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Towards a robust and reliable deep learning approach for detection of compact binary mergers in gravitational wave data

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2023年第4期4卷 045028页

作者： Jadhav, Shreejit Shrivastava, Mihir Mitra, Sanjit Interuniv Ctr Astron & Astrophys IUCAA Post Bag 4 Pune 411007 India Swinburne Univ Technol Ctr Astrophys & Supercomp Hawthorn Vic 3122 Australia ARC Ctr Excellence Gravitat Wave Discovery OzGrav Melbourne Australia Indian Inst Technol IIT Kharagpur India

The ability of deep learning (DL) approaches to learn generalised signal and noise models, coupled with their fast inference on GPUs, holds great promise for enhancing gravitational-wave (GW) searches in terms of speed, parameter space coverage, and search sensitivity. However, the opaque nature of DL models severely harms their reliability. In this work, we meticulously develop a DL model stage-wise and work towards improving its robustness and reliability. First, we address the problems in maintaining the purity of training data by deriving a new metric that better reflects the visual strength of the 'chirp' signal features in the data. Using a reduced, smooth representation obtained through a variational auto-encoder (VAE), we build a classifier to search for compact binary coalescence (CBC) signals. Our tests on real LIGO data show an impressive performance of the model. However, upon probing the robustness of the model through adversarial attacks, its simple failure modes were identified, underlining how such models can still be highly fragile. As a first step towards bringing robustness, we retrain the model in a novel framework involving a generative adversarial network (GAN). Over the course of training, the model learns to eliminate the primary modes of failure identified by the adversaries. Although absolute robustness is practically impossible to achieve, we demonstrate some fundamental improvements earned through such training, like sparseness and reduced degeneracy in the extracted features at different layers inside the model. We show that these gains are achieved at practically zero loss in terms of model performance on real LIGO data before and after GAN training. Through a direct search on similar to 8.8 days of LIGO data, we recover two significant CBC events from GWTC-2.1 (Abbott et al 2022 2108.01045 [gr-qc]), GW190519_153544 and GW190521_074359. We also report the search sensitivity obtained from an injection study.

关键词： gravitational wave deep learning cbc search variational autoencoder generative adversarial network robust and reliable model

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node2hash: Graph aware deep semantic text hashing

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INFORMATION PROCESSING & MANAGEMENT 2020年第6期57卷 102143-102143页

作者： Chaidaroon, Suthee Park, Dae Hoon Chang, Yi Fang, Yi Santa Clara Univ Santa Clara CA 95053 USA Huawei Res Amer Santa Clara CA USA Jilin Univ Changchun Peoples R China

Semantic hashing is an effective method for fast similarity search which maps high-dimensional data to a compact binary code that preserves the semantic information of the original data. Most existing text hashing approaches treat each document separately and only learn the hash codes from the content of the documents. However, in reality, documents are related to each other either explicitly through an observed linkage such as citations or implicitly through unobserved connections such as adjacency in the original space. The document relationships are pervasive in the real world while they are largely ignored in the prior semantic hashing work. In this paper, we propose node2hash, an unsupervised deep generative model for semantic text hashing by utilizing graph context. It is designed to incorporate both document content and connection information through a probabilistic formulation. Based on the deep generative modeling framework, node2hash employs deep neural networks to learn complex mappings from the original space to the hash space. Moreover, the probabilistic formulation enables a principled way to generate hash codes for unseen documents that do not have any connections with the existing documents. Besides, node2hash can go beyond one-hop connections about directed linked documents by considering more global graph information. We conduct comprehensive experiments on seven datasets with explicit and implicit connections. The results have demonstrated the effectiveness of node2hash over competitive baselines.

