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Developing a Generative Model Utilizing Self-attention Networks: Application to Materials/Drug Discovery

MOLECULAR INFORMATICS 2021年第10期40卷 e2100102-e2100102页

作者： Kondo, Masakazu JNC Petrochem Corp Ichihara Res Labs 5-1 Goi Kaigan Ichihara Chiba 2908551 Japan

A new generative model, in which the variational autoencoder network is combined with the Transformer architecture, is developed. The proposed model, the variational Autoencoding Transformer (VAT), is applied to the task of generating molecules, showing that, with proper training, the VAT model can not only produce similar molecules to input ones with high accuracy but also generate new molecules from a predefined prior almost perfectly. A desirable aspect of our VAT is that no heuristic setting is necessary for optimal performance, which suggests that the model can readily be available to a variety of datasets. As practical directions toward materials/drug discovery, two strategies: a fine-tuning method for directed molecular generation and a method of mixing molecules in the latent space, are demonstrated.

关键词： Generative model variational autoencoder Transformer

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A survey of machine learning techniques in structural and multidisciplinary optimization

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2022年第9期65卷 266-266页

作者： Ramu, Palaniappan Thananjayan, Pugazhenthi Acar, Erdem Bayrak, Gamze Park, Jeong Woo Lee, Ikjin Indian Inst Technol Madras Dept Engn Design Adv Design Optimizat & Probabilist Tech Lab Chennai 600036 Tamil Nadu India TOBB Univ Econ & Technol Dept Mech Engn TR-06560 Ankara Turkey Korea Adv Inst Sci & Technol Mech Engn Dept 291 Daehak Ro Daejeon 34141 South Korea

Machine Learning (ML) techniques have been used in an extensive range of applications in the field of structural and multidisciplinary optimization over the last few years. This paper presents a survey of this wide but disjointed literature on ML techniques in the structural and multidisciplinary optimization field. First, we discuss the challenges associated with conventional optimization and how Machine learning can address them. Then, we review the literature in the context of how ML can accelerate design synthesis and optimization. Some real-life engineering applications in structural design, material design, fluid mechanics, aerodynamics, heat transfer, and multidisciplinary design are summarized, and a brief list of widely used open-source codes as well as commercial packages are provided. Finally, the survey culminates with some concluding remarks and future research suggestions. For the sake of completeness, categories of ML problems, algorithms, and paradigms are presented in the Appendix.

关键词： Classification Clustering Deep learning Design diversity Dimension reduction Generative design Machine learning Neural network Optimization Reinforcement learning Regression Supervised unsupervised learning Uncertainty variational autoencoder

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Cyberattack Detection Using Deep Generative Models with variational Inference

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2019年第2期145卷 4018093.1-4018093.10页

作者： Chandy, Sarin E. Rasekh, Amin Barker, Zachary A. Shafiee, M. Ehsan Xylem Inc Adv Infrastruct Analyt 817 West Peachtree St Atlanta GA 30308 USA Xylem Inc Adv Infrastruct Analyt 639 Davis Dr Morrisville NC 27560 USA

Recent years have witnessed a rise in the frequency and intensity of cyberattacks targeted at critical infrastructure systems. This study designs a versatile, data-driven cyberattack detection platform for infrastructure systems cybersecurity, with a special demonstration in the water sector. A deep generative model with variational inference autonomously learns normal system behavior and detects attacks as they occur. The model can process the natural data in its raw form and automatically discover and learn its representations, hence augmenting system knowledge discovery and reducing the need for laborious human engineering and domain expertise. The proposed model is applied to a simulated cyberattack detection problem involving a drinking water distribution system subject to programmable logic controller hacks, malicious actuator activation, and deception attacks. The model is only provided with observations of the system, such as pump pressure and tank water level reads, and is blind to the internal structures and workings of the water distribution system. The simulated attacks are manifested in the model's generated reproduction probability plot, indicating its ability to discern the attacks. There is, however, need for improvements in reducing false alarms, especially by optimizing detection thresholds. Altogether, the results indicate ability of the model in distinguishing attacks and their repercussions from normal system operation in water distribution systems, and the promise it holds for cyberattack detection in other domains.

