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Can Perturbations Help Reduce Investment Risks? Risk-aware Stock Recommendation via Split variational Adversarial Training

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ACM TRANSACTIONS ON INFORMATION SYSTEMS 2024年第4期42卷 1-28页

作者： Cheng, Jiezhu Huang, Kaizhu Zheng, Zibin Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Higher Educ Mega Ctr 132 East Outer Ring Rd Guangzhou 510006 Guangdong Peoples R China Duke Kunshan Univ Data Sci Res Ctr 8 Duke Ave Suzhou 215316 Jiangsu Peoples R China Sun Yat Sen Univ Sch Software Engn Zhuhai 519082 Guangdong Peoples R China

In the stock market, a successful investment requires a good balance between profits and risks. Based on the learning to rank paradigm, stock recommendation has been widely studied in quantitative finance to recommend stocks with higher return ratios for investors. Despite the efforts to make profits, many existing recommendation approaches still have some limitations in risk control, which may lead to intolerable paper losses in practical stock investing. To effectively reduce risks, we draw inspiration from adversarial learning and propose a novel Split variational Adversarial Training (SVAT) method for risk-aware stock recommendation. Essentially, SVAT encourages the stock model to be sensitive to adversarial perturbations of risky stock examples and enhances the model's risk awareness by learning from perturbations. To generate representative adversarial examples as risk indicators, we devise a variational perturbation generator to model diverse risk factors. Particularly, the variational architecture enables our method to provide a rough risk quantification for investors, showing an additional advantage of interpretability. Experiments on several real-world stock market datasets demonstrate the superiority of our SVAT method. By lowering the volatility of the stock-recommendation model, SVAT effectively reduces investment risks and outperforms state-of-the-art baselines by more than 30% in terms of risk-adjusted profits. All the experimental data and source code are available at https://***/drive/folders/14AdM7WENEvIp5x5bV3zV_i4Aev21C9g6?usp=sharing

关键词： Stock recommendation risk control variational autoencoder

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QARV: Quantization-Aware ResNet VAE for Lossy Image Compression

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第1期46卷 436-450页

作者： Duan, Zhihao Lu, Ming Ma, Jack Huang, Yuning Ma, Zhan Zhu, Fengqing Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN 47907 USA Nanjing Univ Sch Elect Sci & Engn Nanjing 210093 Jiangsu Peoples R China

This paper addresses the problem of lossy image compression, a fundamental problem in image processing and information theory that is involved in many real-world applications. We start by reviewing the framework of variational autoencoders (VAEs), a powerful class of generative probabilistic models that has a deep connection to lossy compression. Based on VAEs, we develop a new scheme for lossy image compression, which we name quantization-aware ResNet VAE (QARV). Our method incorporates a hierarchical VAE architecture integrated with test-time quantization and quantization-aware training, without which efficient entropy coding would not be possible. In addition, we design the neural network architecture of QARV specifically for fast decoding and propose an adaptive normalization operation for variable-rate compression. Extensive experiments are conducted, and results show that QARV achieves variable-rate compression, high-speed decoding, and better rate-distortion performance than existing baseline methods.

关键词： Lossy image compression learned image compression variational autoencoder deep learning

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A Novel Calibration Scheme of Gas Sensor Array for a More Accurate Measurement Model of Mixed Gases

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ACS SENSORS 2024年第11期9卷 6022-6031页

作者： Ma, Yilun Qiu, Xingchang Duan, Zaihua Liu, Lili Li, Juan Wu, Yuanming Yuan, Zhen Jiang, Yadong Tai, Huiling Univ Elect Sci & Technol China UESTC Sch Optoelect Sci & Engn State Key Lab Elect Thin Films & Integrated Device Chengdu 610054 Peoples R China

Gas sensor arrays (GSAs) usually encounter challenges due to the cross-contamination of mixed gases, leading to reduced accuracy in measuring gas mixtures. However, with the advent of artificial intelligence, there is a promising avenue for addressing this issue effectively. In pursuit of more accurate mixed gas measurements, we proposed a measurement model leveraging neural networks. Our approach involved employing the encoder of an autoencoder network (AEN) to extract features from experimental data, while fully connected layers were utilized for predicting concentrations of mixed gases. To refine the neural network parameters, we employed a variational autoencoder to generate additional data resembling the distribution of experimental data. Subsequently, we designed a domain difference maximum entropy technique to identify optimal concentration points for the calibration data. These calibration points were instrumental in training the fully connected layers, enhancing the model's accuracy. During practical usage, with the AEN configuration fixed, the model can be fine-tuned by using a small subset of test points across large-scale GSA deployments. Simulation and practical measurement results demonstrated the efficacy of our proposed measurement model, boasting high accuracy, with confidence intervals for relative errors of the four gas measurements below 3% at the 95% confidence level. Besides, the calibration scheme reduced the number of test points compared with traditional methods, reducing the cost of labor and equipment.

