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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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Identifying Performance Inefficiencies of Parallel Program with Spatial and Temporal Trace Analysis

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IEEE Transactions on Parallel and Distributed Systems 2025年第7期36卷 1387-1400页

作者： Xuan, Zhibo Sun, Xin You, Xin Yang, Hailong Luan, Zhongzhi Liu, Yi Qian, Depei Beihang University State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beijing100191 China

Performance inefficiencies can lead to performance anomalies in parallel programs. Existing performance analysis tools either have a limited detection scope or require significant domain knowledge to use, which constrains their practical adoption to identify performance inefficiencies. In this paper, we propose STAD, a performance analysis tool for parallel programs that considers both spatial and temporal patterns within trace data. STAD captures the spatial communication patterns between processes using a spatial communication pattern graph. It then adopts a dynamic graph neural network-based unsupervised model to learn the evolving temporal patterns along the timeline. Additionally, STAD diagnoses the root causes of performance anomalies by exploiting the aggregated feature of anomalies along the call tree. Our evaluation results demonstrate that STAD can effectively detect performance anomalies with acceptable overhead and diagnose the root causes attributed to both the program itself and the running environment. © 1990-2012 IEEE.

关键词： Performance Analysis Training Libraries Graph Neural Networks Analytical Models Accuracy Sun Phasor Measurement Units Heating Systems Hardware Anomalies Parallel Programs Performance Analysis Root Cause Parallel Computing Spatial Patterns Communication Patterns Trace Data Programming Tools Dynamic Graph Artificial Neural Network Matrix Elements Unsupervised Learning Unique Way Anomaly Detection Red Points Graph Convolutional Network System Noise variational autoencoder Graph Neural Networks Program Execution Time Slice Network Congestion Node Score Anomaly Score Dynamic Topology Graph Attention Network Dynamic Neural Network Load Imbalance White Blocks Dynamic Network Graph Neural Network Model Tree View Vector Of Node

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variational Kernel Density Estimation Recommendation Algorithm for Users with Diverse Activity Levels 29th

Variational Kernel Density Estimation Recommendation Algorit...

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29th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Liu, Wei Liang, Shangsong Zhu, Huaijie Hou, Leong U. Yu, Jianxing Li, Xiang Yin, Jian Sun Yat Sen Univ Sch Artificial Intelligence Guangzhou Peoples R China Technol Innovat Ctr Collaborat Applicat Nat Resou Guangzhou Peoples R China Univ Macau Taipa Macao Peoples R China WakeData Co Zhuhai Peoples R China

ISBN: (纸本)9789819757787;9789819757794

Top-N recommendation is widely accepted as an effective method in personalized service that well serves users of different interests. However, as analyzed from the SOTAs, their performance on the users with diverse activity levels has significant distinction, which seriously damage the service quality of personalized recommendation. Existing studies do not pay a high attention to this issue, which simply assume the preference of all users follows a common probability distribution and then use a fixed schema (e.g., one latent vector) to model user representation. This assumption makes existing models hard to accommodate users of diverse activity levels. In this work, we propose a variational Kernel Density Estimation (VKDE) model, a non-parametric estimation, which aims to fit arbitrary preference distributions for users. VKDE constructs user (global) preference distribution with multiple local distributions collectively. We propose a variational kernel function to infer user one-faceted interests and generate each local distribution. A sampling strategy for user one-faceted interest is further proposed to reduce training complexity and keep the recommendation effectiveness. Our experimental results on three public datasets show that VKDE outperforms SOTAs and greatly improves the accuracy for users of diverse activity levels.

关键词： variational autoencoder Kernel Density Estimation Implicit Feedback Personalized Recommendation.

