newborn functional connectome: A deep learning approach

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NEUROIMAGE 2024年 299卷 120806-120806页

作者： Kim, Jung-Hoon De Asis-Cruz, Josepheen Limperopoulos, Catherine Childrens Natl Developing Brain Inst 111 Michigan Ave NW Washington DC 20010 USA

Recent studies indicate that differences in cognition among individuals may be partially attributed to unique brain wiring patterns. While functional connectivity (FC)-based fingerprinting has demonstrated high accuracy in identifying adults, early studies on neonates suggest that individualized FC signatures are absent. We posit that individual uniqueness is present in neonatal FC data and that conventional linear models fail to capture the rapid developmental trajectories characteristic of newborn brains. To explore this hypothesis, we employed a deep generative model, known as a variational autoencoder (VAE), leveraging two extensive public datasets: one comprising resting-state functional MRI (rs-fMRI) scans from 100 adults and the other from 464 neonates. VAE models trained on rs-fMRI from both adults and newborns produced superior age prediction performance (with r between predicted- and actual age similar to 0.7) and individual identification accuracy (similar to 45 %) compared to models trained solely on adult or neonatal data. The VAE model also showed significantly higher individual identification accuracy than linear models (=10 similar to 30 %). Importantly, the VAE differentiated connections reflecting age-related changes from those indicative of individual uniqueness, a distinction not possible with linear models. Moreover, we derived 20 latent variables, each corresponding to distinct patterns of cortical functional network (CFNs). These CFNs varied in their representation of brain maturation and individual signatures;notably, certain CFNs that failed to capture neurodevelopmental traits, in fact, exhibited individual signatures. CFNs associated with neonatal neurodevelopment predominantly encompassed unimodal regions such as visual and sensorimotor areas, whereas those linked to individual uniqueness spanned multimodal and transmodal brain regions. The VAE's capacity to extract features from rs-fMRI data beyond the capabilities of linear models posit

关键词： Neonate Resting-state fMRI variational autoencoder Subject identification Age prediction Individual uniqueness

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Rate Controllable Learned Image Compression Based on RFL Model

Rate Controllable Learned Image Compression Based on RFL Mod...

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IEEE International Conference on Visual Communications and Image Processing (VCIP)

作者： Zhang, Saiping Wang, Luge Mao, Xionghui Yang, Fuzheng Wan, Shuai Xidian Univ Sch Telecommun Engn Xian Peoples R China Northwestern Polytech Univ Sch Elect & Informat Xian Peoples R China

ISBN: (纸本)9781665475921

In this paper, we propose a rate controllable image compression framework, Rate Controllable variational autoencoder (RC-VAE), based on the Rate-Feature-Level (RFL) model established through our exploration on the correlation among target rates, image features and quantization levels. Considering that, when meeting the same target rate, different images should be quantized in different levels, we focus on jointly utilizing the target rate and the extracted features of the image to predict the corresponding quantization level and propose the RFL model. Combining the proposed RFL model with a Hyperprior Continuously Variable Rate (HCVR) image compression network, we further propose the RC-VAE. By controlling information loss in quantization process, the RC-VAE can work at the target rate. Experimental results have demonstrated that one single RC-VAE model can adapt to multiple target rates with higher rate control accuracy and better R-D performance compared with the stateof-the-art rate controllable image compression networks.

关键词： Deep image compression rate control variational autoencoder rate-distortion

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Active Patterns Perceived for Stochastic Video Prediction 22

Active Patterns Perceived for Stochastic Video Prediction

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30th ACM International Conference on Multimedia (MM)

作者： Xu, Yechao Sun, Zhengxing Li, Qian Sun, Yunhan Luo, Shoutong Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China Natl Univ Def Technol Coll Meteorol & Oceanog Changsha Hunan Peoples R China

ISBN: (纸本)9781450392037

Predicting future scenes based on historical frames is challenging, especially when it comes to the complex uncertainty in nature. We observe that there is a divergence between spatial-temporal variations of active patterns and non-active patterns in a video, where these patterns constitute visual content and the former ones implicate more violent movement. This divergence enables active patterns the higher potential to act with more severe future uncertainty. Meanwhile, the existence of non-active patterns provides an opportunity for machines to examine some underlying rules with a mutual constraint between non-active patterns and active patterns. In order to solve this divergence, we provide a method called active patterns-perceived stochastic video prediction (ASVP) which allows active patterns to be perceived by neural networks during training. Our method starts with separating active patterns along with non-active ones from a video. Then, both scene-based prediction and active pattern-perceived prediction are conducted to respectively capture the variations within the whole scene and active patterns. Specially for active pattern-perceived prediction, a conditional generative adversarial network (CGAN) is exploited to model active patterns as conditions, with a variational autoencoder (VAE) for predicting the complex dynamics of active patterns. Additionally, a mutual constraint is designed to improve the learning procedure for the network to better understand underlying interacting rules among these patterns. Extensive experiments are conducted on both KTH human action and BAIR action-free robot pushing datasets with comparison to state-of-the-art works. Experimental results demonstrate the competitive performance of the proposed method as we expected. The released code and models are at https://***/tolearnmuch/ASVP.

