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Φ-DVAE: Physics-informed dynamical variational autoencoders for unstructured data assimilation

JOURNAL OF COMPUTATIONAL PHYSICS 2024年 515卷

作者： Glyn-Davies, Alex Duffin, Connor Akyildiz, O. Deniz Girolami, Mark Univ Cambridge Dept Engn Trumpington St Cambridge CB2 1PZ England Imperial Coll London Dept Math Exhibit Rd London SW7 2AZ England Alan Turing Inst British Lib 96 Euston Rd London NW1 2DB England

Incorporating unstructured data into physical models is a challenging problem that is emerging in data assimilation. Traditional approaches focus on well-defined observation operators whose functional forms are typically assumed to be known. This prevents these methods from achieving a consistent model-data synthesis in configurations where the mapping from data-space to model- space is unknown. To address these shortcomings, in this paper we develop a physics-informed dynamical variational autoencoder (Phi-DVAE) to embed diverse data streams into time-evolving physical systems described by differential equations. Our approach combines a standard, possibly nonlinear, filter for the latent state-space model and a VAE, to assimilate the unstructured data into the latent dynamical system. Unstructured data, in our example systems, comes in the form of video data and velocity field measurements, however the methodology is suitably generic to allow for arbitrary unknown observation operators. A variational Bayesian framework is used for the joint estimation of the encoding, latent states, and unknown system parameters. To demonstrate the method, we provide case studies with the Lorenz-63 ordinary differential equation, and the advection and Korteweg-de Vries partial differential equations. Our results, with synthetic data, show that Phi-DVAE provides a data efficient dynamics encoding methodology which is competitive with standard approaches. Unknown parameters are recovered with uncertainty quantification, and unseen data are accurately predicted.

关键词： Data assimilation Nonlinear filtering variational autoencoder Inverse problem variational Bayes

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Multi-Objective Design of DNA-Stabilized Nanoclusters Using variational autoencoders With Automatic Feature Extraction

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ACS NANO 2024年第39期18卷 26997-27008页

作者： Sadeghi, Elham Mastracco, Peter Gonzalez-Rosell, Anna Copp, Stacy M. Bogdanov, Petko Univ Albany SUNY Dept Comp Sci Albany NY 12222 USA Univ Calif Irvine Dept Mat Sci & Engn Irvine CA 92697 USA Univ Calif Irvine Dept Chem Dept Chem & Biomol Engn Irvine CA 92697 USA Univ Calif Irvine Dept Phys & Astron Irvine CA 92697 USA

DNA-stabilized silver nanoclusters (Ag-N-DNAs) have sequence-tuned compositions and fluorescence colors. High-throughput experiments together with supervised machine learning models have recently enabled design of DNA templates that select for Ag-N-DNA properties, including near-infrared (NIR) emission that holds promise for deep tissue bioimaging. However, these existing models do not enable simultaneous selection of multiple Ag-N-DNA properties, and require significant expert input for feature engineering and class definitions. This work presents a model for multiobjective, continuous-property design of Ag-N-DNAs with automatic feature extraction, based on variational autoencoders (VAEs). This model is generative, i.e., it learns both the forward mapping from DNA sequence to Ag-N-DNA properties and the inverse mapping from properties to sequence, and is trained on an experimental data set of DNA sequences paired with Ag-N-DNA fluorescence properties. Experimental testing shows that the model enables effective design of Ag-N-DNA emission, including bright NIR Ag-N-DNAs with 4-fold greater abundance compared to training data. In addition, Shapley analysis is employed to discern learned nucleobase patterns that correspond to fluorescence color and brightness. This generative model can be adapted for a range of biomolecular systems with sequence-dependent properties, enabling precise design of emerging biomolecular nanomaterials.