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Rethinking the core symptom of schizophrenia: An exploration of disorganized thought centrality in psychosis

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Schizophrenia Research 2025年 281卷 201-208页

作者： Li, Jingjing Zhang, Xiaoyun Liu, Fang Pang, Lijuan Sun, Yao Chen, Zhihan Kang, Yulin Hei, Gangrui Song, Xueqin Department of Psychiatry the First Affiliated Hospital of Zhengzhou University Zhengzhou China Henan International Joint Laboratory of Biological Psychiatry Zhengzhou China Henan Psychiatric Transformation Research Key Laboratory Zhengzhou University Zhengzhou China Department of Operation Management the First Affiliated Hospital of Zhengzhou University Zhengzhou China Henan Institute of Interconnected Intelligent Health Management Zhengzhou China Henan Key Laboratory of Chronic Disease Prevention and Therapy & Intelligent Health Management Zhengzhou China Department of Social Medicine College of Health Management China Medical University Shenyang China Chinese Research Academy of Environmental Sciences China

Background and hypothesis: Schizophrenic symptoms are currently classified into several dimensions: positive symptoms, negative symptoms, disorganized thought, cognitive deficits, and general symptoms. This multi-dimensional categorization of symptoms has sparked an ongoing debate in the field: what is the core symptom of schizophrenia? This question remains central to our understanding of the disorder. In our previous research, we demonstrated the centrality of disorganized thought in schizophrenia. Building on this finding, we hypothesized that this centrality is not a feature of all psychotic disorders. Study design: We analyzed Brief Psychiatric Rating Scale (BPRS) data from 17,414 outpatients using a variational autoencoder (VAE) model. Clustering analysis was performed on the latent representations, followed by network analysis to examine symptom interactions within identified clusters. Analyses were further stratified by age and BPRS item types. Study results: Clustering analysis revealed four distinct groups, in which one cluster exhibiting characteristics with more severe symptoms (mean BPRS score 99.73 [SD 9.46]), younger age (mean 19.48 [SD 9.31] years), and smaller sample size showed a unique centrality of thought disturbance among five subscale symptoms. This pattern persisted in both adult and adolescent subgroups and when analyzing only verbally reported items. Conclusions: Our findings support the specificity of disorganized thought centrality in one particular cluster, distinguishing it from other psychiatric presentations. This study demonstrates the value in analyzing large-scale clinical data to refine our understanding of complex psychiatric disorders, potentially informing future targeted interventions. © 2025 Elsevier B.V.

关键词： Disorganized thought Network analysis Schizophrenia Self-disorder Symptom centrality variational autoencoder

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variational autoencoders-LSTM based fault detection of time-dependent high dimensional processes

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第4期62卷 1092-1107页

作者： Maged, Ahmed Lui, Chun Fai Haridy, Salah Xie, Min City Univ Hong Kong Dept Adv Design & Syst Engn Kowloon Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen Peoples R China Benha Univ Benha Fac Engn Dept Mech Engn Banha Egypt Univ Sharjah Coll Engn Dept Ind Engn & Engn Management Sharjah U Arab Emirates City Univ Hong Kong Kowloon Tong Kowloon Hong Kong Peoples R China

In modern large-scale industrial processes, data are often high dimensional time-dependent due to the frequent sampling, dynamic nature and large number of variables. Appropriate monitoring of such processes allows for efficient decision-making that can improve the baseline of manufacturing companies either through decreasing production costs or enhancing production efficiency. Various latent variable-based control charts have been proposed for addressing high dimensional data;however, many of these methods assume that the data are independent and normally distributed. The violation of these assumptions results in an increased false alarm rate, in addition to the deterioration in the performance of such methods. In this study, we propose a variational autoencoder-Long Short Term Memory (VAE-LSTM) deep learning based T(2 )chart that integrates the unique features of both VAE and LSTM for intelligent fault detection of time-dependent high dimensional processes. The effectiveness and applicability of the proposed model are demonstrated through extensive simulations, an open-source online dataset, and a real case study.

关键词： Anomaly detection multivariate process monitoring deep learning variational autoencoder LSTM

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variational autoencoders for Biomedical Signal Morphology Clustering and Noise Detection

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第1期28卷 169-180页

作者： Nowroozilarki, Zhale Mortazavi, Bobak J. Jafari, Roozbeh Texas A&M Univ Dept Comp Sci & Engn College Stn TX 77840 USA Texas A&M Univ Dept Biomed Engn Elect & Comp Engn Comp Sci & Engn College Stn TX 77840 USA

