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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Beining Yang Kai Wang Qingyun Sun Cheng Ji Xingcheng Fu Hao Tang Yang You Jianxin Li School of Computer Science and Engineering Beihang University and Advanced Innovation Center for Big Data and Brain Computing Beihang University National University of Singapore Carnegie Mellon University

Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have attracted increasing concerns. Existing graph condensation methods primarily focus on optimizing the feature matrices of condensed graphs while overlooking the impact of the structure information from the original graphs. To investigate the impact of the structure information, we conduct analysis from the spectral domain and empirically identify substantial Laplacian Energy Distribution (LED) shifts in previous works. Such shifts lead to poor performance in cross-architecture generalization and specific tasks, including anomaly detection and link prediction. In this paper, we propose a novel Structure-broadcasting Graph dataset Distillation (SGDD) scheme for broadcasting the original structure information to the generation of the synthetic one, which explicitly prevents overlooking the original structure information. Theoretically, the synthetic graphs by SGDD are expected to have smaller LED shifts than previous works, leading to superior performance in both cross-architecture settings and specific tasks. We validate the proposed SGDD across 9 datasets and achieve state-of-the-art results on all of them: for example, on the YelpChi dataset, our approach maintains 98.6% test accuracy of training on the original graph dataset with 1,000 times saving on the scale of the graph. Moreover, we empirically evaluate there exist 17.6% ~ 31.4% reductions in LED shift crossing 9 datasets. Extensive experiments and analysis verify the effectiveness and necessity of the proposed designs. The code is available in the https://***/RingBDStack/SGDD.

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Forecasting Hierarchical Time Series Using Non-Linear Mappings

SSRN

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SSRN 2024年

作者： Wickramasuriya, Shanika L. Bandara, Kasun Hewamalage, Hansika Perera, Maneesha Department of Econometrics and Business Statistics Monash University Melbourne Australia School of Computing and Information Systems Melbourne Centre for Data Science The University of Melbourne Melbourne Australia School of Computer Science & Engineering University of New South Wales Sydney Australia Department of Mechanical Engineering School of Electrical Mechanical and Infrastructure Engineering The University of Melbourne Melbourne Australia

Forecast reconciliation is a procedure for forecasting multivariate time series with linear aggregation constraints to ensure that forecasts satisfy the same set of constraints. Most state-of-the-art reconciliation methods reconcile the forecasts using linear mappings to project the base forecasts onto a coherent subspace. They also use assumptions such as the forecasts are unbiased and residuals are jointly covariance stationary. In this study, we propose a non-linear forecast reconciliation approach by using machine learning methods, relaxing the assumptions of traditional linear reconciliation approaches. We introduce a novel loss function incorporating non-linear mappings to obtain a set of coherent forecasts from the individual base forecasts. To obtain the weights of the non-linear mapping between the base forecasts, we train a feed-forward neural network with the proposed loss function using transformed fitted values of the base models as inputs to the network. We evaluate the proposed methodology against state-of-the-art linear reconciliation approaches on multiple benchmark datasets. The results indicate that the proposed method works well when there is a reasonable number of observations in comparison to the number of bottom-level and non-bottom level series. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Mixture Gaussian Distribution-Based Collaborative Reinforcement Learning for 3D UAV Localization Optimization Against Jamming Attacks

Mixture Gaussian Distribution-Based Collaborative Reinforcem...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhu, Yujiao Chen, Mingzhe Wang, Sihua Liu, Yuchen Li, Gaolei Yin, Changchuan Quek, Tony Q.S. Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Network Beijing100876 China Singapore University of Technology and Design 487372 Singapore University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States North Carolina State University Department of Computer Science RaleighNC27695 United States Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China

ISBN: (纸本)9798350351255

In this paper, the optimization of unmanned aerial vehicle (UAV) localization under jamming attacks is studied. In the considered network, a base station (BS) collaborates with an active UAV to localize a target UAV. During this positioning process, a jamming UAV transmits discontinuous signals to passive UAVs to interfere the distance information measurement. To localize the target UAV under jamming attacks, the BS jointly use two localization methods: 1) generative adversarial network (GAN)-based positioning method and 2) time difference of arrival (TDOA)-based positioning method. Since GAN-based positioning method cannot defense in a strong jamming signal while TDOA-based positioning method may consume more energy and sacrifice localization accuracy, the BS must select an appropriate positioning method (GAN-based or TDOA-based methods) and four distance measurement information of passive UAVs to estimate the position of the target UAV. This problem is formulated as an optimization problem whose goal is to minimize the positioning error between the estimated and the ground truth positions of the target UAV while considering jamming attacks and the trajectory of passive UAVs. To solve this problem, we propose a mixture Gaussian distribution model-based collaborative reinforcement learning (RL) method which enables the active UAV to determine its transmit power and trajectory, and enables the BS to select the most appropriate subsets of distance measurement information and the optimal positioning method according to the movement of passive UAVs and the unknown jamming attack pattern of the jamming UAV. Simulation results show the proposed method can reduce the positioning error of the target UAV by up to 36.5% compared to the method that does not consider the GAN-based positioning method. © 2024 IEEE.

