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Solving Multivariate Polynomial Matrix Diophantine Equations with Gr?bner Basis Method

Journal of Systems Science & Complexity 2022年第1期35卷 413-426页

作者： XIAO Fanghui LU Dong WANG Dingkang College of Mathematics and Statistics Hunan Normal UniversityChangsha 410006China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China School of Mathematical Sciences Beihang UniversityBeijing 100191China. KLMM Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China

Different from previous viewpoints,multivariate polynomial matrix Diophantine equations are studied from the perspective of modules in this paper,that is,regarding the columns of matrices as elements in modules.A necessary and sufficient condition of the existence for the solution of equations is *** powerful features and theoretical foundation of Gr?bner bases for modules,the problem for determining and computing the solution of matrix Diophantine equations can be ***,the authors make use of the extension on modules for the GVW algorithm that is a signature-based Gr?bner basis algorithm as a powerful tool for the computation of Gr?bner basis for module and the representation coefficients problem directly related to the particular solution of *** a consequence,a complete algorithm for solving multivariate polynomial matrix Diophantine equations by the Gr?bner basis method is presented and has been implemented on the computer algebra system Maple.

关键词： Gr?bner basis matrix Diophantine equation module multivariate polynomial

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Non-salient region erasure for time series augmentation

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Frontiers of Computer Science 2022年第6期16卷 203-205页

作者： Pin LIU Xiaohui GUO Bin SHI Rui WANG Tianyu WO Xudong LIU State Key Lab of Software Development Environment(SKLSDE) Beihang UniversityBeijing100191China Beijing Advanced Innovation Center for Big Data and Brain Computing(BDBC) Beijing100191China Hangzhou Innovation Institute Beihang UniversityHangzhou310052China School of Computer Science and Technology Xi’an Jiaotong UniversityXi’an710049China

1 Introduction Time seriesaugmentationis an essential approachto solvethe overfitting problem on the time series classification(TSC)task[1,2].Although existing approaches perform better in mitigating this problem,none of them focus on protecting saliency regions on time *** key informative shapelets contained in these regions are the core basis for distinguishing categories(e.g.,upward spikes in ECG and high amplitude in Sensor).

关键词： Sensor series series

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Multiple Unmanned Armored Vehicle Formation Transform

Multiple Unmanned Armored Vehicle Formation Transform

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Chinese Intelligent Systems Conference, CISC 2020

作者： Huang, Miqi Tian, Daxin Duan, Xuting Beijing Advanced Innovation Center for Big Data and Brain Computing School of Transport Science and Engineering Beihang University Beijing China

ISBN: (纸本)9789811584572

The formation of armored vehicle formation achievement and transforming are important research contents of vehicle formation control. Especially for the situation of unmanned armored vehicle formation. This paper combines the virtual navigator method and the behavior-based bait-predator method, and applies it to the formation achievement and transforming control of four unmanned armored vehicle formations. The target position is used as a bait to realize flexible transforming between linear, triangular and diamond formations. This method can prevent the formation leader from losing control due to the incapacitation of the formation leader, and does not specify the fixed position of the vehicle in the formation, which greatly guarantees the stability of the formation and the flexibility of formation transforming. The simulation experiment also proves that the method has good effect on the formation and formation transforming of vehicle formation. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Armored vehicles

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Patching Weak Convolutional Neural Network Models through Modularization and Composition 22

Patching Weak Convolutional Neural Network Models through Mo...

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Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering

作者： Binhang Qi Hailong Sun Xiang Gao Hongyu Zhang SKLSDE Lab Beihang University China and Beijing Advanced Innovation Center for Big Data and Brain Computing China The University of Newcastle Australia

ISBN: (纸本)9781450394758

Despite great success in many applications, deep neural networks are not always robust in practice. For instance, a convolutional neuron network (CNN) model for classification tasks often performs unsatisfactorily in classifying some particular classes of objects. In this work, we are concerned with patching the weak part of a CNN model instead of improving it through the costly retraining of the entire model. Inspired by the fundamental concepts of modularization and composition in software engineering, we propose a compressed modularization approach, CNNSplitter, which decomposes a strong CNN model for N-class classification into N smaller CNN modules. Each module is a sub-model containing a part of the convolution kernels of the strong model. To patch a weak CNN model that performs unsatisfactorily on a target class (TC), we compose the weak CNN model with the corresponding module obtained from a strong CNN model. The ability of the weak CNN model to recognize the TC can thus be improved through patching. Moreover, the ability to recognize non-TCs is also improved, as the samples misclassified as TC could be classified as non-TCs correctly. Experimental results with two representative CNNs on three widely-used datasets show that the averaged improvement on the TC in terms of precision and recall are 12.54% and 2.14%, respectively. Moreover, patching improves the accuracy of non-TCs by 1.18%. The results demonstrate that CNNSplitter can patch a weak CNN model through modularization and composition, thus providing a new solution for developing robust CNN models.

