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Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting

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arXiv 2023年

作者： Jin, Ming Shi, Guangsi Li, Yuan-Fang Xiong, Bo Zhou, Tian Salim, Flora D. Zhao, Liang Wu, Lingfei Wen, Qingsong Pan, Shirui School of Information and Communication Technology Griffith University Gold Coast Australia Department of Data Science and AI Monash University Melbourne Australia International Max Plank Research School for Intelligent Systems The University of Stuttgart Stuttgart Germany Alibaba Group Hangzhou China School of Computer Science and Engineering University of New South Wales Sydney Australia Department of Computer Science Emory University Atlanta United States Anytime.AI New York United States Squirrel Ai Learning Bellevue United States

Time series forecasting has remained a focal point due to its vital applications in sectors such as energy management and transportation planning. Spectral-temporal graph neural network is a promising abstraction underlying most time series forecasting models that are based on graph neural networks (GNNs). However, more is needed to know about the underpinnings of this branch of methods. In this paper, we establish a theoretical framework that unravels the expressive power of spectral-temporal GNNs. Our results show that linear spectral-temporal GNNs are universal under mild assumptions, and their expressive power is bounded by our extended first-order Weisfeiler-Leman algorithm on discrete-time dynamic graphs. To make our findings useful in practice on valid instantiations, we discuss related constraints in detail and outline a theoretical blueprint for designing spatial and temporal modules in spectral domains. Building on these insights and to demonstrate how powerful spectral-temporal GNNs are based on our framework, we propose a simple instantiation named Temporal Graph Gegenbauer Convolution (TGGC), which significantly outperforms most existing models with only linear components and shows better model efficiency. Our findings pave the way for devising a broader array of provably expressive GNN-based models for time series. © 2023, CC BY.

关键词： Time series

Structured probabilistic end-to-end learning from crowds 29

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Structured probabilistic end-to-end learning from crowds

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Chen, Zhijun Wang, Huimin Sun, Hailong Chen, Pengpeng Han, Tao Liu, Xudong Yang, Jie SKLSDE Lab School of Computer Science and Engineering Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China Web Information Systems Delft University of Technology Netherlands

ISBN: (纸本)9780999241165

End-to-end learning from crowds has recently been introduced as an EM-free approach to training deep neural networks directly from noisy crowdsourced annotations. It models the relationship between true labels and annotations with a specific type of neural layer, termed as the crowd layer, which can be trained using pure backpropagation. Parameters of the crowd layer, however, can hardly be interpreted as annotator reliability, as compared with the more principled probabilistic approach. The lack of probabilistic interpretation further prevents extensions of the approach to account for important factors of annotation processes, e.g., instance difficulty. This paper presents SpeeLFC, a structured probabilistic model that incorporates the constraints of probability axioms for parameters of the crowd layer, which allows to explicitly model annotator reliability while benefiting from the end-to-end training of neural networks. Moreover, we propose SpeeLFC-D, which further takes into account instance difficulty. Extensive validation on real-world datasets shows that our methods improve the state-of-the-art. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Deep neural networks

Graph neural network and expert-guided deep reinforcement learning for solving flexible job-shop scheduling problem

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Computers & Operations Research 2025年 183卷

作者： Wenqiang Zhang Xuan Bao Huili Geng Guohui Zhang Mitsuo Gen School of Artificial Intelligence and Big Data Henan University of Technology China College of Information Science and Engineering Henan University of Technology China School of Management Engineering Zhengzhou University of Aeronautics China Fuzzy Logic Systems Institute/Tokyo University of Science Japan

Flexible Job-shop Scheduling Problem (FJSP) is crucial for efficient and flexible automated production. Recent advancements in Reinforcement Learning (RL) have shown promise in solving sequential decision problems. However, most Deep Reinforcement Learning (DRL) algorithms rely on Priority Dispatching Rules (PDRs), which limits scheduling efficiency. This paper proposed a novel Graph Neural Network and Expert-Guided Deep Reinforcement Learning (GNN-EGDRL) framework that employs Graph Neural Network (GNN) to extract features from both machines and operations, thereby integrating operation selection and machine assignment into a unified composite decision-making process. Expert solutions generated by PDRs guide the initial stages of training, allowing agents to gradually transition to self-directed action selection, thus balancing exploration and exploitation. The expert guidance strategy emphasizes the relationships between operations and machines, enhancing the model’s sensitivity and providing granular guidance for feature extraction, leading to optimized solutions. Extensive experiments demonstrate that the proposed GNN-EGDRL method consistently outperforms traditional PDRs and other end-to-end DRL over all problem instances. Notably, this superiority has been validated in a wide range of scenarios, including larger-scale examples, underscoring the method’s scalability and robustness. Furthermore, the method exhibits strong performance in scenarios not encountered during training, highlighting its effectiveness and adaptability in diverse production environments.

