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Exploring attractor reconstruction derived from encoder-decoder time series prediction

Physical Review E 2025年第5期111卷 054214-054214页

作者： Yifei Chen Youfang Lin Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence School of Computer Science and Technology Beijing Jiaotong University Beijing 100044 People's Republic of China

Reconstructing the state space is a generic and basic step for studying nonlinear dynamics with partial observables. Recently, time series forecasting through an encoder-decoder artificial neural network is expected to implicitly realize this procedure. However, existing explanatory and comparative studies are insufficient to strongly support such an argument. This paper analyzes the feasibility of such a reconstruction from the predictive perspective. To produce the topologically equivalent attractor via the encoder, we generalize the conditions about the predictive range, measurement noise and model suitability. Based on these analyses, we explore an adapted reconstruction methodology from time series prediction. Moreover, we found that the inverted transformer network is more appealing for multivariate observations and the penalty function of noise amplification compensates for the deficiency of standard prediction error. We conduct an experimental comparison between the prediction-based reconstruction and typical delay coordinate reconstruction in multiple situations. The comparative outcomes demonstrate that sensible predictive designs can steadily reconstruct the attractor at the best level. Additionally, we recover the underlying dynamics from real meteorological data associated with El Niño–Southern Oscillation.

关键词： Forecasting

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The Chinese Hα Solar Explorer(CHASE) mission: An overview

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science China(Physics,Mechanics & Astronomy) 2022年第8期65卷 2-9页

作者： Chuan Li Cheng Fang Zhen Li MingDe Ding PengFei Chen Ye Qiu Wei You Yuan Yuan MinJie An HongJiang Tao XianSheng Li Zhe Chen Qiang Liu Gui Mei Liang Yang Wei Zhang WeiQiang Cheng JianXin Chen ChangYa Chen Qiang Gu QingLong Huang MingXing Liu ChengShan Han HongWei Xin ChangZheng Chen YiWei Ni WenBo Wang ShiHao Rao HaiTang Li Xi Lu Wei Wang Jun Lin YiXian Jiang LingJie Meng Jian Zhao School of Astronomy and Space Science Nanjing University Key Laboratory for Modern Astronomy and Astrophysics (Nanjing University) Ministry of Education Shanghai Institute of Satellite Engineering Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Shanghai Academy of Spaceflight Technology China Center for Resources Satellite Data and Application China Aerospace Science and Technology Corporation Key and Special Project Center of China National Space Administration (CNSA)

The Chinese Hα Solar Explorer(CHASE), dubbed “Xihe”—Goddess of the Sun, was launched on October 14, 2021 as the first solar space mission of China National Space Administration(CNSA). The CHASE mission is designed to test a newly developed satellite platform and to acquire the spectroscopic observations in the Hα waveband. The Hα Imaging Spectrograph(HIS)is the scientific payload of the CHASE satellite. It consists of two observational modes: raster scanning mode and continuum imaging mode. The raster scanning mode obtains full-Sun or region-of-interest spectral images from 6559.7 to 6565.9 ? and from 6567.8 to 6570.6 ? with 0.024 ? pixel spectral resolution and 1 min temporal resolution. The continuum imaging mode obtains photospheric images in continuum around 6689 ? with the full width at half maximum of 13.4 ?. The CHASE mission will advance our understanding of the dynamics of solar activity in the photosphere and chromosphere. In this paper, we present an overview of the CHASE mission including the scientific objectives, HIS instrument overview, data calibration flow, and first results of on-orbit observations.

关键词： space-based telescope solar physics chromosphere photosphere

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Toward Intelligent Financial Advisors for Identifying Potential Clients:A Multitask Perspective

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Big data Mining and Analytics 2022年第1期5卷 64-78页

作者： Qixiang Shao Runlong Yu Hongke Zhao Chunli Liu Mengyi Zhang Hongmei Song Qi Liu Department of Anhui Province Key Laboratory of Big Data Analysis and Application(BDAA) School of Computer Science and TechnologyUniversity of Science and Technology of China Department of College of Management and Economics Tianjin University School of Management Hefei University of Technology Department of Information Technology China Merchants Bank