关键词： variational autoencoder silver nanocluster DNA interpretable machinelearning fluorescence near-infrared

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Convolutional variational autoencoders and Resampling Techniques with Generative Adversarial Network for Enhancing Internet of Thing Security

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PATTERN RECOGNITION AND IMAGE ANALYSIS 2024年第3期34卷 562-569页

作者： Dong, Huiyao Kotenko, I. V. Russian Acad Sci St Petersburg Fed Res Ctr St Petersburg 199178 Russia

The Internet of Things is a pivotal constituent of the contemporary technological revolution and has experienced expeditious expansion in recent times. The proliferation of Internet of Things devices has led to enhanced convenience and automation. However, the extensive deployment of Internet of Things devices has also engendered concerns regarding data privacy and security. Among various detection and prevention methodologies, deep learning is emerging as a prominent trend. This paper ultilizes convolutional variational autoencoders and resampling techniques for network attacks detection. The proposed methodology employs a hybrid data resampling technique to tackle the issue of imbalanced classes, followed by the implementation of a convolutional variational autoencoder classification model with a weighted loss function. The experiments demonstrate that the light-weighted convolutional variational autoencoder outperforms the baseline models. Therefore, it possesses the capability to effectively detect intrusive activities in real-world settings and strengthen the Internet of Things security.

关键词： Internet of Things cyber-attacks variational autoencoder

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Aspect-level item recommendation based on user reviews with variational autoencoders

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INFORMATION SCIENCES 2024年 671卷

作者： Ou, Wei Huynh, Van-Nam Zhejiang Gongshang Univ Sch Tourism & Urban Rural Planning Hangzhou Peoples R China Japan Adv Inst Sci & Technol Sch Knowledge Sci Nomi Japan

In this paper we propose an aspect-based recommendation model based on variational autoencoders, that provides not only coarse predictions about what items users may like, but also finer-grained predictions of specific aspects users may be interested in of the recommended items. The proposed model first employs convolution operations to learn probability distributions of aspect-level embeddings for users and items from user aspect-level sentiments on seen items. Then it samples from these distributions and feeds the samples into transpose convolution operations to 'reconstruct' missing aspect-level sentiments for unseen items, thereby serving as signals for generating recommendations. To prevent overfitting, we impose a prior on the representation distributions and penalize the model if the learned distributions diverge from the prior. Based on the output of the proposed model, we propose a two-stage ranking scheme that combines aspect-level and overall sentiment signals to rank items. Experiment results show that the proposed model outperforms state -of -the -art aspect-based recommendation models, and the two-stage ranking scheme improves the traditional ranking by overall sentiment predictions.

关键词： Aspect-based recommender systems variational autoencoder Item ranking Aspect-level sentiment prediction

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A Survey on variational autoencoders in Recommender Systems

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ACM COMPUTING SURVEYS 2024年第10期56卷 1页

作者： Liang, Shangsong Pan, Zhou Liu, Wei Yin, Jian De Rijke, Maarten Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Sun Yat Sen Univ Sch Artificial Intelligence Zhuhai Peoples R China Univ Amsterdam Amsterdam Noord Holland Netherlands

Recommender systems have become an important instrument to connect people to information. Sparse, complex, and rapidly growing data presents new challenges to traditional recommendation algorithms. To overcome these challenges, various deep learning-based recommendation algorithms have been proposed. Among these, variational autoencoder (VAE)-based recommendation methods stand out. VAEs are based on a flexible probabilistic framework, which is robust for data sparsity and compatible with other deep learning-based models for dealing with multimodal data. In addition, the deep generative structure of VAEs helps to perform Bayesian inference in an efficient manner. VAE-based recommendation algorithms have given rise to many novel graphical models, and they have achieved promising performance. In this article, we conduct a survey to systematically summarize recent VAE-based recommendation algorithms. Four frequently used characteristics of VAE-based recommendation algorithms are summarized, and a taxonomy of VAE-based recommendation algorithms is proposed. We also identify future research directions for, advanced perspectives on, and the application of VAEs in recommendation algorithms, to inspire future work on VAEs for recommender systems.