关键词： Semantic hashing variational autoencoder Deep learning

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An Energy-efficient And Trustworthy Unsupervised Anomaly Detection Framework (EATU) for IIoT

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ACM TRANSACTIONS ON SENSOR NETWORKS 2022年第4期18卷 56-56页

作者： Huang, Zijie Wu, Yulei Tempini, Niccolo Lin, Hui Yin, Hao Univ Exeter Coll EMPS Dept Comp Sci Harrison BldgStreatham CampusN Pk Rd Exeter Devon England Univ Exeter Coll SSIS Dept Sociol Philosophy & Anthropol Amory BldgRennes Dr Exeter EX4 4RJ Devon England Fujian Normal Univ Coll Comp & Cyber Secur 8 Shangsan Rd Fuzhou 350117 Fujian Peoples R China Tsinghua Univ Res Inst Informat Technol Beijing 100084 Peoples R China

Many anomaly detection techniques have been adopted by Industrial Internet of Things (IIoT) for improving self-diagnosing efficiency and infrastructures security. However, they are usually associated with the issues of computational-hungry and "black box." Thus, it becomes important to ensure that the detection is not only accurate but also energy-efficient and trustworthy. In this article, we propose an Energy-efficient And Trustworthy Unsupervised anomaly detection framework (EATU) for IIoT. The framework consists of two levels of feature extraction: (1) autoencoder-based feature extraction and (2) Efficient DeepExplainer-based explainable feature selection. We propose an Efficient DeepExplainer model based on perturbation-focused sampling, which demonstrates the most computational efficiency among state-of-the-art explainable models. With the important features selected by Efficient DeepExplainer, the rationale of why an anomaly detection decision was made is given, enhancing the trustworthiness of the detection as well as improving the accuracy of anomaly detection. Three real-world IIoT datasets with high-dimensional features are used to validate the effectiveness of the proposed framework. Extensive experimental results demonstrate that in comparison with the state-of-the-art, our framework has the attributes of improved accuracy, trustworthiness (in terms of correctness and stability of the explanation), and energy-efficiency (in terms of wall-clock-time and resource usage).

关键词： Energy-efficiency variational autoencoder explainable AI Industrial Internet of Things feature extraction anomaly detection

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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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Highly Productive 3D Printing Process to Transcend Intractability in Materials and Geometries via Interactive Machine-Learning-Based Technique

ADVANCED INTELLIGENT SYSTEMS

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ADVANCED INTELLIGENT SYSTEMS 2023年第7期5卷

作者： Kim, Yuseok Park, Suk Hee Pusan Natl Univ Sch Mech Engn Busan 46241 South Korea

Herein, a highly productive and defect-free 3D-printing system enforced by deep-learning (DL)-based anomaly detection and reinforcement-learning (RL)-based optimization processes is developed. Unpredictable defect factors, such as machine setting errors or unexpected material flow, are analyzed by image-based anomaly detection implemented using a variational autoencoder DL model. Real-time detection and in situ correction of defects are implemented by an autocalibration algorithm in conjunction with the DL system. In view of productivity enhancement, the optimized set of diversified printing speeds can be generated from virtual simulation of RL, which is established using a physics-based engineering model. The RL-simulated parameter set maximizes printing speed and minimizes deflection-related failures throughout the 3D-printing process. With the synergistic assistance of DL and RL techniques, the developed system can overcome the inherent challenging intractability of 3D printing in terms of material property and geometry, achieving high process productivity.

关键词： deep learning defect control reinforcement learning variational autoencoder 3D printing

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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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variational data augmentation for a learning-based granular predictive model of

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 232卷

作者： Zhao, Tianqiao Yue, Meng Jensen, Michael Endo, Satoshi Marschilok, Amy C. Nugent, Brian Cerruti, Brian Spanos, Constantine Brookhaven Natl Lab Upton NY 11973 USA Orange & Rockland Util Inc Pearl River NY 10965 USA Con Edison Co New York NY USA

As the trend in climate change continues, extreme weather events are expected to occur with increasing frequency and severity and pose a significant threat to the electric power infrastructure. Regardless of the efforts a utility puts towards hardening the grid, storm-induced damage to the utility assets such as cables and distributed energy resources (DERs) that are particularly vulnerable to such events is unavoidable. Access to a highly granular, in space and time, outage forecasting tool with long lead times (i.e., days ahead) will enhance the efficiency of service restoration efforts. In this study, we propose to develop and implement a multi -model framework as an operational tool based on a granular and multi -day outage forecasting model using operational numerical weather prediction model forecasts and detailed component outage information. An innovative two-layered recurrent neural network, i.e., a long-short-term-memory (LSTM)-based variational autoencoder (VAE) framework and a sliding window are used to address the uneven distribution of different types of weather events and make better use of the time -series data. Case studies are performed to demonstrate the performance of the new framework.

