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Data-Driven Discovery of 2D Materials for Solar Water Splitting

FRONTIERS IN MATERIALS 2021年 8卷

作者： Agarwal, Abhishek Goverapet Srinivasan, Sriram Rai, Beena Tata Consulatncy Serv Ltd TCS Res Pune Maharashtra India

Hydrogen economy, wherein hydrogen is used as the fuel in the transport and energy sectors, holds significant promise in mitigating the deleterious effects of global warming. Photocatalytic water splitting using sunlight is perhaps the cleanest way of producing the hydrogen fuel. Among various other factors, widespread adoption of this technology has mainly been stymied by the lack of a catalyst material with high efficiency. 2D materials have shown significant promise as efficient photocatalysts for water splitting. The availability of open databases containing the "computed" properties of 2D materials and advancements in deep learning now enable us to do "inverse" design of these 2D photocatalysts for water splitting. We use one such database (Jain et al., ACS Energ. Lett. 2019, 4, 6, 1410-1411) to build a generative model for the discovery of novel 2D photocatalysts. The structures of the materials were converted into a 3D image-based representation that was used to train a cell, a basis autoencoder and a segmentation network to ascertain the lattice parameters as well as position of atoms from the images. Subsequently, the cell and basis encodings were used to train a conditional variational autoencoder (CVAE) to learn a continuous representation of the materials in a latent space. The latent space of the CVAE was then sampled to generate several new 2D materials that were likely to be efficient photocatalysts for water splitting. The bandgap of the generated materials was predicted using a graph neural network model while the band edge positions were obtained via empirical correlations. Although our generative modeling framework was used to discover novel 2D photocatalysts for water splitting reaction, it is generic in nature and can be used directly to discover novel materials for other applications as well.

关键词： generative modeling variational autoencoder inverse design photocatalysts water splitting reaction

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Improving disentanglement in variational auto-encoders via feature imbalance-informed dimension weighting

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KNOWLEDGE-BASED SYSTEMS 2024年 296卷

作者： Liu, Yue Yu, Zhenyao Liu, Zitu Yu, Ziyi Yang, Xinyan Li, Xingyue Guo, Yike Liu, Qun Wang, Guoyin Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Shanghai Engn Res Ctr Intelligent Comp Syst Shanghai 200444 Peoples R China Imperial Coll London Dept Comp London SW7 2AZ England Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing 400065 Peoples R China Shanghai Univ Coll Sci Shanghai 200444 Peoples R China

Using variational Auto-Encoder (VAE) to learn disentangled representation holds great promise. But there is a feature imbalance in the learning process of VAEs, and the model usually concentrates on the learning of some dimensions at the expense of others. Instead of attempting to rectify it, we exploit feature imbalance to propose a Dimension Weighting method that can boost the disentanglement effect of VAE-based models. In order to conduct disentanglement learning under a fixed size of the latent space, the intrinsic dimension of raw data is estimated by a Dimension Number Estimator and set as the latent space size. Then, leveraging the feature imbalance, a Dimension Importance Evaluator is constructed to separate each dimension in the latent variable into important, unimportant, and general dimensions. By exerting different learning pressures on specific dimensions, we further optimize the variational lower bound of model and retrain it, thus promoting the disentanglement of important dimensions. The experiments on four benchmark datasets show that the Dimension Weighting can further improve the disentangling effect without compromising model performance, and in approximately 80% of cases, the results in disentangling metric evaluation experiments achieve better disentangling scores than the original models. This reveals that not all dimensions of the latent variables are equally influential. By focusing on the crucial dimensions within the latent representation, the model can achieve better performance.

关键词： variational autoencoder Latent space Disentangled representation Dimension weighting

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Incremental learning model for dynamical identification and classification of abnormal vibration in operational underground facilities

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2023年第1期140卷

作者： Chai, Fu Zhou, Biao Xie, Xiongyao Zhang, Zixin Wang, Chen Tongji Univ Key Lab Geotech & Underground Engn Minist Educ Shanghai 200092 Peoples R China Tongji Univ Dept Geotech Engn Shanghai 200092 Peoples R China State Grid Shanghai Municipal Elect Power Co Shanghai 200072 Peoples R China

Underground infrastructures are rapidly growing in size and complexity. However, their operations are affected by several hazards, including hidden structural deterioration and effects of random external constructions. Dynamic monitoring of these hazards is essential to provide early warning. We propose a vibration-based self-supervised incremental learning model for dynamic monitoring of emerging operational threats by recognizing abnormal responses. The model comprises teacher and student models based on a variational autoencoder (VAE) with a metric function. When the teacher model detects a new category of abnormal vibration, the student model is trained to recognize this abnormality through sample rehearsals and knowledge distillations. Subsequently, it becomes the teacher model for the next round of incremental learning. We demonstrate through a case study that catastrophic forgetting can be avoided and memory consumption can be reduced during dynamic network updates. Moreover, the use of a metric function in the VAE increases the vibration identification accuracy.