Accurate estimation of physiological biomarkers using raw waveform data from non-invasive wearable devices requires extensive data preprocessing. An automatic noise detection method would offer significant utility for various domains. As data labeling is onerous, having a minimally supervised abnormality detection method for input data, as well as an estimation of the severity of the signal corruptness, is essential. We propose a model-free, time-series biomedical waveform noise detection framework using a variational autoencoder coupled with Gaussian Mixture Models, which can detect a range of waveform abnormalities without annotation, providing a confidence metric for each segment. Our technique operates on biomedical signals that exhibit periodicity of heart activities. This framework can be applied to any machine learning or deep learning model as an initial signal validator component. Moreover, the confidence score generated by the proposed framework can be incorporated into different models' optimization to construct confidence-aware modeling. The result confirms that our approach removes noisy cardiac cycles and the remaining signals, classified as clean, exhibit a 59.92% reduction in the standard deviation of DTW distances. Using a dataset of bio-impedance data of 97885 cardiac cycles, we further demonstrate a significant improvement in the downstream task of cuffless blood pressure estimation, with an average reduction of 2.67 mmHg root mean square error (RMSE) of Diastolic Blood pressure and 2.13 mmHg RMSE of systolic blood pressure, with increases of average Pearson correlation of 0.28 and 0.08, with a statistically significant improvement of signal-to-noise ratio respectively in the presence of different synthetic noise sources.

关键词： Biomarker estimation biomedical signal processing deep learning Gaussian mixture models variational autoencoder

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variational autoencoders for 3D data processing

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第2期57卷 42-42页

作者： Molnar, Szilard Tamas, Levente Tech Univ Cluj Napoca Automat Dept Memorandumului St 25 Cluj Napoca 400114 Romania

variational autoencoders (VAEs) play an important role in high-dimensional data generation based on their ability to fuse the stochastic data representation with the power of recent deep learning techniques. The main advantages of these types of generators lie in their ability to encode the information with the possibility to decode and generalize new samples. This capability was heavily explored for 2D image processing;however, only limited research focuses on VAEs for 3D data processing. In this article, we provide a thorough review of the latest achievements in 3D data processing using VAEs. These 3D data types are mostly point clouds, meshes, and voxel grids, which are the focus of a wide range of applications, especially in robotics. First, we shortly present the basic autoencoder with the extensions towards the VAE with further subcategories relevant to discrete point cloud processing. Then, the 3D data specific VAEs are presented according to how they operate on spatial data. Finally, a few comprehensive table summarizing the methods, codes, and datasets as well as a citation map is presented for a better understanding of the VAEs applied to 3D data. The structure of the analyzed papers follows a taxonomy, which differentiates the algorithms according to their primary data types and application domains.

关键词： variational autoencoder Survey 3D data

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variational autoencoders for Localized Mesh Deformation Component Analysis

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第10期44卷 6297-6310页

作者： Tan, Qingyang Zhang, Ling-Xiao Yang, Jie Lai, Yu-Kun Gao, Lin Chinese Acad Sci Inst Comp Technol Beijing Key Lab Mobile Comp & Pervas Device Beijing 100864 Peoples R China Univ Maryland College Pk MD 20742 USA Univ Chinese Acad Sci Beijing 100049 Peoples R China Cardiff Univ Sch Comp Sci & Informat Cardiff CF10 3AT Wales

Spatially localized deformation components are very useful for shape analysis and synthesis in 3D geometry processing. Several methods have recently been developed, with an aim to extract intuitive and interpretable deformation components. However, these techniques suffer from fundamental limitations especially for meshes with noise or large-scale nonlinear deformations, and may not always be able to identify important deformation components. In this paper we propose a mesh-based variational autoencoder architecture that is able to cope with meshes with irregular connectivity and nonlinear deformations, assuming that the analyzed dataset contains meshes with the same vertex connectivity, which is common for deformation analysis. To help localize deformations, we introduce sparse regularization in this framework, along with spectral graph convolutional operations. Through modifying the regularization formulation and allowing dynamic change of sparsity ranges, we improve the visual quality and reconstruction ability of the extracted deformation components. Our system also provides a nonlinear approach to reconstruction of meshes using the extracted basis, which is more effective than the current linear combination approach. As an important application of localized deformation components and a novel approach on its own, we further develop a neural shape editing method, achieving shape editing and deformation component extraction in a unified framework, and ensuring plausibility of the edited shapes. Extensive experiments show that our method outperforms state-of-the-art methods in both qualitative and quantitative evaluations. We also demonstrate the effectiveness of our method for neural shape editing.