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data augmentation in microscopic images for material data mining

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npj Computational Materials 2020年第1期6卷 601-609页

作者： Boyuan Ma Xiaoyan Wei Chuni Liu Xiaojuan Ban Haiyou Huang Hao Wang Weihua Xue Stephen Wu Mingfei Gao Qing Shen Michele Mukeshimana Adnan Omer Abuassba Haokai Shen Yanjing Su Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijing 100083China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing 100083China Institute for Advanced Materials and Technology University of Science and Technology BeijingBeijing 100083China School of Materials Science and Engineering University of Science and Technology BeijingBeijing 100083China School of Materials Science and Technology Liaoning Technical UniversityLiaoning 114051China The Institute of Statistical Mathematics Research Organization of Information and SystemsTachikawaTokyo 190-8562Japan National Intellectual Property Administration Beijing 100088China Faculty of Engineering Sciences University of BurundiBujumburaBurundi Faculty of Engineering and Technology Palestine Technical University–KadoorieTulkaremPalestine College of Information Science and Engineering China University of PetroleumBeijingChina Key Lab of Petroleum Data Mining China University of PetroleumBeijingChina

Recent progress in material data mining has been driven by high-capacity models trained on large ***,collecting experimental data(real data)has been extremely costly owing to the amount of human effort and expertise ***,we develop a novel transfer learning strategy to address problems of small or insufficient *** strategy realizes the fusion of real and simulated data and the augmentation of training data in a data mining *** a specific task of grain instance image segmentation,this strategy aims to generate synthetic data by fusing the images obtained from simulating the physical mechanism of grain formation and the“image style”information in real *** results show that the model trained with the acquired synthetic data and only 35%of the real data can already achieve competitive segmentation performance of a model trained on all of the real *** the time required to perform grain simulation and to generate synthetic data are almost negligible as compared to the effort for obtaining real data,our proposed strategy is able to exploit the strong prediction power of deep learning without significantly increasing the experimental burden of training data preparation.

关键词： mining instance competitive

A Tent Lévy Flying Sparrow Search Algorithm for Feature Selection: A COVID-19 Case Study

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arXiv 2022年

作者： Yang, Qinwen Gao, Yuelin Song, Yanjie School of Computer Science and Engineering North Minzu University Yinchuan750021 China School of Mathematics and Information Science North Minzu University Yinchuan750021 China Ningxia Key Laboratory of Intelligent Information and Big Data Processing Yinchuan750021 China Ningxia Province Cooperative Innovation Center of Scientific Computing and Intelligent in Formation Processing North Minzu University Yinchuan750021 China College of Systems Engineering National University of Defense Technology Changsha410073 China

The "Curse of Dimensionality" induced by the rapid development of information science, might have a negative impact when dealing with big datasets. In this paper, we propose a variant of the sparrow search algorithm (SSA), called Tent Lévy flying sparrow search algorithm (TFSSA), and use it to select the best subset of features in the packing pattern for classification purposes. SSA is a recently proposed algorithm that has not been systematically applied to feature selection problems. After verification by the CEC2020 benchmark function, TFSSA is used to select the best feature combination to maximize classification accuracy and minimize the number of selected features. The proposed TFSSA is compared with nine algorithms in the literature. Nine evaluation metrics are used to properly evaluate and compare the performance of these algorithms on twenty-one datasets from the UCI repository. Furthermore, the approach is applied to the coronavirus disease (COVID-19) dataset, yielding the best average classification accuracy and the average number of feature selections, respectively, of 93.47% and 2.1. Experimental results confirm the advantages of the proposed algorithm in improving classification accuracy and reducing the number of selected features compared to other wrapper-based algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： COVID-19

Women in ISIS Propaganda: A Natural Language Processing Analysis of Topics and Emotions in a Comparison with a Mainstream Religious Group

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Women in ISIS Propaganda: A Natural Language Processing Anal...

science and information Conference, SAI 2020

作者： Heidarysafa, Mojtaba Kowsari, Kamran Odukoya, Tolu Potter, Philip Barnes, Laura E. Brown, Donald E. Department of Systems and Information Engineering University of Virginia CharlottesvilleVA United States School of Data Science University of Virginia CharlottesvilleVA United States Department of Politics University of Virginia CharlottesvilleVA United States

ISBN: (纸本)9783030522452

Online propaganda is central to the recruitment strategies of extremist groups and in recent years these efforts have increasingly extended to women. To investigate Islamic State’s approach to targeting women in their online propaganda and uncover implications for counterterrorism, we rely on text mining and natural language processing (NLP). Specifically, we extract articles published in Dabiq and Rumiyah (Islamic State’s online English language publications) to identify prominent topics. To identify similarities or differences between these texts and those produced by non-violent religious groups, we extend the analysis to articles from a Catholic forum dedicated to women. We also perform an emotional analysis of both of these resources to better understand the emotional components of propaganda. We rely on Depechemood (a lexical-base emotion analysis method) to detect emotions most likely to be evoked in readers of these materials. The findings indicate that the emotional appeal of ISIS and Catholic materials are similar. © 2020, Springer Nature Switzerland AG.