关键词： gas sensor array gas concentrationcalibration variational autoencoder maximum entropy cross-contamination transfer learning

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Deep Learning for Size-Agnostic Inverse Design of Random-Network 3D Printed Mechanical Metamaterials

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ADVANCED MATERIALS 2024年第6期36卷 2303481-2303481页

作者： Pahlavani, Helda Tsifoutis-Kazolis, Kostas Saldivar, Mauricio C. Mody, Prerak Zhou, Jie Mirzaali, Mohammad J. Zadpoor, Amir A. Delft Univ Technol TU Delft Fac Mech Maritime & Mat Engn Dept Biomech Engn Mekelweg 2 NL-2628 CD Delft Netherlands Leiden Univ Med Ctr LUMC Div Image Proc LKEB Radiol Albinusdreef 2 NL-2333 ZA Leiden Netherlands

Practical applications of mechanical metamaterials often involve solving inverse problems aimed at finding microarchitectures that give rise to certain properties. The limited resolution of additive manufacturing techniques often requires solving such inverse problems for specific specimen sizes. Moreover, the candidate microarchitectures should be resistant to fatigue and fracture. Such a multi-objective inverse design problem is formidably difficult to solve but its solution is the key to real-world applications of mechanical metamaterials. Here, a modular approach titled "Deep-DRAM" that combines four decoupled models is proposed, including two deep learning (DL) models, a deep generative model based on conditional variational autoencoders, and direct finite element (FE) simulations. Deep-DRAM integrates these models into a framework capable of finding many solutions to the posed multi-objective inverse design problem based on random-network unit cells. Using an extensive set of simulations as well as experiments performed on 3D printed specimens, it is demonstrate that: 1) the predictions of the DL models are in agreement with FE simulations and experimental observations, 2) an enlarged envelope of achievable elastic properties (e.g., rare combinations of double auxeticity and high stiffness) is realized using the proposed approach, and 3) Deep-DRAM can provide many solutions to the considered multi-objective inverse design problem. Mechanical metamaterials are architected engineered materials whose unprecedented properties originate from their designs at the microscale. Here, deep-learning techniques are utilized to develop a modular approach titled "Deep-DRAM" that generates numerous microstructures for mechanical metamaterials with predefined elastic properties and dimensions for various applications in high-tech industries, such as soft robotics and biomedical ***

关键词： additive manufacturing deep learning numerical simulations random-network mechanical metamaterials size-agnostic variational autoencoder

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Bayesian Inference for Non-Linear Forward Model by Using a VAE-Based Neural Network Structure

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 1400-1411页

作者： Zhang, Yechuan Zheng, Jian-Qing Chappell, Michael Univ Oxford Inst Biomed Engn Dept Engn Sci Oxford OX1 3PJ England Univ Oxford Kennedy Inst Rheumatol Nuffield Dept Orthopaed Rheumatol & Musculoskeleta Oxford OX3 7FY England Univ Nottingham Sir Peter Mansfield Imaging Ctr Sch Med Nottingham NG7 2RD England Univ Nottingham Sch Med Mental Hlth & Clin Neurosci Nottingham NG7 2RD England Univ Oxford Wellcome Ctr Integrat Neuroimaging Nuffield Dept Clin Neurosci FMRIB Oxford OX3 9DU England

In this paper, a variational autoencoder (VAE) based framework is introduced to solve parameter estimation problems for non-linear forward models. In particular, we focus on applications in the field of medical imaging where many thousands of model-based inference analyses might be required to populate a single parametric map. We adopt the concept from variational Bayes (VB) of using an approximate representation of the posterior, and the concept from the VAE of using the latent space representation to encode the parameters of a forward model. Our work develops the idea of mapping between time-series data and latent parameters using a neural network in variational way. A loss function that differs from the classic VAE formulation and a new sampling strategy are proposed to enable uncertainty estimation as part of the forward model inference. The VAE-based structure is evaluated using simulation experiments on a simple example and two perfusion MRI forward models. Compared with analytical VB (aVB) and Markov Chain Monte Carlo (MCMC), our VAE-based model achieves comparable accuracy, and hundredfold improvement in computational time (100ms/image). We believe this VAE-like framework can be generalized to imaging modularities with higher complexity and thus benefit clinical adoption where otherwise long processing time associated with conventional inference methods is prohibitive.