关键词： variational autoencoder recommender systems deep learning Bayesian network

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Combining propensity score methods with variational autoencoders for generating synthetic data in presence of latent sub-groups

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BMC MEDICAL RESEARCH METHODOLOGY 2024年第1期24卷 198页

作者： Farhadyar, Kiana Bonofiglio, Federico Hackenberg, Maren Behrens, Max Zoeller, Daniela Binder, Harald Univ Freiburg Inst Med Biometry & Stat Freiburg Germany Univ Freiburg Freiburg Ctr Data Anal & Modeling Freiburg Germany Natl Res Council Italy ISMAR Lerici Italy

In settings requiring synthetic data generation based on a clinical cohort, e.g., due to data protection regulations, heterogeneity across individuals might be a nuisance that we need to control or faithfully preserve. The sources of such heterogeneity might be known, e.g., as indicated by sub-groups labels, or might be unknown and thus reflected only in properties of distributions, such as bimodality or skewness. We investigate how such heterogeneity can be preserved and controlled when obtaining synthetic data from variational autoencoders (VAEs), i.e., a generative deep learning technique that utilizes a low-dimensional latent representation. To faithfully reproduce unknown heterogeneity reflected in marginal distributions, we propose to combine VAEs with pre-transformations. For dealing with known heterogeneity due to sub-groups, we complement VAEs with models for group membership, specifically from propensity score regression. The evaluation is performed with a realistic simulation design that features sub-groups and challenging marginal distributions. The proposed approach faithfully recovers the latter, compared to synthetic data approaches that focus purely on marginal distributions. Propensity scores add complementary information, e.g., when visualized in the latent space, and enable sampling of synthetic data with or without sub-group specific characteristics. We also illustrate the proposed approach with real data from an international stroke trial that exhibits considerable distribution differences between study sites, in addition to bimodality. These results indicate that describing heterogeneity by statistical approaches, such as propensity score regression, might be more generally useful for complementing generative deep learning for obtaining synthetic data that faithfully reflects structure from clinical cohorts.

关键词： Synthetic data Complex distribution Propensity score Deep generative model variational autoencoder

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Scalable variational Quantum Circuits for autoencoder-based Drug Discovery

Scalable Variational Quantum Circuits for Autoencoder-based ...

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25th Design, Automation and Test in Europe Conference and Exhibition (DATE)

作者： Li, Junde Ghosh, Swaroop Penn State Univ Dept Comp Sci & Engn University Pk PA 16802 USA

ISBN: (纸本)9783981926361

The de novo design of drug molecules is recognized as a time-consuming and costly process, and computational approaches have been applied in each stage of the drug discovery pipeline. variational autoencoder is one of the computer-aided design methods which explores the chemical space based on an existing molecular dataset. Quantum machine learning has emerged as an atypical learning method that may speed up some classical learning tasks because of its strong expressive power. However, near-term quantum computers suffer from limited number of qubits which hinders the representation learning in high dimensional spaces. We present a scalable quantum generative autoencoder (SQ-VAE) for simultaneously reconstructing and sampling drug molecules, and a corresponding vanilla variant (SQ-AE) for better reconstruction. The architectural strategies in hybrid quantum classical networks such as, adjustable quantum layer depth, heterogeneous learning rates, and patched quantum circuits are proposed to learn high dimensional dataset such as, ligand-targeted drugs. Extensive experimental results are reported for different dimensions including 8x8 and 32x32 after choosing suitable architectural strategies. The performance of quantum generative autoencoder is compared with the corresponding classical counterpart throughout all experiments. The results show that quantum computing advantages can be achieved for normalized low-dimension molecules, and that high-dimension molecules generated from quantum generative autoencoders have better drug properties within the same learning period.

关键词： Quantum Machine Learning variational autoencoder Drug Discovery

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Conservative Policy Construction Using variational autoencoders for Logged Data With Missing Values

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第9期34卷 6368-6378页

作者： Abroshan, Mahed Yip, Kai Hou Tekin, Cem van der Schaar, Mihaela Alan Turing Inst London NW1 2DB England UCL London WC1E 6BT England Bilkent Univ Dept Elect & Elect Engn TR-06800 Ankara Turkey Univ Cambridge Dept Appl Math & Theoret Phys Cambridge CB2 1TN England Univ Calif Los Angeles Los Angeles CA 90095 USA