关键词： Natural language processing systems

A high-frequency mobility big-data reveals how COVID-19 spread across professions, locations and age groups

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arXiv 2022年

作者： Zhao, Chen Zhang, Jialu Hou, Xiaoyue Yeung, Chi Ho Zeng, An College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Key Laboratory of Network and Information Security Shijiazhuang050024 China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Shijiazhuang050024 China Department of Science and Environmental Studies The Education University of Hong Kong Hong Kong School of Systems Science Beijing Normal University Beijing100875 China

As infected and vaccinated population increases, some countries decided not to impose non-pharmaceutical intervention measures anymore and to coexist with COVID-19. However, we do not have a comprehensive understanding of its consequence, especially for China where most population has not been infected and most Omicron transmissions are silent. This paper serves as the first study to reveal the complete silent transmission dynamics of COVID-19 overlaying a big data of more than 0.7 million real individual mobility tracks without any intervention measures throughout a week in a Chinese city, with an extent of completeness and realism not attained in existing studies. Together with the empirically inferred transmission rate of COVID-19, we find surprisingly that with only 70 citizens to be infected initially, 0.33 million becomes infected silently at last. We also reveal a characteristic daily periodic pattern of the transmission dynamics, with peaks in mornings and afternoons. In addition, retailing, catering and hotel staff are more likely to get infected than other professions. Unlike all other age groups and professions, elderly and retirees are more likely to get infected at home than outside home. Copyright © 2022, The Authors. All rights reserved.

关键词： COVID-19

Communication Experiment of Iridium Satellite and Super Pressure Balloon over Antarctica

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Communication Experiment of Iridium Satellite and Super Pres...

Asia-Pacific Conference on Microwave

作者： Takana Kaho Yoshitaka Saito Yoshihiro Tomikawa Faculty of Engineering Shonan Institute of Technology Fujisawa Kanagawa Japan Institute of Space and Astronautical Science Japan Aerospace Exploration Agency Sagamihara City Kanagawa Japan National Institute of Polar Research Tokyo Japan Department of Polar Science The Graduate University for Advanced Studies SOKENDAI Tokyo Japan Polar Environment Data Science Center Research Organization of Information and Systems Tokyo Japan

ISBN: (数字)9784902339567

ISBN: (纸本)9781665451086

An experiment (LODEWAVE) was conducted from January to February 2022 to measure the atmospheric gravity wave over the Antarctica using a super pressure balloon and the PANSY radar located at Syowa Station in the Antarctica. The short burst communication of the Iridium satellite was used for communication with the balloon. In this paper, we report the experimental results on the error of the position information of the balloon and the communication quality due to the use of Iridium over Antarctica.

关键词： Satellites Thermometers Atmospheric measurements Spaceborne radar Redundancy Extraterrestrial measurements Antarctica

Exploring the Potential of Large Language Models for Estimating the Reading Comprehension Question Difficulty

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arXiv 2025年

作者： Jain, Yoshee Hollander, John He, Amber Tang, Sunny Zhang, Liang Sabatini, John Siebel School of Computing and Data Science University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Psychology and Counseling Arkansas State University JonesboroAR72401 United States School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Heinz College of Information Systems and Public Policy Carnegie Mellon University PittsburghPA15213 United States Department of Electrical and Computer Engineering University of Memphis MemphisTN38152 United States Institute for Intelligent Systems University of Memphis MemphisTN38152 United States Department of Psychology University of Memphis MemphisTN38152 United States

Reading comprehension is a key for individual success, yet the assessment of question difficulty remains challenging due to the extensive human annotation and large-scale testing required by traditional methods such as linguistic analysis and Item Response Theory (IRT). While these robust approaches provide valuable insights, their scalability is limited. There is potential for Large Language Models (LLMs) to automate question difficulty estimation;however, this area remains underexplored. Our study investigates the effectiveness of LLMs, specifically OpenAI’s GPT-4o and o1, in estimating the difficulty of reading comprehension questions using the Study Aid and Reading Assessment (SARA) dataset. We evaluated both the accuracy of the models in answering comprehension questions and their ability to classify difficulty levels as defined by IRT. The results indicate that, while the models yield difficulty estimates that align meaningfully with derived IRT parameters, there are notable differences in their sensitivity to extreme item characteristics. These findings suggest that LLMs can serve as the scalable method for automated difficulty assessment, particularly in dynamic interactions between learners and Adaptive Instructional systems (AIS), bridging the gap between traditional psychometric techniques and modern AIS for reading comprehension and paving the way for more adaptive and personalized educational assessments. Copyright © 2025, The Authors. All rights reserved.

关键词： Question answering