关键词： Neural networks Computational modeling Bayes methods Data models Estimation Biomedical imaging Optimization variational autoencoder variational Bayes parameter estimation problems medical imaging

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Auto-evaluation of skull radiograph accuracy using unsupervised anomaly detection

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JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024年第4期32卷 1151-1162页

作者： Watanabe, Haruyuki Ezawa, Yuina Matsuyama, Eri Kondo, Yohan Hayashi, Norio Maruyama, Sho Ogura, Toshihiro Shimosegawa, Masayuki Gunma Prefectural Coll Hlth Sci Sch Radiol Technol 323-1 Kamioki Machi Maebashi Gunma Japan Univ Fukuchiyama Fac Informat Fukuchiyama Japan Niigata Univ Grad Sch Hlth Sci Niigata Japan

BACKGROUND: Radiography plays an important role in medical care, and accurate positioning is essential for providing optimal quality images. Radiographs with insufficient diagnostic value are rejected, and retakes are required. However, determining the suitability of retaking radiographs is a qualitative evaluation. OBJECTIVE: To evaluate skull radiograph accuracy automatically using an unsupervised learning-based autoencoder (AE) and a variational autoencoder (VAE). In this study, we eliminated visual qualitative evaluation and used unsupervised learning to identify skull radiography retakes from the quantitative evaluation. METHODS: Five skull phantoms were imaged on radiographs, and 1,680 images were acquired. These images correspond to two categories: normal images captured at appropriate positions and images captured at inappropriate positions. This study verified the discriminatory ability of skull radiographs using anomaly detection methods. RESULTS: The areas under the curves for AE and VAE were 0.7060 and 0.6707, respectively, in receiver operating characteristic analysis. Our proposed method showed a higher discrimination ability than those of previous studies which had an accuracy of 52%. CONCLUSIONS: Our findings suggest that the proposed method has high classification accuracy in determining the suitability of retaking skull radiographs. Automation of optimal image consideration, whether or not to retake radiographs, contributes to improving operational efficiency in busy X-ray imaging operations.

关键词： Anomaly detection unsupervised learning autoencoder variational autoencoder skull radiograph

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Generating practical adversarial examples against learning-based network intrusion detection systems

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ANNALS OF TELECOMMUNICATIONS 2025年第3-4期80卷 209-226页

作者： Kumar, Vivek Kumar, Kamal Singh, Maheep Natl Inst Technol Dept Comp Sci & Engn Srinagar 246174 Uttarakhand India THDC Inst Hydropower Engn & Technol Dept Comp Sci & Engn Tehri 249124 Uttarakhand India IGDTUW Dept Informat Technol Delhi 110006 India

There has been a significant development in the design of intrusion detection systems (IDS) by using deep learning (DL)/machine learning (ML) methods for detecting threats in a computer network. Unfortunately, these DL/ML-based IDS are vulnerable to adversarial examples, wherein a malicious data sample can be slightly perturbed to cause a misclassification by an IDS while retaining its malicious properties. Unlike image recognition domain, the network domain has certain constraints known as domain constraints which are multifarious interrelationships and dependencies between features. To be considered as practical and realizable, an adversary must ensure that the adversarial examples comply with domain constraints. Recently, generative models like GANs and VAEs have been extensively used for generating adversarial examples against IDS. However, majority of these techniques generate adversarial examples which do not satisfy all domain constraints. Also, current generative methods lack explicit restrictions on the amount of perturbation which a malicious data sample undergoes during the crafting of adversarial examples, leading to the potential generation of invalid data samples. To address these limitations, a solution is presented in this work which utilize a variational autoencoder to generate adversarial examples that not only result in misclassification by an IDS, but also satisfy domain constraints. Instead of perturbing the data samples itself, the adversarial examples are crafted by perturbing the latent space representation of the data sample. It allows the generation of adversarial examples under limited perturbation. This research has explored the novel applications of generative networks for generating constraint satisfying adversarial examples. The experimental results support the claims with an attack success rate of 64.8%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy}

关键词： Adversarial example Domain constraints Deep learning Machine learning variational autoencoder

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Low-carbon optimization scheduling of IES based on enhanced diffusion model for scenario deep generation

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2025年 167卷

作者： Wang, Danhao Peng, Daogang Huang, Dongmei Zhao, Huirong Qu, Bogang Shanghai Univ Elect Power Coll Automation Engn Shanghai Peoples R China

With the widespread integration of renewable energy sources into IES (Integrated Energy Systems), the uncertainties in renewable energy outputs and the volatility of user loads present new challenges for energy scheduling optimization. To effectively address these uncertainties, this paper proposes a scheduling method for IES based on an enhanced DM (Diffusion Models). Firstly, a mathematical model of a regional IES encompassing energy supply devices, energy coupling devices, and energy storage devices is constructed. Secondly, an enhanced DM optimized with a VAE (variational autoencoder) is developed. By efficiently generating data in the latent space, this model enhances the representation capability of uncertainty factors, enabling precise modeling of fluctuations in wind and solar power outputs as well as heating, cooling, and electrical loads. Finally, leveraging the improved DM, a large number of high-quality error scenarios are generated, and scheduling optimization experiments are designed with the objectives of minimizing economic costs and carbon emissions. Experimental results demonstrate that the proposed method not only achieves more efficient energy distribution and lower carbon emissions but also maintains the continuous and stable operation of the system when faced with uncertainties such as renewable energy generation fluctuations and changes in load demand.