关键词： Cybersecurity Water distribution system variational autoencoder Deep learning Anomaly detection

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Improving calibration of forensic glass comparisons by considering uncertainty in feature-based elemental data

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2021年 217卷 104399-104399页

作者： Ramos, Daniel Maronas, Juan Almirall, Jose Univ Autonoma Madrid Escuela Politecn Super AUDIAS Lab Aud Data Intelligence & Speech Calle Francisco Tomas & Valiente 11 Madrid 28049 Spain Univ Politecn Valencia Escuela Tecn Super Ingn Informat Pattern Recognit & Human Language Tecnol PRHLT Re Camino Vera S-N Valencia 46022 Spain Florida Int Univ Dept Chem & Biochem Ctr Adv Res Forens Sci Miami FL 33199 USA

The computation of likelihood ratios (LR) to measure the weight of forensic glass evidence with LA-ICP-MS data directly in the feature space without computing any kind of score as an intermediate step is a complex problem. A probabilistic two-level modeling of the within-source and between-source variability of the glass samples is needed in order to compare the elemental profiles measured from glass recovered from a suspect or a crime scene and compared to glass samples of a known source of origin. Calibration of the likelihood ratios generated using previously reported models is essential to the realistic reporting of the value of the glass evidence comparisons. We propose models that outperform previously proposed feature-based LR models, in particular by improving the calibration of the computed LRs. We assume that the within-source variability is heavy-tailed, in order to incorporate uncertainty when the available data is scarce, as it typically happens in forensic glass comparison. Moreover, we address the complexity of the between-source variability by the use of probabilistic machine learning algorithms, namely a variational autoencoder and a warped Gaussian mixture. Our results show that the overall performance of the likelihood ratios generated by our model is superior to classical approaches, and that this improvement is due to a dramatic improvement in the calibration despite some loss in discriminating power. Moreover, the robustness of the calibration of our proposal is remarkable.

关键词： Likelihood ratio Forensic glass comparison LA-ICP-MS variational autoencoder Warped Gaussian mixture Heavy-tailed

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Challenges of Using Synthetic Data Generation Methods for Tabular Microdata

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APPLIED SCIENCES-BASEL 2024年第14期14卷 5975页

作者： Miletic, Marko Sariyar, Murat Bern Univ Appl Sci Sch Engn & Comp Sci Quellgasse 21 CH-2502 Biel Switzerland

Featured Application This study's findings hold significant implications for enhancing data privacy and utility in healthcare analytics. By evaluating synthetic data generation methods like CTGAN, TVAE, CopulaGAN and Copula across diverse medical datasets containing sensitive patient information, such as genetic profiles and medical histories, the research aims to improve the development of predictive models without compromising patient *** The generation of synthetic data holds significant promise for augmenting limited datasets while avoiding privacy issues, facilitating research, and enhancing machine learning models' robustness. Generative Adversarial Networks (GANs) stand out as promising tools, employing two neural networks-generator and discriminator-to produce synthetic data that mirrors real data distributions. This study evaluates GAN variants (CTGAN, CopulaGAN), a variational autoencoder, and copulas on diverse real datasets of different complexity encompassing numerical and categorical attributes. The results highlight CTGAN's sensitivity to training parameters and TVAE's robustness across datasets. Scalability challenges persist, with GANs demanding substantial computational resources. TVAE stands out for its high utility across all datasets, even for high-dimensional data, though it incurs higher privacy risks, which is indicative of the curse of dimensionality. While no single model universally excels, understanding the trade-offs and leveraging model strengths can significantly enhance synthetic data generation (SDG). Future research should focus on adaptive learning mechanisms, scalability enhancements, and standardized evaluation metrics to advance SDG methods effectively. Addressing these challenges will foster broader adoption and application of synthetic data.

关键词： synthetic data variational autoencoder copula generative adversarial networks GAN

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BmmW: A DNN-based joint BLE and mmWave radar system for accurate 3D localization with-oriented communication

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PERVASIVE AND MOBILE COMPUTING 2024年 102卷

作者： Li, Peizheng Singh, Jagdeep Cui, Han Boano, Carlo Alberto Toshiba Europe Ltd Bristol Res & Innovat Lab Staines Upon Thames England Univ Bristol Bristol England Graz Univ Technol Inst Tech Informat Graz Austria