Intelligent Financial Advisors(IFAs)in online financial applications(apps)have brought new life to personal investment by providing appropriate and high-quality portfolios for *** real-world scenarios,identifying potential clients is a crucial issue for IFAs,i.e.,identifying users who are willing to purchase the ***,extracting useful information from various characteristics of users and further predicting their purchase inclination are ***,two critical problems encountered in real practice make this prediction task challenging,i.e.,sample selection bias and data *** this study,we formalize a potential conversion relationship,i.e.,user→activated user→client and decompose this relationship into three related ***,we propose a Multitask Feature Extraction Model(MFEM),which can leverage useful information contained in these related tasks and learn them jointly,thereby solving the two problems *** addition,we design a two-stage feature selection algorithm to select highly relevant user features efficiently and accurately from an incredibly huge number of user feature ***,we conduct extensive experiments on a real-world dataset provided by a famous fintech *** results clearly demonstrate the effectiveness of MFEM.

关键词： Intelligent Financial Advisor(IFA) potential client identification MultiTask Learning(MTL) feature selection

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DDIPrompt: Drug-Drug Interaction Event Prediction based on Graph Prompt Learning

arXiv

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arXiv 2024年

作者： Wang, Yingying Xiong, Yun Wu, Xixi Sun, Xiangguo Zhang, Jiawei Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China The Chinese University of Hong Kong Hong Kong University of California Davis United States

Drug combinations can cause adverse drug-drug interactions(DDIs). Identifying specific effects is crucial for developing safer therapies. Previous works on DDI event prediction have typically been limited to using labels of specific events as supervision, which renders them insufficient to address two significant challenges: (1) the bias caused by highly imbalanced event distribution where certain interaction types are vastly under-represented. (2) the scarcity of labeled data for rare events, a pervasive issue where rare yet potentially critical interactions are often overlooked or under-explored due to limited available data. In response, we offer "DDIPrompt", an innovative solution inspired by the recent advancements in graph prompt learning. Our framework aims to address these issues by leveraging the intrinsic knowledge from pre-trained models, which can be efficiently deployed with minimal downstream data. Specifically, to solve the first challenge, DDIPrompt features a hierarchical pre-training strategy to foster a generalized and comprehensive understanding of drug properties. It captures intra-molecular structures through augmented links based on structural proximity between drugs, further learns inter-molecular interactions emphasizing edge connections rather than concrete catagories. For the second challenge, we implement a prototype-enhanced prompting mechanism during inference. This mechanism, refined by few-shot examples from each category, effectively harnesses the rich pre-training knowledge to enhance prediction accuracy, particularly for these rare but crucial interactions. Extensive experiments on two benchmark datasets demonstrate DDIPrompt's SOTA performance, especially for those rare DDI events. Copyright © 2024, The Authors. All rights reserved.

关键词： Drug interactions

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Dynamic Graph Neural Networks-Based Alert Link Prediction for Online Service Systems 23

Dynamic Graph Neural Networks-Based Alert Link Prediction fo...

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Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering

作者： Yiru Chen Chenxi Zhang Zhen Dong Dingyu Yang Xin Peng Jiayu Ou Hong Yang Zheshun Wu Xiaojun Qu Wei Li School of Computer Science Fudan University China and Shanghai Key Laboratory of Data Science Fudan University China and Shanghai Collaborative Innovation Center of Intelligent Visual Computing China Alibaba Group China

ISBN: (纸本)9798350329964

A fault in large online service systems often triggers numerous alerts due to the complex business and component dependencies among services, which is known as "alert storm". In a short time, an online service system may generate a huge amount of alert data. This poses a challenge for on-call engineers to identify alerts that are associated with a system failure for root cause analysis. In this paper, we propose DyAlert, a dynamic graph neural networks-based approach for linking alerts that might be triggered by a same fault to reduce the burden of on-call engineers in the fault analysis. Our insight is that alerts are often triggered by alert propagation when a system failure occurs, e.g., alert a would lead to the occurrence of alert b. Whether two alerts should be linked depends on if one alert is triggered by the propagation of the other. Leveraging this insight, we design a dynamic graph (namely Alert-Metric Dynamic Graph) that describes the propagation process of alerts. Based on the dynamic graph, we train a neural networks-based model to predict alert links. We evaluate DyAlert with real-world data collected from an online service system running 85 business units and about 30,000 different services in a large enterprise. The results show that DyAlert is effective in predicting alert links and it outperforms the state-of-the-art approaches with an average increase of 0.259 in F1-score.