关键词： Gaussian distribution

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Single Bridge Formation in Self-Organizing Particle Systems

arXiv

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arXiv 2024年

作者： Oh, Shunhao Briones, Joseph L. Calvert, Jacob Egan, Noah Randall, Dana Richa, Andréa W. School of Computer Science Georgia Institute of Technology AtlantaGA United States School of Computing and Augmented Intelligence Arizona State University TempeAZ United States Institute for Data Engineering and Science Georgia Institute of Technology United States

Local interactions of uncoordinated individuals produce the collective behaviors of many biological systems, inspiring much of the current research in programmable matter. A striking example is the spontaneous assembly of fire ants into "bridges" comprising their own bodies to traverse obstacles and reach sources of food. Experiments and simulations suggest that, remarkably, these ants always form one bridge-instead of multiple, competing bridges-despite a lack of central coordination. We argue that the reliable formation of a single bridge does not require sophistication on behalf of the individuals by provably reproducing this behavior in a self-organizing particle system. We show that the formation of a single bridge by the particles is a statistical inevitability of their preferences to move in a particular direction, such as toward a food source, and their preference for more neighbors. Two parameters, η and β, reflect the strengths of these preferences and determine the Gibbs stationary measure of the corresponding particle system's Markov chain dynamics. We show that a single bridge almost certainly forms when η and β are sufficiently large. Our proof introduces an auxiliary Markov chain, called an "occupancy chain," that captures only the significant, global changes to the system. Through the occupancy chain, we abstract away information about the motion of individual particles, but we gain a more direct means of analyzing their collective behavior. Such abstractions provide a promising new direction for understanding many other systems of programmable matter. Copyright © 2024, The Authors. All rights reserved.

关键词： Markov chains

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Cutting plane algorithms for nonlinear binary optimization

arXiv

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arXiv 2022年

作者： Bui, Hoa T. Lin, Qun Loxton, Ryan ARC Training Centre for Transforming Maintenance through Data Science School of Electrical Engineering Computing and Mathematical Sciences Curtin University Australia School of Electrical Engineering Computing and Mathematical Sciences Curtin University Australia

Current state-of-the-art methods for solving discrete optimization problems are usually restricted to convex settings. In this paper, we propose a general approach based on cutting planes for solving nonlinear, possibly nonconvex, binary optimization problems. We provide a rigorous convergence analysis that quantifies the number of iterations required under different conditions. This is different to most other work in discrete optimization where only finite convergence is proved. Moreover, using tools from variational analysis, we provide necessary and sufficient dual optimality *** Codes 90-08 © 2022, CC BY-NC-SA.

关键词： Combinatorial optimization

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Demand Response Scheme: Achieving Sustainability in Distributed data Processing 9

Demand Response Scheme: Achieving Sustainability in Distribu...

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9th IEEE International Conference on data Science in Cyberspace, DSC 2024

作者： Zhai, Xueying Peng, Yunfeng Guo, Xiuping Zhang, Liang Zhang, Wei University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Beijing University of Posts and Telecommunications School of Economics and Management Beijing China Guizhou Institute of Technology Guizhou Key Laboratory of Electric Power Big Data Guizhou China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

The rapid development of Internet of Things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (RESs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of RES generation and grid electricity prices provide favorable conditions for distributed data processing. Therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where RES is abundant or electricity prices are low, based on RES generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization. © 2024 IEEE.

关键词： Low emission

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Faithful Polarization Entanglement Distribution in the SeQUeNCe Quantum Network Simulator

Faithful Polarization Entanglement Distribution in the SeQUe...

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2024 Frontiers in Optics, FiO 2024

作者： Singal, Ansh Zang, Allen Ramesh, Anirudh Kolar, Alexander Chung, Joaquin Kettimuthu, Rajkumar Kumar, Prem Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States Pritzker School of Molecular Engineering University of Chicago 5801 S Ellis Ave ChicagoIL60637 United States Data Science and Learning Division Argonne National Laboratory 9700 S Cass Ave. LemontIL60439 United States

We simulate polarization entanglement distribution with the Fock state representation in the open-sourced quantum network simulator SeQUeNCe. Simulations are validated by comparing with experimental entanglement distribution results and unknown experimental parameters are estimated. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Polarization

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An Ensemble Convolutional Neural Network for Recognizing and Counting of Mango Pests using Superpixel Segmentation 10

An Ensemble Convolutional Neural Network for Recognizing and...