关键词： Underground infrastructure Monitoring Incremental learning variational autoencoder Metric function

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Deep Neural Network-based Optimization Framework for Safety Evacuation Route during Toxic Gas Leak Incidents

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2022年第PartA期218卷 108102-108102页

作者： Seo, Seung-Kwon Yoon, Young-Gak Lee, Ju-sung Na, Jonggeol Lee, Chul-Jin Chung Ang Univ Sch Chem Engn & Mat Sci 84 Heukseok Ro Seoul 06979 South Korea Samsung Engn Proc Engn Team Seoul 05288 South Korea Ewha Womans Univ Dept Chem Engn & Mat Sci Grad Program Syst Hlth Sci & Engn Seoul 03760 South Korea Chung Ang Univ Dept Intelligent Energy & Ind Seoul 06979 South Korea

Evacuation planning is important for reducing casualties in toxic gas leak incidents. However, most evacuation plans are too qualitative to be applied to unexpected practical situations. Here, we suggest an evacuation route proposal system based on a quantitative risk evaluation that provides the safest route for individual evacuees by predicting dynamic gas dispersion with a high accuracy and short calculation time. Detailed evacuation scenarios, including weather conditions, leak intensity, and evacuee information, were considered. The proposed system evaluates the quantitative risk in the affected area using a deep neural network surrogate model to determine optimal evacuation routes by integer programming. The surrogate model was trained using data from computational fluid dynamics simulations. A variational autoencoder was applied to extract the geometric features of the affected area. The predicted risk was combined with linearized integer programming to determine the optimal path in a predefined road network. A leak scenario of an ammonia gas pipeline in a petrochemical complex was used for the case study. The results show that the developed model offers the safest route within a few seconds with minimum risk. The developed model was applied to a sensitivity analysis to determine variable influences and safe shelter locations.

关键词： Evacuation Toxic gas leak Computational fluid dynamics Surrogate model variational autoencoder

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Simulating real profiles for shilling attacks: A generative approach

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KNOWLEDGE-BASED SYSTEMS 2021年 230卷 107390-107390页

作者： Barbieri, Julio Alvim, Leandro G. M. Braida, Filipe Zimbrao, Geraldo Univ Fed Rio de Janeiro PESC COPPE CT Cidade UnivPOB 68511 Rio De Janeiro Brazil Univ Fed Rural Rio de Janeiro DCC IM BR-26020740 Rio De Janeiro Brazil

Collaborative Filtering (CF) approaches are vulnerable to Shilling Attacks, in which malicious users or companies inject a large number of fake profiles in a system in order to manipulate its recommendations. One problem of current Shilling Attack models is that they commonly use straightforward statistical templates, producing profiles with different rating patterns than actual system data, which facilitates its detection, requiring a larger amount of profiles to achieve its goals. To address this problem and create profiles closer to reality, we propose using a generative model, variational autoencoder (VAE) to map original data distribution. With VAE, it is possible to generate new profiles based on real data, without explicit copying their actual ratings. Its generated profiles are converted to malicious profiles by adding target item rating value. We test our attack model on MovieLens 100k data set and compare to literature attack models. Our results indicate that our model outperforms all other models in model-based CF system, especially using low attack sizes (from 3% to 5%). Also, analysis comparing profiles generated from it and other approaches shows that our model ratings pattern are very similar to real profiles, which may indicate that attacks mounted using our approach may be less likely to be detected by detection approaches. Thus, we show that our attack model represents an advance on Shilling Attack models, since its superior results in model-based CF and possible indistinction from real profiles may be useful as a baseline to test detection techniques and other tasks among Shilling Attack area. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Recommender systems Collaborative Filtering Shilling Attack Generative Model variational autoencoder

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A semi-supervised deep learning approach for vessel trajectory classification based on AIS data

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OCEAN & COASTAL MANAGEMENT 2022年第0期218卷 106015-106015页

作者： Duan, Hongda Ma, Fei Miao, Lixin Zhang, Canrong Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Tsinghua Univ Res Ctr Modern Logist Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen 518055 Peoples R China