Bluetooth Low Energy (BLE) has emerged as one of the reference technologies for the development of indoor localization systems, due to its increasing ubiquity, low-cost hardware, and to the introduction of direction -finding enhancements improving its ranging performance. However, the intrinsic narrowband nature of BLE makes this technology susceptible to multipath and channel interference. As a result, it is still challenging to achieve decimetre -level localization accuracy, which is necessary when developing location -based services for smart factories and workspaces. To address this challenge, we present BmmW, an indoor localization system that augments the ranging estimates obtained with BLE 5.1's constant tone extension feature with mmWave radar measurements to provide 3D localization of a mobile tag with decimetre -level accuracy. Specifically, BmmW embeds a deep neural network (DNN) that is jointly trained with both BLE and mmWave measurements, practically leveraging the strengths of both technologies. In fact, mmWave radars can locate objects and people with decimetre -level accuracy, but their effectiveness in monitoring stationary targets and multiple objects is limited, and they also suffer from a fast signal attenuation limiting the usable range to a few meters. We evaluate BmmW's performance experimentally, and show that its joint DNN training scheme allows to track mobile tags with a mean 3D localization accuracy of 10 cm when combining angle -of -arrival BLE measurements with mmWave radar data. We further assess two variations of BmmW: BmmW- Lira and BmmW- Lira+ , both tailored for single -antenna BLE devices, which eliminates the necessity for bulky and expensive multi -antenna arrays and represents a cost-effective solution that is easy to integrate into compact IoT devices. In contrast to classic BmmW (which utilizes angle -of -arrival info), BmmW- Lira uses raw in-phase/quadrature (I/Q) measurements, and achieves a mean localization accuracy of 3

关键词： Indoor localization Data fusion Deep neural networks Bluetooth direction-finding mmWave-radar sensing Goal-oriented communications variational autoencoder

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Virtual disease landscape using mechanics-informed machine learning: Application to esophageal disorders

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2022年 134卷 102435-102435页

作者： Halder, Sourav Yamasaki, Jun Acharya, Shashank Kou, Wenjun Elisha, Guy Carlson, Dustin A. Kahrilas, Peter J. Pandolfino, John E. Patankar, Neelesh A. Northwestern Univ Mormick Sch Engn Theoret & Appl Mech Program Evanston IL USA Northwestern Univ McCormick Sch Engn Dept Mech Engn Evanston IL USA Northwestern Univ Feinberg Sch Med Dept Med Div Gastroenterol & Hepatol Chicago IL USA Northwestern Univ Dept Mech Engn 2145 Sheridan RdRoom Tech L490 Evanston IL 60208 USA

Esophageal disorders are related to the mechanical properties and function of the esophageal wall. Therefore, to understand the underlying fundamental mechanisms behind various esophageal disorders, it is crucial to map mechanical behavior of the esophageal wall in terms of mechanics-based parameters corresponding to altered bolus transit and increased intrabolus pressure. We present a hybrid framework that combines fluid mechanics and machine learning to identify the underlying physics of various esophageal disorders (motility disorders, eosinophilic esophagitis, reflux disease, scleroderma esophagus) and maps them onto a parameter space which we call the virtual disease landscape (VDL). A one-dimensional inverse model processes the output from an esophageal diagnostic device called the functional lumen imaging probe (FLIP) to estimate the mechanical "health" of the esophagus by predicting a set of mechanics-based parameters such as esophageal wall stiffness, muscle contraction pattern and active relaxation of esophageal wall. The mechanics-based parameters were then used to train a neural network that consists of a variational autoencoder that generated a latent space and a side network that predicted mechanical work metrics for estimating esophagogastric junction motility. The latent vectors along with a set of discrete mechanics-based parameters define the VDL and formed clusters corre-sponding to specific esophageal disorders. The VDL not only distinguishes among disorders but also displayed disease progression over time. Finally, we demonstrated the clinical applicability of this framework for esti-mating the effectiveness of a treatment and tracking patients' condition after a treatment.

关键词： Achalasia FLIP Convolutional neural network Computational fluid dynamics Dysphagia variational autoencoder

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Anomaly detection in aeronautics data with quantum-compatible discrete deep generative model

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2023年第3期4卷 035018页

作者： Templin, Thomas Memarzadeh, Milad Vinci, Walter Lott, P. Aaron Asanjan, Ata Akbari Armenakas, Anthony Alexiades Rieffel, Eleanor NASA Data Sci Grp Ames Res Ctr Moffett Field CA 94035 USA NASA Univ Space Res Assoc Data Sci Grp Ames Res Ctr Moffett Field CA 94035 USA HP SCDS Leon 24009 Spain NASA Univ Space Res Assoc Quantum Artificial Intelligence Lab Ames Res Ctr Moffett Field CA 94035 USA Harvard Univ Dept Phys Cambridge MA 02138 USA NASA Ames Res Ctr Quantum Artificial Intelligence Lab Moffett Field CA 94035 USA

