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Slice-to-voxel stochastic reconstructions on porous media with hybrid deep generative model

COMPUTATIONAL MATERIALS SCIENCE 2021年 186卷 110018-110018页

作者： Zhang, Fan Teng, Qizhi Chen, Honggang He, Xiaohai Dong, Xiucheng Sichuan Univ Coll Elect & Informat Engn Chengdu Peoples R China Xihua Univ Sch Elect Engn & Elect Informat Chengdu Peoples R China Minist Educ Key Lab Wireless Power Transmiss Chengdu Peoples R China

Three-dimensional (3D) microstructures are useful for studying the spatial structures and physical properties of porous media. A number of stochastic reconstructions are essential to discover the geometry and topology of the porous media and the its flow behavior. While several deep-learning based generative models have been proposed to deal with the issue, the obstacles about stable training and difficulty in convergence limit the application of these models. To address these problems, a hybrid deep generative model for 3D porous media reconstruction is proposed. The hybrid model is composed of a variant autoencoder (VAE) and a generative adversarial network (GAN). It receives a two-dimensional image as input and generates 3D porous media. The encoder from VAE characterizes the statistical and morphological information of input image and generates a low-dimensional feature vector for generator. Benefiting from the hybrid model, the training becomes more stable and the generative capability is enhanced as well. Furthermore, a simple but useful loss function is used to help improve accuracy. The proposed model is tested on both isotropic and anisotropic porous media. The results show the synthetic realizations have good agreement to the targets on visual inspection, statistical functions and two-phase flow simulation.

关键词： Porous media 3D reconstruction Generative adversarial networks variational autoencoder Hybrid deep generative model

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Deep generative learning for exploration in large electrochemical impedance dataset

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartC期126卷

作者： Doonyapisut, Dulyawat Kim, Byeongkyu Kim, Jung Kyu Lee, Eunseok Chung, Chan-Hwa Sungkyunkwan Univ Sch Chem Engn Suwon 16419 South Korea Sungkyunkwan Univ Coll Comp & Informat Suwon 16419 South Korea

The fields of energy storage, photocatalysis, and sensors have undergone substantial technological advancements, which have led to the generation of vast amounts of data on electrochemical impedance (EIS). The interpretation of large amounts of EIS data is a challenging task since the analysis of EIS data requires multiple steps to get a suitable equivalent circuit. Recently, some progress has been made in the machine learning (ML) model for EIS classification. However, most of the ML models are performed as a "black box" model, which provides only the classification result and lacks physical descriptor representation. Here, we apply variational autoencoders (VAE) to EIS data analysis, which includes classification, parameter prediction, and the visualization of physical descriptors. The VAE model performed well in the classification task, with an accuracy of 82.0%-92.4%. In the prediction task, VAE shows a high R-squared value on the Randles circuit. Additionally, the VAE model can map physical descriptors to the latent space, allowing the latent space to transform into a property space, which plays an important role in the optimization and exploration of novel materials research.

关键词： Electrochemical impedance spectroscopy Equivalent circuit model variational autoencoder Impedance optimization Impedance visualization

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Data-Driven Virtual Material Analysis and Synthesis for Solid Electrolyte Interphases

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ADVANCED ENERGY MATERIALS 2023年第40期13卷 2301985-2301985页

作者： Rajagopal, Deepalaxmi Koeppe, Arnd Esmaeilpour, Meysam Selzer, Michael Wenzel, Wolfgang Stein, Helge Nestler, Britta Karlsruhe Inst Technol KIT Inst Appl Mat IAM MMS Str Forum 7 D-76131 Karlsruhe Germany Karlsruhe Inst Technol KIT Inst Nanotechnol INT Hermann Von Helmholtz Pl 1 D-76344 Eggenstein Leopoldshafen Germany Helmholtz Inst Ulm Lise Meitner Str 16 D-89081 Ulm Germany