关键词： variational data augmentation Outage prediction variational autoencoder Recurrent neural networks Weather-related outages

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An automatic classification framework for identifying type of plant leaf diseases using multi-scale feature fusion-based adaptive deep network

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年第PartA期95卷

作者： Nagachandrika, Bathula Prasath, R. Joe, I. R. Praveen KCG Coll Technol Dept Comp Sci & Engn Chennai 600097 Tamil Nadu India Vellore Inst Technol Comp Sci & Engn Tiruvalam Rd Vellore 632014 Tamil Nadu India

This method of identifying plant leaf disease generally involves a large team of experts with extensive knowledge of plant diseases, and it can be expensive, time-consuming, and subjective. Hence, a novel plant leaf disease classification framework is proposed to classify the plant diseases and then take preventive measures based on the classified outcomes. The plant leaf images are collected from traditional databases. The classification of leaf diseases is done with the support of the developed Multi-scale Feature Fusion-based Adaptive Deep Network (MFF-ADNet). In this developed MFF-ADNet, two processes are carried out such as feature extraction and classification. The collected images are given to the feature extraction phase, where the Visual Geometry Group (16) (VGG16), variational autoencoder (VAE), and Visual Transformer (ViT) network are used for extracting the features. The extracted features are fused and the resultant Multi-scale fused features are provided to the input of the classification process. Here, the Adaptive Convolutional Neural Network with Attention Mechanism (CNNAM) is utilized for classifying the plant leaf diseases and the parameters are optimized using the Enhanced Gannet Optimization Algorithm (EGOA) approach. From the results, the median value is obtained for a proposed method that is more than 7.18% of MAO-MFF-ADNet, 4.11% of TSO-MFF-ADNet, 8.03% of CO-MFF-ADNet and 4.07% of GOA-MFF-ADNet. Therefore, the experimental outcome of the developed plant leaf classification model is validated over various approaches to ensure the goodness of the developed scheme.

关键词： Classification of Plant Leaf Diseases Multi-scale Feature Fusion-based Adaptive Deep Network Visual Geometry Group 16 variational autoencoder Visual Transformer Adaptive Convolutional Neural Network with Attention Mechanism Enhanced Gannet Optimization Algorithm

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Online training of deep neural networks for classification

Online training of deep neural networks for classification

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作者： Tumpach, Jiří Charles University of Prague

Deep learning is usually applied to static datasets. If used for classification based on data streams, it is not easy to take into account a non-stationarity. This thesis presents work in progress on a new method for online deep classifi- cation learning in data streams with slow or moderate drift, highly relevant for the application domain of malware detection. The method uses a combination of multilayer perceptron and variational autoencoder to achieve constant mem- ory consumption by encoding past data to a generative model. This can make online learning of neural networks more accessible for independent adaptive sys- tems with limited memory. First results for real-world malware stream data are presented, and they look promising. 1

关键词： neural network variational autoencoder online learning classification

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Energy Theft Detection Model Based on VAE-GAN for Imbalanced Dataset

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ENERGIES 2023年第3期16卷 1109-1109页

作者： Sun, Youngghyu Lee, Jiyoung Kim, Soohyun Seon, Joonho Lee, Seongwoo Kyeong, Chanuk Kim, Jinyoung Kwangwoon Univ Dept Elect Convergence Engn Seoul 01897 South Korea

Energy theft causes a lot of economic losses every year. In the practical environment of energy theft detection, it is required to solve imbalanced data problem where normal user data are significantly larger than energy theft data. In this paper, a variational autoencoder-generative adversarial network (VAE-GAN)-based energy theft-detection model is proposed to overcome the imbalanced data problem. In the proposed model, the VAE-GAN generates synthetic energy theft data with the features of real energy theft data for augmenting the energy theft dataset. The obtained balanced dataset is applied to train a detector which is designed as one-dimensional convolutional neural network. The proposed model is simulated on the practical dataset for comparing with various generative models to evaluate their performance. From simulation results, it is confirmed that the proposed model outperforms the other existing models. Additionally, it is shown that the proposed model is also very useful in the environments of extreme data imbalance for a wide variety of applications by analyzing the performance of detector according to the balance rate.

关键词： energy theft imbalanced dataset data augmentation variational autoencoder generative adversarial network

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