关键词： Video prediction Stochastic video prediction Conditional generative adversarial network variational autoencoder Active pattern mining

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Multi-Agent Reinforcement Learning with Clustering and Experience Sharing

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Procedia Computer Science 2024年 246卷 1329-1337页

作者： Kaname Inokuchi Toshiharu Sugawara Department of Computer Science and Communications Engineering Waseda University Japan

We propose a training method for a heterogeneous multi-agent system to improve the learning efficiency in sparse-reward environments. Although extensive research on multi-agent deep reinforcement learning are conducted actively, these studies often assume that all agents are homogeneous to share/utilize learning parameters in their networks. Unfortunately, this is not always the case in real-world applications where heterogeneous autonomous agents, i.e., those with different capabilities and perspectives, must properly cooperate and coordinate with each other. In our learning method, which is an extension of the shared experience actor-critic (SEAC) for a heterogeneous agent environment, agents are classified depending on their features (such as trajectories of the observations, actions and received rewards) using variational autoencoder, and share their experience among agents within each cluster to train their individual agents for improving the learning efficiency in a sparse-reward environment. Our experimental evaluation shows that the proposed method is capable of more efficient cooperative/coordinated behaviors than the baselines while remaining the advantages of SEAC.

关键词： Multi-agent deep reinforcement learning Experience Sharing variational autoencoder heterogeneous multi-agent systems Sparse-reward environment

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Unsupervised Deep Anomaly Detection in Chest Radiographs

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JOURNAL OF DIGITAL IMAGING 2021年第2期34卷 418-427页

作者： Nakao, Takahiro Hanaoka, Shouhei Nomura, Yukihiro Murata, Masaki Takenaga, Tomomi Miki, Soichiro Watadani, Takeyuki Yoshikawa, Takeharu Hayashi, Naoto Abe, Osamu Univ Tokyo Hosp Dept Computat Diagnost Radiol & Prevent Med Bunkyo Ku 7-3-1 Hongo Tokyo Japan Univ Tokyo Hosp Dept Radiol Bunkyo Ku 7-3-1 Hongo Tokyo Japan Japan Univ Econ Dept Management 3-11-25 Gojo Dazaifu Fukuoka Japan Univ Tokyo Grad Sch Med Div Radiol & Biomed Engn Bunkyo Ku 7-3-1 Hongo Tokyo Japan

The purposes of this study are to propose an unsupervised anomaly detection method based on a deep neural network (DNN) model, which requires only normal images for training, and to evaluate its performance with a large chest radiograph dataset. We used the auto-encoding generative adversarial network (alpha-GAN) framework, which is a combination of a GAN and a variational autoencoder, as a DNN model. A total of 29,684 frontal chest radiographs from the Radiological Society of North America Pneumonia Detection Challenge dataset were used for this study (16,880 male and 12,804 female patients;average age, 47.0 years). All these images were labeled as "Normal," "No Opacity/Not Normal," or "Opacity" by board-certified radiologists. About 70% (6,853/9,790) of the Normal images were randomly sampled as the training dataset, and the rest were randomly split into the validation and test datasets in a ratio of 1:2 (7,610 and 15,221). Our anomaly detection system could correctly visualize various lesions including a lung mass, cardiomegaly, pleural effusion, bilateral hilar lymphadenopathy, and even dextrocardia. Our system detected the abnormal images with an area under the receiver operating characteristic curve (AUROC) of 0.752. The AUROCs for the abnormal labels Opacity and No Opacity/Not Normal were 0.838 and 0.704, respectively. Our DNN-based unsupervised anomaly detection method could successfully detect various diseases or anomalies in chest radiographs by training with only the normal images.