Automatic identification system (AIS) refers to a new type of navigation aid system equipped in maritime vehicles to monitor ship performance. It provides trajectory information of vessels which can be used for the classification task. The classification results facilitate ocean surveillance and conservation, vessel management enhancement, fishery regulation, marine ecological sustainability, and nautical safety protection. Some progresses have been made based on machine learning or deep learning strategies to perform supervised learning by assuming that during the training process, the category labels of historical trajectory data are available. However, in reality, the label information may be difficult or expensive to obtain, resulting in that only a small part of the training data is labeled, and most of the training data is unlabeled. To address this issue, this paper proposes a semi-supervised deep learning approach to integrate the knowledge of unlabeled data for vessel trajectory classification. Here, we call our approach SSL-VTC. Specifically, we first extract vessel trajectories by integrating the kinematic and static information from historical AIS messages. Then, we design convolutional neural networks to extract feature representations from the vessel trajectories. Finally, we develop a semi-supervised learning algorithm based on the variational autoencoder to perform discriminative learning and generative learning simultaneously. In this way, our SSL-VTC framework can fully leverage the labeled data and unlabeled data during the training process. To the best of our knowledge, we are the first to utilize semi-supervised learning for vessel trajectory classification. Experimental results on the public AIS dataset show that our SSL-VTC can effectively extract feature representations from the AIS messages and its performance is significantly better than the traditional supervised learning methods. The approach and findings of this study provide important i

关键词： Automatic identification system Vessel trajectory classification Deep learning Semi-supervised learning variational autoencoder

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Matrix-variate variational auto-encoder with applications to image process

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2020年 67卷 102750-102750页

作者： Li, Jinghua Yan, Huixia Gao, Junbin Kong, Dehui Wang, Lichun Wang, Shaofan Yin, Baocai Beijing Univ Technol Fac Informat Technol Beijing Key Lab Multimedia & Intelligent Software Beijing Peoples R China Univ Sydney Univ Sydney Business School Discipline Business Analyt Sydney NSW 2006 Australia Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian Peoples R China

variational Auto-Encoder (VAE) is an important probabilistic technology to model 1D vectorial data. However, when applying VAE model to 2D image, vectorization is necessary. Vectorization process may lead to dimension curse and lose valuable spatial information. To avoid these problems, we propose a novel VAE model based on matrix variables named as Matrix-variate variational Auto-Encoder (MVVAE). In this model, input, hidden and latent variables are all in matrix form, therefore inherent spatial structure of 2D images can be maintained and utilized better. Especially, the latent variable is assumed to follow matrix Gaussian distribution which is more suitable for describing 2D images. To solve the weights and the posterior of latent variable, the variational inference process is given. The experiments are designed for three real-world application: reconstruction, denoising and completion. The experimental results demonstrate that MVVAE shows better performance than VAE and other probabilistic methods for modeling and processing 2D data. (C) 2020 Elsevier Inc. All rights reserved.

关键词： variational autoencoder Matrix Gaussian distribution variational inference Face completion Image denoising

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VAEAT: variational AutoeEncoder with adversarial training for multivariate time series anomaly detection

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INFORMATION SCIENCES 2024年 676卷

作者： He, Sheng Du, Mingjing Jiang, Xiang Zhang, Wenbin Wang, Congyu Jiangsu Normal Univ Sch Comp Sci & Technol Xuzhou 221116 Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China

High labor costs and the requirement for significant domain expertise often result in a lack of anomaly labels in most time series. Consequently, employing unsupervised methods becomes critical for practical industrial applications. However, prevailing reconstruction-based anomaly detection algorithms encounter challenges in capturing intricate underlying correlations and temporal dependencies in time series. This study introduces an unsupervised anomaly detection model called variational AutoeEncoder with Adversarial Training for Multivariate Time Series Anomaly Detection (VAEAT). Its fundamental concept involves adopting a two-phase training strategy to improve anomaly detection precision through adversarial reconstruction of raw data. In the first phase, the model reconstructs raw data to extract its basic features by training two enhanced variational autoencoders (VAEs) that incorporate both the long short -term memory (LSTM) network and the attention mechanism in their common encoder. In the second phase, the model refines reconstructed data to optimize the reconstruction quality. In this manner, this two-phase VAE model effectively captures intricate underlying correlation and temporal dependencies. A large number of experiments are conducted to evaluate the performance on five publicly available datasets, and experimental results illustrate that VAEAT exhibits robust performance and effective anomaly detection capabilities. The source code of the proposed VAEAT can be available at https://github .com /Du -Team /VAEAT.