关键词： Modularization and Composition DNN Patching CNN

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Noise-injected Consistency Training and Entropy-constrained Pseudo Labeling for Semi-supervised Extractive Summarization 29

Noise-injected Consistency Training and Entropy-constrained ...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Wang, Yiming Mao, Qianren Liu, Junnan Jiang, Weifeng Zhu, Hongdong Li, Jianxin Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China The State Key Laboratory of Software Development Environment Beihang University China Institute of Artificial Intelligence Beihang University China

Labeling large amounts of extractive summarization data is often prohibitive expensive due to time, financial, and expertise constraints, which poses great challenges to incorporating summarization system in practical applications. This limitation can be overcome by semi-supervised approaches: consistency-training and pseudo-labeling to make full use of unlabeled data. Researches on the two, however, are conducted independently, and very few works try to connect them. In this paper, we first use the noise-injected consistency training paradigm to regularize model predictions. Subsequently, we propose a novel entropy-constrained pseudo labeling strategy to obtain high-confidence labels from unlabeled predictions, which can obtain high-confidence labels from unlabeled predictions by comparing the entropy of supervised and unsupervised predictions. By combining consistency training and pseudo-labeling, this framework enforce a low-density separation between classes, which decently improves the performance of supervised learning over an insufficient labeled extractive summarization dataset. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Entropy

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Environment-aware dynamic graph learning for out-of-distribution generalization 23

Environment-aware dynamic graph learning for out-of-distribu...

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

作者： Haonan Yuan Qingyun Sun Xingcheng Fu Ziwei Zhang Cheng Ji Hao Peng Jianxin Li Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University and School of Computer Science and Engineering Beihang University Key Lab of Education Blockchain and Intelligent Technology Guangxi Normal University Department of Computer Science and Technology Tsinghua University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribution shifts, which are common in real-world scenarios. As the generation of dynamic graphs is heavily influenced by latent environments, investigating their impacts on the out-of-distribution (OOD) generalization is critical. However, it remains unexplored with the following two major challenges: (1) How to properly model and infer the complex environments on dynamic graphs with distribution shifts? (2) How to discover invariant patterns given inferred spatio-temporal environments? To solve these challenges, we propose a novel Environment-Aware dynamic Graph LEarning (EAGLE) framework for OOD generalization by modeling complex coupled environments and exploiting spatiotemporal invariant patterns. Specifically, we first design the environment-aware EADGNN to model environments by multi-channel environments disentangling. Then, we propose an environment instantiation mechanism for environment diversification with inferred distributions. Finally, we discriminate spatio-temporal invariant patterns for out-of-distribution prediction by the invariant pattern recognition mechanism and perform fine-grained causal interventions node-wisely with a mixture of instantiated environment samples. Experiments on real-world and synthetic dynamic graph datasets demonstrate the superiority of our method against state-of-the-art baselines under distribution shifts. To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective.

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Driving Capability, a Unified Driver Model for ADAS

Driving Capability, a Unified Driver Model for ADAS

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2021 International Conference on advanced Technologies and Applications of Modern Industry, ATAMI 2021

作者： Sun, Bohua Zhai, Yang Li, Yaxin Deng, Weiwen Zhao, Shuai State Key Laboratory of Automotive Simulation and Control Jilin University Changchun130022 China Co. Ltd. Tianjin China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

To allocate driving privilege in a reasonable way in shared control for intelligent vehicle, the study on driving capability, the unified driver model for ADAS in the longitudinal and lateral scenarios was proposed, which can improve the safety and comfort for intelligent vehicles as well. Driving capability is defined and analyzed and car-following stimulate in longitudinal scenario and moving double lane change stimulate in lateral scenario were designed. data collection was conducted in Driver-In-the-Loop Intelligent Simulation Platform (DILISP). Driving capability identification model was established basing on Hammerstein process and Principal Component Analysis (PCA) was used to decouple and reduce the dimension for the key parameters in Hammerstein identification model. The classification is done basing on the particle clustering algorithm and the evaluation equation for driving capability was calculated by Multiple Linear Regression (MLR). Results show that the proposed evaluation method for driving capability in the longitudinal and lateral scenarios can achieve accurate and reliable evaluation results. © Published under licence by IOP Publishing Ltd.