关键词： linked alerts

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Auto-MatRegressor材料性能自动预测器:解放材料机器学习"调参师"

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science Bulletin 2023年第12期68卷 1259-1270,M0004页

作者：刘悦王双燕杨正伟 Maxim Avdeev 施思齐 School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Shanghai Engineering Research Center of Intelligent Computing System Shanghai 200444China State Key Laboratory of Advanced Special Steel School of Materials Science and EngineeringShanghai UniversityShanghai 200444China Materials Genome Institute Shanghai UniversityShanghai 200444China Zhejiang Laboratory Hangzhou 311100China Australian Nuclear Science and Technology Organisation Sydney 2232Australia School of Chemistry The University of SydneySydney 2006Australia

机器学习因其能够快速、精准拟合数据的潜在模式而被广泛应用于材料构效关系研究。然而,材料科学家往往需要进行繁琐的模型选择及参数寻优才能构建出高精度预测模型,为了解放材料机器学习"调参师",本文研发了基于元学习的材... 详细信息

机器学习因其能够快速、精准拟合数据的潜在模式而被广泛应用于材料构效关系研究。然而,材料科学家往往需要进行繁琐的模型选择及参数寻优才能构建出高精度预测模型,为了解放材料机器学习"调参师",本文研发了基于元学习的材料性能自动预测器,采集了60份文献公开数据集与60份标准数据集,基于此训练18种常用回归算法并获得其预测性能,定义与计算了27个刻画数据集特点的元特征,以此构建了一份蕴含建模经验的元数据集;同时,创建了表征数据集所属材料类型的类别树,将其嵌入基于距离的元学习算法,进一步耦合贝叶斯优化算法,实现领域知识和元数据协同驱动下的自动算法推荐和模型参数确定,实验结果表明,材料科学家仅需为新材料性能预测任务提供数据集,便可利用该预测器高效地构建具有与文献报道相当或更高预测精度的机器学习模型.

关键词：机器学习贝叶斯优化算法预测器材料科学家元数据参数寻优预测性能材料性能

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UVTM: Universal Vehicle Trajectory Modeling with ST Feature Domain Generation

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IEEE Transactions on Knowledge and data Engineering 2025年

作者： Lin, Yan Hu, Jilin Guo, Shengnan Yang, Bin Jensen, Christian S. Lin, Youfang Wan, Huaiyu Aalborg University Department of Computer Science Aalborg9220 Denmark East China Normal University School of Data Science and Engineering Shanghai200050 China Beijing Jiaotong University Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence School of Computer Science and Technology Beijing100044 China

Vehicle movement is frequently captured in the form of GPS trajectories, i.e., sequences of timestamped GPS locations. Such data is widely used for various tasks such as travel-time estimation, trajectory recovery, and trajectory prediction. A universal vehicle trajectory model could be applied to different tasks, removing the need to maintain multiple specialized models, thereby reducing computational and storage costs. However, creating such a model is challenging when the integrity of trajectory features is compromised, i.e., in scenarios where only partial features are available or the trajectories are sparse. To address these challenges, we propose the Universal Vehicle Trajectory Model (UVTM), which can effectively adapt to different tasks without excessive retraining. UVTM incorporates two specialized designs. First, it divides trajectory features into three distinct domains. Each domain can be masked and generated independently to accommodate tasks with only partially available features. Second, UVTM is pre-trained by reconstructing dense, feature-complete trajectories from sparse, feature-incomplete counterparts, enabling strong performance even when the integrity of trajectory features is compromised. Experiments involving four representative trajectory-related tasks on three real-world vehicle trajectory datasets provide insight into the performance of UVTM and offer evidence that it is capable of meeting its objectives. © 1989-2012 IEEE.