Solid electrolyte interphases (SEIs) form as reduction products at the electrodes and strongly affect battery performance and safety. Because SEI formation poses a highly nonlinear, complex multi-physics problem over various lengths and time scales, traditional modeling approaches struggle to characterize SEI evolution solely with existing physical properties. To improve the characterization of SEIs, it proposes a data-driven strategy for a virtual material design that learns to represent and characterize SEI formation with physical and data-driven properties from kinetic Monte Carlo simulations. A variational autoencoder with a property regressor learns data-driven properties, which represent SEI configurations and correlate with physical target properties. This new neural network design encodes the high-dimensional structural and reaction spaces into a lower-dimensional latent space, while the property regressor orders the latent space by physical target properties. The model achieves high correlation scores between target and predicted properties from latent representations, thereby proving that the data-driven properties enrich the expressiveness of SEI characterizations. A recent study has developed a parallel regressor variational autoencoder tool that can automate the characterization of solid electrolyte interphase (SEI) in batteries. This tool can synthesize and explain SEI configurations by analyzing data-driven properties encoded with physical properties. The study also investigates the impact of reaction barriers on SEI ***

关键词： kinetic Monte Carlo simulations solid electrolyte interphases variational autoencoder virtual material design

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Deep generative fuel design in low data regimes via multi-objective imitation

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CHEMICAL ENGINEERING SCIENCE 2023年第1期274卷

作者： Liu, Yifan Liu, Runze Duan, Jinyu Wang, Li Zhang, Xiangwen Li, Guozhu Tianjin Univ Sch Chem Engn & Technol Key Lab Green Chem Technol Minist Educ Tianjin 300072 Peoples R China Collaborat Innovat Ctr Chem Sci & Engn Tianjin Tianjin 300072 Peoples R China Haihe Lab Sustainable Chem Transformat Tianjin 300192 Peoples R China

Commercial fuel discovery faces a constantly decreasing return of investment due to due to increasingly tight environmental criteria and reducing potential uses for each new fuel. In this paper, a deep generative model, termed Latent Interspace Generative Adversarial Network with a Domain of Stacking (LIGANDS), has been established to screen desired fuel molecules in the large chemical space without setting design rules manually. A variational autoencoder, a generative adversarial network and a stacking model are well integrated in LIGANDS through model convergence. Given only the structures of 255 typical highenergy???density fuels in low data regimes, LIGANDS generated 3461 new fuel molecules with similar property distribution and improved energy performance as the qualified candidates of next-generation fuels. To expand and enrich the fuel-relevant chemical space with innovative molecular entities on demand, in-depth multi-objective imitation on the key properties of target fuel is realized by LIGANDS through optimizing generative molecular structures and their distribution. ?? 2023 Elsevier Ltd. All rights reserved.

关键词： Deep learning Generative model Fuel variational autoencoder Generative adversarial network Ensemble learning

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Knowledge Graph and Personalized Answer Sequences for Programming Knowledge Tracing

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APPLIED SCIENCES-BASEL 2024年第17期14卷 7952页

作者： Pan, Jianguo Dong, Zhengyang Yan, Lijun Cai, Xia Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai 200234 Peoples R China

Knowledge tracing is a significant research area in educational data mining, aiming to predict future performance based on students' historical learning data. In the field of programming, several challenges are faced in knowledge tracing, including inaccurate exercise representation and limited student information. These issues can lead to biased models and inaccurate predictions of students' knowledge states. To effectively address these issues, we propose a novel programming knowledge tracing model named GPPKT (Knowledge Graph and Personalized Answer Sequences for Programming Knowledge Tracing), which enhances performance by using knowledge graphs and personalized answer sequences. Specifically, we establish the associations between well-defined knowledge concepts and exercises, incorporating student learning abilities and latent representations generated from personalized answer sequences using variational autoencoders (VAE) in the model. This deep knowledge tracing model employs Long Short-Term Memory (LSTM) networks and attention mechanisms to integrate the embedding vectors, such as exercises and student information. Extensive experiments are conducted on two real-world programming datasets. The results indicate that GPPKT outperforms state-of-the-art methods, achieving an AUC of 0.8840 and an accuracy of 0.8472 on the Luogu dataset, and an AUC of 0.7770 and an accuracy of 0.8799 on the Codeforces dataset. This demonstrates the superiority of the proposed model, with an average improvement of 9.03% in AUC and 2.02% in accuracy across both datasets.