关键词： Multivariate time series Anomaly detection variational autoencoder Adversarial training

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Allocation of flood drainage rights in the middle and lower reaches of the Yellow River based on deep learning and flood resilience

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JOURNAL OF HYDROLOGY 2022年第PartA期615卷

作者： Zhang, Kaize Dong, Zengchuan Guo, Li Boyer, Elizabeth W. Mello, Carlos R. Shen, Juqin Lan, Ping Wang, Jianlin Fan, Bihang Hohai Univ Coll Hydrol & Water Resources Nanjing 211100 Peoples R China Sichuan Univ Coll Water Resource & Hydropower State Key Lab Hydraul & Mt River Engn Chengdu 610065 Peoples R China Penn State Univ Dept Ecosyst Sci & Management University Pk PA 16802 USA Univ Fed Lavras Water Resources Dept CP 3037 BR-37200900 Lavras MG Brazil Hohai Univ Coll Agr Engn Nanjing 210098 Peoples R China Henan Univ Sch Comp & Informat Engn Kaifeng 475000 Peoples R China

Climate change has increased the intensity and frequency of storms in many world regions, calling for new flood planning and management strategies. The concept of flood drainage rights (FDR), or the legal rights of regions to drain floodwaters into river reaches, is used in watershed planning in China. Quantifying the allocation of FDR remains challenging, where some previous methods have resulted in unreasonable or impractical allocation plans due to incomplete consideration of driving factors or the use of unscientific allocation methods. This study ex-plores the allocation plan of FDR in the middle and lower reaches of the Yellow River Watershed in China. Climatic variability and change have caused frequent flooding in portions of the basin, with significant societal and economic implications. First, we comprehensively analyzed factors driving FDR for regions in the watershed. Following the conceptual flood resilience strategy currently being advocated for the region, we considered natural, socioeconomic, governance, resilience, and resistance factors that influence the complex allocation of FDR and established a qualitative indicator system to reflect the complexity of these driving factors. Second, we quantified FDR values for flood-prone regions in the middle and lower river reaches of this major river basin. We introduced a specific deep learning method, called the variational autoencoder (VAE) model, to quantify FDR allocation, providing a robust solution to the challenge of the multi-objective, high-dimensional, nonlinear, and non-normal distribution of factors driving FDR allocation. Next, using data from 2005 to 2019, this model was applied to the study area. The allocation of FDR (summing to 100%) across five flood-prone provinces of the watershed includes Inner Mongolia (9.36%), Shaanxi (10.00%), Shanxi (10.95%), Henan (32.58%), and Shan-dong (37.12%). Using the harmony evaluation method based on harmony theory, we compared the new VAE allocation method

关键词： Flood drainage rights Flood resilience strategy Deep learning variational autoencoder Harmonious evaluation Yellow River

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Multimodal Classification of Parkinson's Disease in Home Environments with Resiliency to Missing Modalities

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SENSORS 2021年第12期21卷 4133-4133页

作者： Heidarivincheh, Farnoosh McConville, Ryan Morgan, Catherine McNaney, Roisin Masullo, Alessandro Mirmehdi, Majid Whone, Alan L. Craddock, Ian Univ Bristol Sch Comp Sci Elect & Elect Engn & Engn Maths Bristol BS8 1UB Avon England Univ Bristol Med Sch Translat Hlth Sci Bristol BS8 1UD Avon England North Bristol NHS Trust Bristol Brain Ctr Bristol BS10 5PN Avon England Monash Univ Dept Human Centred Comp Melbourne Vic 3000 Australia

Parkinson's disease (PD) is a chronic neurodegenerative condition that affects a patient's everyday life. Authors have proposed that a machine learning and sensor-based approach that continuously monitors patients in naturalistic settings can provide constant evaluation of PD and objectively analyse its progression. In this paper, we make progress toward such PD evaluation by presenting a multimodal deep learning approach for discriminating between people with PD and without PD. Specifically, our proposed architecture, named MCPD-Net, uses two data modalities, acquired from vision and accelerometer sensors in a home environment to train variational autoencoder (VAE) models. These are modality-specific VAEs that predict effective representations of human movements to be fused and given to a classification module. During our end-to-end training, we minimise the difference between the latent spaces corresponding to the two data modalities. This makes our method capable of dealing with missing modalities during inference. We show that our proposed multimodal method outperforms unimodal and other multimodal approaches by an average increase in F1-score of 0.25 and 0.09, respectively, on a data set with real patients. We also show that our method still outperforms other approaches by an average increase in F1-score of 0.17 when a modality is missing during inference, demonstrating the benefit of training on multiple modalities.

关键词： Parkinson's disease deep learning multimodal data missing modality accelerometer computer vision variational autoencoder

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