关键词： variational autoencoder Diffusion Model Multi-Scenario Generation Integrated Energy Scheduling Uncertainty Modeling Low-Carbon Optimization

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Dynamical mode recognition of coupled flame oscillators by supervised and unsupervised learning approaches

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KNOWLEDGE-BASED SYSTEMS 2025年 307卷

作者： Xu, Weiming Yang, Tao Zhang, Peng City Univ Hong Kong Dept Mech Engn Kowloon Tong Kowloon Hong Kong 999077 Peoples R China Hong Kong Polytech Univ Dept Mech Engn Hung Hong Kowloon Hong Kong 999077 Peoples R China

Combustion instability in gas turbines and rocket engines, as one of the most challenging problems in combustion research, arises from the complex interactions among flames influenced by chemical reactions, heat and mass transfer, and acoustics. Identifying and understanding combustion instability is essential for ensuring the safe and reliable operation of many combustion systems, where exploring and classifying the dynamical behaviors of complex flame systems is a core task. To facilitate fundamental studies, the present work concerned dynamical mode recognition of coupled flame oscillators made of flickering buoyant diffusion flames, which have gained increasing attention in recent years but are not sufficiently understood. The time series data of flame oscillators were generated through fully validated reacting flow simulations. Due to the limitations of expertise-based models, a data-driven approach was adopted. In this study, a nonlinear dimensional reduction model of variational autoencoder (VAE) was used to project the high dimensional data onto a 2-dimensional latent space. Based on phase trajectories in the latent space, both supervised and unsupervised classifiers were proposed for datasets with and without well-known labeling, respectively. For labeled datasets, we established the Wassersteindistance-based classifier (WDC) for mode recognition;for unlabeled datasets, we developed a novel unsupervised classifier (GMM-DTW) combining dynamic time warping (DTW) and Gaussian mixture model (GMM). Through comparing with conventional approaches for dimensionality reduction and classification, the proposed supervised and unsupervised VAE-based approaches exhibit a prominent performance across seven assessment metrics for distinguishing dynamical modes, implying their potential extension to dynamical mode recognition in complex combustion problems.

关键词： variational autoencoder Dynamical mode recognition Coupled flames Wasserstein distance Dynamic time warping

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A generalized network with domain invariance and specificity representation for bearing remaining useful life prediction under unknown conditions

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KNOWLEDGE-BASED SYSTEMS 2025年 310卷

作者： Zheng, Qing Teng, Pengtao Zhang, Kai Ding, Guofu Lai, Xuwei Li, Zhixuan Yuan, Zhaocheng Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Southwest Jiaotong Univ Technol & Equipment Rail Transit Operat & Maintena Chengdu 610031 Peoples R China Southwest Jiaotong Univ State Key Lab Rail Transit Vehicle Syst Chengdu 610031 Peoples R China Sichuan Special Antenna Mat & Adv Forming Engn Tec Mianyang 621000 Peoples R China Chengdu Inst Special Equipment Inspect & Testing Chengdu 610031 Peoples R China

The requirement to satisfy the assumption of identically and independently distributed data limits the practical application of traditional remaining useful life (RUL) prediction methods. In domain generalization methods, data augmentation and domain-invariant representations face the challenges of maintaining sequence consistency for long time-series data and prognosticating RUL under unknown operating conditions. A novel generalized network with invariance and specificity representation is proposed to address these issues. First, the runto-failure bearing data were divided into healthy and degraded stages. Next, the time-vector quantized variational autoencoder network generates diverse data from the degradation stage. Subsequently, the domain invariant and domain-specific feature representation network (DIDSR) is proposed for cross-condition prediction of rolling bearing RUL. The DIDSR network is divided into two parts: the domain-invariant component and the domain-specific component. The former extracts shared features from multiple source domains, while the latter learns the characteristics of each source domain through a domain classifier and weights them to obtain the final prediction. Ultimately, the proposed method is extensively evaluated on two bearing run-to-failure datasets. Comparative and ablation experiments validate the effectiveness and superiority of the approach.

关键词： Rotating machinery variational autoencoder Time series generation Domain-invariant representation learning Remaining useful life prognosis

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