关键词： Strain Shape Three-dimensional displays Principal component analysis Geometry Convolution Solid modeling 3D meshes variational autoencoder graph convolution sparsity regularization

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CO2 plume Imaging with accelerated deep learning-based data Assimilation: Applications at the Illinois Basin-Decatur Carbon sequestration project

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FUEL 2025年 385卷

作者： Sakai, Takuto Nagao, Masahiro Datta-Gupta, Akhil Mishra, Srikanta Texas A&M Univ 3116 TAMU College Stn TX 77843 USA

It is crucial to monitor the CO2 plume effectively throughout the life cycle of a geologic CO2 sequestration project to ensure safety and storage efficiency. However, the computational cost of existing data assimilation methods can be prohibitively expensive due to the complex physics with multi-component non-isothermal simulation and high dimensionality of field-scale reservoir models. We address this challenge by proposing an accelerated deep learning-based workflow for model calibration and prediction of CO2 plume evolution in the reservoir. In the proposed workflow, a neural network model utilizes available monitoring data such as downhole pressure and temperature measurements as input and predicts the propagating pressure 'front' using the diffusive time of flight (DTOF) map which is considered as representative reservoir image of the flow field. The DTOF is the arrival time of pressure front propagation, which can be computed by the Fast Marching Method (FMM) rapidly without flow simulations. Reservoir model calibration can be implemented by selecting the training data samples that describe the predicted DTOF map based on observed data. The computational efficiency of the data assimilation framework is significantly enhanced in two ways. First, instead of using multiple CO2 saturation maps for different timesteps, a single DTOF map is used as the output image. Since the reservoir dynamics is compressed into a single DTOF image, the memory and computational cost are reduced significantly. Second, an optimum coarsening of geologic model is applied, which substantially reduces the training data generation cost. The optimum coarsening scheme is utilized to maximize the computational efficiency while minimizing the error of simulated monitoring data, such as well pressure and temperature data. The power and efficacy of our workflow is demonstrated by application to the Illinois Basin-Decatur Project (IBDP), a large-scale CO2 storage test in saline aquifer. We achie

关键词： Deep learning Data Assimilation variational autoencoder Diffusive time of flight CO2 Storage

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variational autoencoderS FOR UNSUPERVISED OBJECT COUNTING FROM VHR IMAGERY: APPLICATIONS IN DWELLING EXTRACTION FROM FORCIBLY DISPLACED PEOPLE SETTLEMENT AREAS

VARIATIONAL AUTOENCODERS FOR UNSUPERVISED OBJECT COUNTING FR...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Gella, Getachew Workineh Gangloff, Hugo Wendt, Lorenz Tiede, Dirk Lang, Stefan Paris Lodron Univ Salzburg PLUS Christian Doppler Lab Geospatial & EO Based Human Dept Geoinformat Z GIS Salzburg Austria Univ Paris Saclay AgroParisTech INRAE UMR MIA Paris Saclay F-91120 Palaiseau France

ISBN: (纸本)9798350320107

Even though computer vision models are excellent for automatic scene segmentation and object identification from remotely sensed imagery, they demand a huge corpus of annotated data for the training and validation which is a huge challenge in humanitarian emergency response. To tackle this problem, we propose unsupervised dwelling object counting combining variational autoencoder (VAE) with an anomaly detection approach. The approach is tested in six Forcibly Displaced People (FDP) settlement areas situated in different parts of the world. Using an anomaly map computed with the VAE model, we demonstrated the possibility of properly locating dwelling objects using anomaly maps. Dwelling counts are obtained by further segmenting anomaly maps. Results show that, though it has strong spatio-temporal variation, the VAE model exhibits promising potential for locating and counting dwellings. It is also observed that in FDP settlements with dense buildings and extremely low contrast between buildings and ground or environment, the performance is relatively lower than the performance achieved in settlement areas with regularly spaced and less complex building structures.

关键词： Anomaly detection Dwelling extraction Emergency response variational autoencoder

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