关键词： knowledge graph programming learning variational autoencoder knowledge tracing LSTM

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Few-Shot User-Adaptable Radar-Based Breath Signal Sensing

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SENSORS 2023年第2期23卷 804-804页

作者： Mauro, Gianfranco Diez, Maria De Carlos Ott, Julius Servadei, Lorenzo Cuellar, Manuel P. Morales-Santos, Diego P. Infineon Technol AG Am Campeon 1 15 D-85579 Neubiberg Germany Univ Granada Dept Elect & Comp Technol Ave de Fuente Nueva s n Granada 18071 Spain Tech Univ Munich Dept Elect & Comp Engn Arcis str 21 D-80333 Munich Germany Univ Granada Dept Comp Sci & Artificial Intelligence C Pdta Daniel Saucedo Aranda s n Granada 18015 Spain

Vital signs estimation provides valuable information about an individual's overall health status. Gathering such information usually requires wearable devices or privacy-invasive settings. In this work, we propose a radar-based user-adaptable solution for respiratory signal prediction while sitting at an office desk. Such an approach leads to a contact-free, privacy-friendly, and easily adaptable system with little reference training data. Data from 24 subjects are preprocessed to extract respiration information using a 60 GHz frequency-modulated continuous wave radar. With few training examples, episodic optimization-based learning allows for generalization to new individuals. Episodically, a convolutional variational autoencoder learns how to map the processed radar data to a reference signal, generating a constrained latent space to the central respiration frequency. Moreover, autocorrelation over recorded radar data time assesses the information corruption due to subject motions. The model learning procedure and breathing prediction are adjusted by exploiting the motion corruption level. Thanks to the episodic acquired knowledge, the model requires an adaptation time of less than one and two seconds for one to five training examples, respectively. The suggested approach represents a novel, quickly adaptable, non-contact alternative for office settings with little user motion.

关键词： vital sign sensing respiration signal artificial neural networks meta-learning radar FMCW few-shot learning autocorrelation variational autoencoder signal processing

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Data Augmentation for a Virtual-Sensor-Based Nitrogen and Phosphorus Monitoring

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SENSORS 2023年第3期23卷 1061页

作者： Paepae, Thulane Bokoro, Pitshou N. Kyamakya, Kyandoghere Univ Johannesburg Dept Elect & Elect Engn Technol ZA-2028 Doornfontein South Africa Alpen Adria Univ Klagenfurt Inst Smart Syst Technol Transportat Informat A-9020 Klagenfurt Austria Univ Kinshasa Fac Polytech PO Box 127 11 Kinshasa DEM REP CONGO

To better control eutrophication, reliable and accurate information on phosphorus and nitrogen loading is desired. However, the high-frequency monitoring of these variables is economically impractical. This necessitates using virtual sensing to predict them by utilizing easily measurable variables as inputs. While the predictive performance of these data-driven, virtual-sensor models depends on the use of adequate training samples (in quality and quantity), the procurement and operational cost of nitrogen and phosphorus sensors make it impractical to acquire sufficient samples. For this reason, the variational autoencoder, which is one of the most prominent methods in generative models, was utilized in the present work for generating synthetic data. The generation capacity of the model was verified using water-quality data from two tributaries of the River Thames in the United Kingdom. Compared to the current state of the art, our novel data augmentation-including proper experimental settings or hyperparameter optimization-improved the root mean squared errors by 23-63%, with the most significant improvements observed when up to three predictors were used. In comparing the predictive algorithms' performances (in terms of the predictive accuracy and computational cost), k-nearest neighbors and extremely randomized trees were the best-performing algorithms on average.