In high-stakes applications of data-driven decision-making such as healthcare, it is of paramount importance to learn a policy that maximizes the reward while avoiding potentially dangerous actions when there is uncertainty. There are two main challenges usually associated with this problem. First, learning through online exploration is not possible due to the critical nature of such applications. Therefore, we need to resort to observational datasets with no counterfactuals. Second, such datasets are usually imperfect, additionally cursed with missing values in the attributes of features. In this article, we consider the problem of constructing personalized policies using logged data when there are missing values in the attributes of features in both training and test data. The goal is to recommend an action (treatment) when (X) over tilde, a degraded version of X with missing values, is observed. We consider three strategies for dealing with missingness. In particular, we introduce the conservative strategy where the policy is designed to safely handle the uncertainty due to missingness. In order to implement this strategy, we need to estimate posterior distribution p(X vertical bar(X) over tilde) and use a variational autoencoder to achieve this. In particular, our method is based on partial variational autoencoders (PVAEs) that are designed to capture the underlying structure of features with missing values.

关键词： Missing values observational data policy construction variational autoencoder

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An entangled mixture of variational autoencoders approach to deep clustering

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NEUROCOMPUTING 2023年第1期529卷 182-189页

作者： Caciularu, Avi Goldberger, Jacob Bar Ilan Univ Comp Sci Dept Ramat Gan Israel Bar Ilan Univ Engn Fac Ramat Gan Israel

We present a novel deep clustering algorithm that utilizes a variational autoencoder (VAE) framework with an entangled multi encoder-decoder neural architecture. Our model enforces a complementary structure that guides the learned latent representations towards a better space arrangement. It differs from previous VAE-based clustering algorithms by employing a new generative model that uses multiple encoder-decoders that are entangled to provide a joint clustering decision. The optimal clustering is found by optimizing a lower bound of the model likelihood function. Both the reconstruction component and the regularization component of the ELBO objective function are explicitly involved in the clustering procedure. We show that this modeling results in both better clustering capabilities and improved data generation. The proposed method is evaluated on standard datasets and is shown to significantly outper-form state-of-the-art deep clustering methods.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Deep clustering variational autoencoder Mixture model

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Joint variational autoencoders for multimodal imputation and embedding

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NATURE MACHINE INTELLIGENCE 2023年第6期5卷 631-+页

作者： Kalafut, Noah Cohen Huang, Xiang Wang, Daifeng Univ Wisconsin Madison Dept Comp Sci Madison WI 53706 USA Univ Wisconsin Madison Waisman Ctr Madison WI 53706 USA Univ Wisconsin Madison Dept Biostat & Med Informat Madison WI 53706 USA

While single-cell multimodal datasets allow for the measurement of individual cells to understand cellular and molecular mechanisms, generating multimodal data for many cells is costly and challenging. Cohen Kalafut and colleagues develop a machine learning model capable of imputing single-cell modalities and prioritizing multimodal features, such as gene expression, chromatin accessibility and electrophysiology. Single-cell multimodal datasets have measured various characteristics of individual cells, enabling a deep understanding of cellular and molecular mechanisms. However, multimodal data generation remains costly and challenging, and missing modalities happen frequently. Recently, machine learning approaches have been developed for data imputation but typically require fully matched multimodalities to learn common latent embeddings that potentially lack modality specificity. To address these issues, we developed an open-source machine learning model, Joint variational autoencoders for multimodal Imputation and Embedding (JAMIE). JAMIE takes single-cell multimodal data that can have partially matched samples across modalities. variational autoencoders learn the latent embeddings of each modality. Then, embeddings from matched samples across modalities are aggregated to identify joint cross-modal latent embeddings before reconstruction. To perform cross-modal imputation, the latent embeddings of one modality can be used with the decoder of the other modality. For interpretability, Shapley values are used to prioritize input features for cross-modal imputation and known sample labels. We applied JAMIE to both simulation data and emerging single-cell multimodal data including gene expression, chromatin accessibility, and electrophysiology in human and mouse brains. JAMIE significantly outperforms existing state-of-the-art methods in general and prioritized multimodal features for imputation, providing potentially novel mechanistic insights at cellular resolution.

关键词： Data imputation Data integration Deep learning Multimodal Single-cell variational autoencoder

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