Deep generative learning cannot only be used for generating new data with statistical characteristics derived from input data but also for anomaly detection, by separating nominal and anomalous instances based on their reconstruction quality. In this paper, we explore the performance of three unsupervised deep generative models-variational autoencoders (VAEs) with Gaussian, Bernoulli, and Boltzmann priors-in detecting anomalies in multivariate time series of commercial-flight operations. We created two VAE models with discrete latent variables (DVAEs), one with a factorized Bernoulli prior and one with a restricted Boltzmann machine (RBM) with novel positive-phase architecture as prior, because of the demand for discrete-variable models in machine-learning applications and because the integration of quantum devices based on two-level quantum systems requires such models. To the best of our knowledge, our work is the first that applies DVAE models to anomaly-detection tasks in the aerospace field. The DVAE with RBM prior, using a relatively simple-and classically or quantum-mechanically enhanceable-sampling technique for the evolution of the RBM's negative phase, performed better in detecting anomalies than the Bernoulli DVAE and on par with the Gaussian model, which has a continuous latent space. The transfer of a model to an unseen dataset with the same anomaly but without re-tuning of hyperparameters or re-training noticeably impaired anomaly-detection performance, but performance could be improved by post-training on the new dataset. The RBM model was robust to change of anomaly type and phase of flight during which the anomaly occurred. Our studies demonstrate the competitiveness of a discrete deep generative model with its Gaussian counterpart on anomaly-detection problems. Moreover, the DVAE model with RBM prior can be easily integrated with quantum sampling by outsourcing its generative process to measurements of quantum states obtained from a quantum anneale

关键词： generative modeling deep learning variational autoencoder anomaly detection restricted Boltzmann machine Gibbs sampling quantum-assisted machine learning

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VAE-Based Latent Representations Learning for Botnet Detection in IoT Networks

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JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 2023年第1期31卷 4-4页

作者： Snoussi, Ramzi Youssef, Habib Univ Sousse Prince Lab ISITCOM Hammam Sousse Sousse Tunisia

Botnets pose significant threats to cybersecurity. The infected Internet of Things (IoT) devices are used to launch unsupported malicious activities on target entities to disrupt their operations and services. To address this danger, we propose a machine learning-based method, for detecting botnets by analyzing network traffic data flow including various types of botnet attacks. Our method uses a hybrid model where a variational autoencoder (VAE) is trained in an unsupervised manner to learn latent representations that describe the benign traffic data, and one-class classifier (OCC) for detecting anomaly (also called novelty detection). The main aim of this research is to learn the discriminating representations of the normal data in low dimensional latent space generated by VAE, and thus improve the predictive power of the OCC to detect malicious traffic. We have evaluated the performance of our model, and compared it against baseline models using a real network based dataset, containing popular IoT devices, and presenting a wide variety of attacks from two recent botnet families Mirai and Bashlite. Tests showed that our model can detect botnets with a satisfactory performance.

关键词： Anomaly detection Botnet detection variational autoencoder One-class classification Latent representations learning

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GenRadar: Self-Supervised Probabilistic Camera Synthesis Based on Radar Frequencies

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IEEE ACCESS 2021年 9卷 148994-149042页

作者： Ditzel, Carsten Dietmayer, Klaus Inst Measurement Control & Microtechnol D-89081 Ulm Germany

Autonomous systems require a continuous and dependable environment perception for navigation and decision-making, which is best achieved by combining different sensor types. Radar continues to function robustly in compromised circumstances in which cameras become impaired, guaranteeing a steady inflow of information. Yet, camera images provide a more intuitive and readily applicable impression of the world. This work combines the complementary strengths of both sensor types in a unique self-learning fusion approach for a probabilistic scene reconstruction in adverse surrounding conditions. After reducing the memory requirements of both high-dimensional measurements through a decoupled stochastic self-supervised compression technique, the proposed algorithm exploits similarities and establishes correspondences between both domains at different feature levels during training. Then, at inference time, relying exclusively on radio frequencies, the model successively predicts camera constituents in an autoregressive and self-contained process. These discrete tokens are finally transformed back into an instructive view of the respective surrounding, allowing to visually perceive potential dangers for important tasks downstream.

关键词： Radar Cameras Radar imaging Robot sensing systems Probabilistic logic Task analysis Signal processing algorithms Radar signal processing computer vision sensor fusion deep learning machine learning variational autoencoder self-supervised learning annotation-free learning scene understanding

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