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10th International Conference on Information Systems and computing Technology, ISCTech 2022

作者： Yang, Fuqiang Choi, Yeonsung Yang, Fuping Xin, Ke Zhao, Xuechen School of Data and Computer Science Shandong Women's University Jinan China School of Computer Information and Communication Engineering Kunsan National University Kunsan Korea Republic of Shandong Labor Vocational and Technical College Network Center Jinan China College of Information Science and Engineering Binzhou University Binzhou China School of Computing National University of Defense Technology Changsha China

ISBN: (纸本)9798350332933

In the agricultural field., the problem of pests attacking crops has long plagued farmers. In order to detect and monitor the development of pests in time, compared with traditional methods, deep learning techniques that have gradually matured and become effective in recent years can be used. The success of deep learning technology in the field of computer vision in recent years can provide support for the solution of insect recognizes and counts in images under the complex background of agriculture. In recent studies, convolutional neural networks have improved the efficiency for the reorganization of insects in specific categories of crops. This paper suggested a method for recognizing and counting mango insects. In order to improve the accuracy and practicability of the model, this paper has done the following work: First of all, optimized and upgraded the A Large-Scale Benchmark dataset for Insect Pest Recognition IP102 dataset version. Some of the pictures were cleaned, and unclear pictures that were difficult to extract in the early insects were deleted. Secondly, this paper suggested the EnRDNet algorithm to ensemble the ResNet and DenseNet models that have outstanding performance in the field of insect recognition. Third, this paper proposes to add a layer of confidence to the trained model when used in actual scene prediction which named Smart-EnRDNet algorithm to determine whether there are insect objects in the picture, and then output the insect category if there is. Experiment shows the efficiency of the proposed algorithms. © 2022 IEEE.

关键词： Convolutional neural networks

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MG-Net: Learn to Customize QAOA with Circuit Depth Awareness

arXiv

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arXiv 2024年

作者： Qian, Yang Wang, Xinbiao Du, Yuxuan Luo, Yong Tao, Dacheng School of Computer Science Faculty of Engineering University of Sydney NSW2008 Australia Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Quantum Approximate Optimization Algorithm (QAOA) and its variants exhibit immense potential in tackling combinatorial optimization challenges. However, their practical realization confronts a dilemma: the requisite circuit depth for satisfactory performance is problem-specific and often exceeds the maximum capability of current quantum devices. To address this dilemma, here we first analyze the convergence behavior of QAOA, uncovering the origins of this dilemma and elucidating the intricate relationship between the employed mixer Hamiltonian, the specific problem at hand, and the permissible maximum circuit depth. Harnessing this understanding, we introduce the Mixer Generator Network (MG-Net), a unified deep learning framework adept at dynamically formulating optimal mixer Hamiltonians tailored to distinct tasks and circuit depths. Systematic simulations, encompassing Ising models and weighted Max-Cut instances with up to 64 qubits, substantiate our theoretical findings, highlighting MG-Net's superior performance in terms of both approximation ratio and efficiency. © 2024, CC BY.

关键词： Hamiltonians

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Bidirectional Weighted Co-Association-Based Clustering Ensemble Algorithm for High-Speed Train Delay Prediction 29th

Bidirectional Weighted Co-Association-Based Clustering Ensem...

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29th International Conference on Computational and Experimental engineering and Sciences, ICCES 2023

作者： Zhao, Yi Bai, Muchen Ni, Panpan Jiang, Yunan Feng, Xuan Zhang, Xiaobo School of Design SouthWest Jiaotong University Chengdu611756 China School of Computing and Artificial Intelligence SouthWest Jiaotong University Chengdu611756 China Sichuan Institute of Computer Sciences Chengdu610042 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China

ISBN: (纸本)9783031425141

In recent years, high-speed trains are one of the popular research topics in the field of transportation and have attracted much attention from scholars at home and abroad in view of their high efficiency and safety features. High-speed trains are susceptible to unexpected situations of delayed arrival due to the disturbance of external factors including wind and temperature during the trip. The delay of high-speed trains has a vital impact on people's work efficiency and living standards. To solve the problem of intelligent forecast of high-speed train delay, we design and implement a clustering ensemble algorithm based on text data of bullet train operation environment monitoring. We firstly combine the concepts of cosine similarity, Jaccard similarity and information entropy, then fuse a bidirectional weighting formula, and further propose a novel Clustering Ensemble algorithm based on Bidirectional Weighted Co-association (CE-BWC). This approach consists of an inter-cluster weighting formula used to obtain the implicit generalized information by weighting each cluster between clusters, and a reliability weighting formula transferring the cluster information to the data item itself to measure the correlation degree of the data group. Experiments prove that our CE-BWC has better prediction performance for high-speed train delay and outperforms the state-of-the-art benchmark algorithms. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Efficiency

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