关键词： Chest radiograph variational autoencoder Generative adversarial network Deep learning Unsupervised learning Anomaly detection

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Disentangling Rotational Dynamics and Ordering Transitions in a System of Self-Organizing Protein Nanorods via Rotationally Invariant Latent Representations

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ACS NANO 2021年第4期15卷 6471-6480页

作者： Kalinin, Sergei, V Zhang, Shuai Valleti, Mani Pyles, Harley Baker, David De Yoreo, James J. Ziatdinov, Maxim Univ Washington Mat Sci & Engn Seattle WA 98195 USA Pacific Northwest Natl Lab Phys Sci Div Richland WA 99354 USA Univ Tennessee Bredesen Ctr Interdisciplinary Res Knoxville TN 37996 USA Univ Washington Dept Biochem Seattle WA 98195 USA Univ Washington Inst Prot Design Seattle WA 98195 USA Univ Washington Inst Prot Design Dept Biochem Seattle WA 98195 USA Univ Washington Howard Hughes Med Inst Seattle WA 98195 USA Oak Ridge Natl Lab Ctr Nanophase Mat Sci Oak Ridge TN 37831 USA Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN 37831 USA

The dynamics of complex ordering systems with active rotational degrees of freedom exemplified by protein self-assembly is explored using a machine learning workflow that combines deep learning-based semantic segmentation and rotationally invariant variational autoencoder-based analysis of orientation and shape evolution. The latter allows for disentanglement of the particle orientation from other degrees of freedom and compensates for lateral shifts. The disentangled representations in the latent space encode the rich spectrum of local transitions that can now be visualized and explored via continuous variables. The time dependence of ensemble averages allows insight into the time dynamics of the system and, in particular, illustrates the presence of the potential ordering transition. Finally, analysis of the latent variables along the single-particle trajectory allows tracing these parameters on a single-particle level. The proposed approach is expected to be universally applicable for the description of the imaging data in optical, scanning probe, and electron microscopy seeking to understand the dynamics of complex systems where rotations are a significant part of the process.

关键词： self-assembly latent space models deep learning variational autoencoder representation learning

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An Efficient Prediction Method for Coronary Heart Disease Risk Based on Two Deep Neural Networks Trained on Well-Ordered Training Datasets

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IEEE ACCESS 2021年 9卷 135210-135223页

作者： Amarbayasgalan, Tsatsral Pham, Van-Huy Theera-Umpon, Nipon Piao, Yongjun Ryu, Keun Ho Chungbuk Natl Univ Sch Elect & Comp Engn Database & Bioinformat Lab Cheongju 28644 South Korea Ton Duc Thang Univ Fac Informat Technol Ho Chi Minh City 700000 Vietnam Chiang Mai Univ Biomed Engn Inst Chiang Mai 50200 Thailand Chiang Mai Univ Fac Engn Dept Elect Engn Chiang Mai 50200 Thailand Nankai Univ Sch Med Tianjin 300071 Peoples R China Tianjin Cent Hosp Gynecol Obstet Tianjin Key Lab Human Dev & Reprod Regulat Tianjin 300199 Peoples R China

This study proposes an efficient prediction method for coronary heart disease risk based on two deep neural networks trained on well-ordered training datasets. Most real datasets include an irregular subset with higher variance than most data, and predictive models do not learn well from these datasets. While most existing prediction models learned from the whole or randomly sampled training datasets, our suggested method draws up training datasets by separating regular and highly biased subsets to build accurate prediction models. We use a two-step approach to prepare the training dataset: (1) divide the initial training dataset into two groups, commonly distributed and highly biased using Principal Component Analysis, (2) enrich the highly biased group by variational autoencoders. Then, two deep neural network classifiers learn from the isolated training groups separately. The well-organized training groups enable a chance to build more accurate prediction models. When predicting the risk of coronary heart disease from the given input, only one appropriate model is selected based on the reconstruction error on the Principal Component Analysis model. Dataset used in this study was collected from the Korean National Health and Nutritional Examination Survey. We have conducted two types of experiments on the dataset. The first one proved how Principal Component Analysis and variational autoencoder models of the proposed method improves the performance of a single deep neural network. The second experiment compared the proposed method with existing machine learning algorithms, including Naive Bayes, Random Forest, K-Nearest Neighbor, Decision Tree, Support Vector Machine, and Adaptive Boosting. The experimental results show that the proposed method outperformed conventional machine learning algorithms by giving the accuracy of 0.892, specificity of 0.840, precision of 0.911, recall of 0.920, f-measure of 0.915, and AUC of 0.882.

关键词： Training Heart Principal component analysis Predictive models Diseases Deep learning Support vector machines Coronary heart disease deep neural network machine learning principal component analysis reconstruction error variational autoencoder

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