关键词： water-quality monitoring eutrophication synthetic data soft sensor surrogate variables variational autoencoder machine learning deep neural network parameter optimization

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HN3S: A Federated autoencoder framework for Collaborative Filtering via Hybrid Negative Sampling and Secret Sharing

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INFORMATION PROCESSING & MANAGEMENT 2024年第2期61卷

作者： Zhang, Lu Li, Guohui Yuan, Ling Ding, Xuanang Rong, Qian Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Software Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China

Federated recommender systems can serve users with suitable item recommendations while preserving their privacy, but most current works cannot serve non-participant users. We consider that Federated autoencoder for Collaborative Filtering (FAE-CF) is expected to solve this problem. However, FAE-CF encounters privacy protection concerns and high communication overhead issues. In this paper, we investigate FAE-CF's privacy protection capabilities and communication volume, unveiling its potential privacy security issues and inferior practicality. By analyzing the FAE-CF gradient, we propose an effective attack that could recover most user data, with all recovery metrics above 95.53% in our simulation experiments. To address these challenges, we propose Hybrid Negative Sampling and Secret Sharing (HN3S), a privacy preserving method designed for FAE-CF. HN3S uses hybrid negative sampling to reduce communication overhead and secret sharing to protect user data. Experiments conducted on various public datasets with multiple AE-CFs have shown that HN3S can effectively protect the privacy of FAE-CF, reducing the communication overhead by up to 60.44%, while maintaining the performance reduction within 10.78%. Our research reveals significant privacy risks and poor utility inherent in current FAE-CFs, and provides a feasible solution to protect user privacy and balance communication overhead with algorithm performance.

关键词： Federated recommendation Collaborative filtering variational autoencoder Negative sampling Secret sharing

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Neural networks based domain name generation

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JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2021年 61卷

作者： Wang, Zheng Guo, Yang NIST Gaithersburg MD 20899 USA

Domain generation algorithm (DGA) is used by botnets to build a stealthy command and control (C&C) communication channel between the C&C server and the bots. A DGA can periodically produce a large number of pseudo-random algorithmically generated domains (AGDs), a few of which direct the bots to the C&C server. AGD detection algorithms provide a lightweight, promising solution in response to the existing DGA techniques. In the constantly evolving attacker-defender game, attackers may seek more advanced DGA techniques to gain a better chance of evading detection by defenders. In this paper, we propose a new DGA, namely a neural networks-based domain name generation (NDG) architecture. NDG is based on a variational autoencoder (VAE), where the encoder and decoder networks use stacked gated convolutional neural networks (GCNNs) to learn the contextual structure hierarchically. NDG is experimentally validated using a set of state-of-the-art AGD detection algorithms. The existing DGAs of different classes following a DGA taxonomy are used to benchmark NDG. NDG shows the best overall anti-detection performance among all tested DGAs. We also demonstrate that NDG is effective in benchmarking AGD detection algorithms.

关键词： Domain generation algorithm Malicious domain name Classification variational autoencoder Deep learning

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Pathogenicity classification of missense mutations based on deep generative model

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COMPUTERS IN BIOLOGY AND MEDICINE 2024年 170卷 107980-107980页

作者： Bai, Ke Yang, Lu Xue, Jian Zhao, Lin Hao, Fanchang Shandong Jianzhu Univ Jinan 250101 Peoples R China

Missense mutations affect the function of human proteins and are closely associated with multiple acute and chronic diseases. The identification of disease-associated missense mutations and their classification for pathogenicity can provide insights into the genetic basis of disease and protein function. This paper proposes MLAE (Method based on LSTM-Ladder autoencoder), a deep learning classification model for identifying disease-associated missense mutations and classifying their pathogenicity based on the variational autoencoder (VAE) framework. MLAE overcomes the limitations of the VAE framework by introducing the Ladder structure, combined with LSTM networks. This reduces the loss of original information during the transmission process, thereby making the model more effective in learning. In the experiment, MLAE classified all 27572 possible missense variants of the three input proteins with an average classification AUC of 0.941. This result provides evidence that MLAE is effective in predicting pathogenicity. Additionally, MLAE provides results for multi-label classification, with an average Hamming loss of 0.196, supporting the classification of complex variants. The proposed MLAE method provides an insightful approach to effectively capture amino acid sequence information and accurately predict the pathogenicity of mutations, thereby providing an analytical basis for the study and prevention of related diseases.

关键词： Single amino acid variation Deep generation model variational autoencoder Pathogenicity classification Multiple label classification

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