关键词： Travel time

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Hierarchical Multi-Task Learning for Fabric Component Analysis Based on NIR Spectral Signals 48

Hierarchical Multi-Task Learning for Fabric Component Analys...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Kim, Joseph Wu, Dong Chi, Mingmin Xu, Gaoqi Fudan University School of Computer Science Shanghai Key Llaboratory of Data Science China Zhengzhou Zhongke Institute of Integrated Circuit and System Application China Zhongshan Fudan Joint Innovation Center Zhongshan PoolNet Technology Co. Ltd China

ISBN: (纸本)9781728163277

Near Infrared (NIR) Spectral signal has been successfully applied to fabric component analysis (FCA), which is used to identify the category of the textile (defined as a classification task) and its corresponding content for that category (defined as a regression problem). Unlike conventional classification tasks, the prediction results usually contain more than two types of materials, i.e., classes. In addition, the spectral curves belonging to the same parent fiber are similar and thus lead to the problem that the intra-class variance is usually larger than the inter-class variance. The paper proposes a hierarchical architecture for Multi-Label Classification (MLC) and Multi-Output Regression (MOR) to simultaneously identify fabric classes and their contents, i.e., fabric component analysis. In addition, two constraints are applied to the loss function for final classification and regression results. Experimental results conducted on the FEAT-NIR dataset show that the proposed method successfully obtains the best performance compared to the baselines. © 2023 IEEE.

关键词： Fine-grained fabric component analysis Multi-label classification Multi-output regression Near Infrared (NIR) signals Neural network for NIR signals

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A Highly Selective Wideband Filtering Stacked DDPA 4

A Highly Selective Wideband Filtering Stacked DDPA

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4th IEEE International Conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2024

作者： Tian, Kang Zhang, JiaSheng La, Dongsheng Northeastern University at Qinhuangdao Hebei Key Laboratory of Marine Perception Network and Data Processing School of Computer and Communication Engineering Qinhuangdao066000 China An Hui Ming Bian Electronic Science & Technology Co. Ltd. China

ISBN: (纸本)9798350363586

In this paper, a compact and highly selective stacked filtering dense dielectric patch (DDP) antenna (DDPA) is proposed. By placing a pair of thin DDP with high dielectric constant along the y axis on the DDP with low dielectric constant, the resonant frequency of the TE12 mode is adjusted to approach low frequency and form a passband with the TE11 mode. By loading a U-shaped microstrip feeding line on the microstrip feeding line, the bandwidth can be further broadened and the TE11, TE11 + δ and TE12 modes can be successfully excited in the passband. The radiation nulls are generated by the U-shaped microstrip line and non-radiative TE12 mode, respectively. By adjusting the length of the U-shaped microstrip line and the size of DDP with high dielectric constant, the independent adjustment of radiation nulls can be achieved. Finally, the proposed filtering antenna achieves a relative bandwidth of 11.66 % (4.04-4.54 GHz). The average realized gain and peak gain are 6.89 dBi and 7.1 dBi, respectively. The realized gain in the passband is very flat, and the fluctuation is less than 0.7 dBi. The proposed antenna has the advantages of compactness, high selectivity and flat gain, and it can be applied to modern satellite communication and wireless communication systems. © 2024 IEEE.

关键词： Bandwidth

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Stable Learning via Triplex Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5267-5276页

作者： Yang, Shuai Jiang, Tingting Dang, Qianlong Gu, Lichuan Wu, Xindong Anhui Agricultural University School of Information and Artificial Intelligence Hefei230036 China Anhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing Hefei230036 China Northwest A & F University College of Science Yangling712100 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

Stable learning aims to learn a model that generalizes well to arbitrary unseen target domain by leveraging a single source domain. Recent advances in stable learning have focused on balancing the distribution of confounders for each feature to eliminate spurious correlations. However, previous studies treat all features equally without considering the difficulties of confounder balancing associated with different features, and regard irrelevant features as confounders, deteriorating generalization performance. To tackle these issues, this article proposes a novel triplex learning (TriL) based stable learning algorithm, which performs sample reweighting, causal feature selection, and representation learning to remove spurious correlations. Specifically, first, TriL adaptively assigns weights to the confounder balancing term of each feature in accordance with the difficulties of confounder balancing, and aligns the confounder distribution of each feature by learning a group of sample weights. Second, TriL integrates the sample weights into a weighted cross-entropy model to compute causal effects of features for excluding irrelevant features from the confounder set. Finally, TriL relearns a set of sample weights and uses them to guide a new supervised dual-autoencoder containing two classifiers to learn feature representations. TriL forces the results of two classifiers to remain consistent for removing spurious correlations by using a cross-classifier consistency regularization. Extensive experiments on synthetic and two real-world datasets show the superiority of TriL compared with seven methods. © 2024 IEEE.

关键词： Feature Selection

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