关键词： Principal component analysis

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data Acquisition and Monitoring Scheme for Satellite Network with Time-Varying Topology

Data Acquisition and Monitoring Scheme for Satellite Network...

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Satellite computing (Satellite), IEEE International Conference on

作者： Ying Zhang Tianyu Wo Guangjian Wang Tianyu Ye Jiwei Zhang Xinye Liu Xiao Feng Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University China Electronics Technology Taiji Group Corporation Limited Beijing China

As human beings further explore space, satellite systems have become an indispensable tool for human society. However, managing satellite networks, especially in edge cloud computing scenarios, presents unique challenges due to its time-varying nature. To address these challenges, we propose a novel data acquisition and monitoring scheme specifically designed for satellite network environments. Our scheme leverages eBPF (extended Berkeley Packet Filter) technology for real-time monitoring data collection and utilizes a spatial-temporal graph representation to capture the dynamic topology of the satellite network. The Max-Min Fair (MMF) algorithm is employed to compute efficient routing paths, while data preprocessing techniques are applied to alleviate data transmission pressure. The scheme is designed to support user-defined monitoring targets and enables active detection and monitoring of application processes based on specific requirements. To evaluate the effectiveness of our proposed scheme, we conduct a comparative analysis against the Shortest Path on the Spatial-Temporal graph (SPST) algorithm and the Energy-Aware Muitipath (EAMP) algorithm. Experimental results demonstrate the superiority of the MMF algorithm, exhibiting a significant 32.6% and 21.5% improvement in data transmission volume compared to the SPST and EAMP algorithms, respectively. Moreover, the utilization of the MMF algorithm reduces the average satellite bandwidth utilization by 37.1 % and 41.2% throughout the transmission cycle, surpassing the performance of the SPST and EAMP alogrithms.

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Optimization Strategies for data Placement in Satellite Cloud-oriented Distributed File Systems

Optimization Strategies for Data Placement in Satellite Clou...

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IEEE International Conference on Joint Cloud computing (JCC)

作者： Ying Zhang Tianyu Wo Kun Fan Tianyu Ye Jiwei Zhang Junhua Zhang Xiao Feng Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University China Electronics Technology Taiji Group Corporation Limited Beijing China

Forming a collaborative computing network among the deployed satellites in space can process data rapidly and reduce data transmission delay by leveraging the communication, storage, and computing capacities of the satellites. Due to the dynamics of satellite orbits, the distance between satellites varies over time, bringing new challenges to the designing of dedicated distributed file systems for collaborative satellites. Traditional distributed file systems did not consider the network topology and dynamics of satellite clouds and the limited computing resources of satellite node cores. In view of the torus network topology of satellite clouds, we design a highly available distributed metadata management mechanism for satellite clouds to ensure efficient metadata reading and reliability. On this basis, a topology-aware replica placement strategy is proposed to minimize communication costs and energy consumption for replica placement. Based on the metadata strategy and replica placement strategy mentioned above, we design and implement a distributed file system for satellite clouds. Experimental results demonstrate that our proposed placement strategy can improve the data transmission performance of the replica placement by more than double compared to the random placement strategy.

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Instructing the Architecture Search for Spatial-temporal Sequence Forecasting with LLM

arXiv

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arXiv 2025年

作者： Xue, Xin Zhou, Haoyi Chen, Tianyu Zhang, Shuai Long, Yizhou Li, Jianxin Department of Computer Science Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China Department of Software Beihang University Beijing100191 China Zhongguancun Laboratory Beijing100190 China

Spatial-temporal sequence forecasting (STSF) is a long-standing research problem with widespread real-world applications. Neural architecture search (NAS), which automates the neural network design, has been shown effective in tackling the STSF problem. However, the existing NAS methods for STSF focus on generating architectures in a time-consuming data-driven fashion, which heavily limits their ability to use background knowledge and explore the complicated search trajectory. Large language models (LLMs) have shown remarkable ability in decision-making with comprehensive internal world knowledge, but how it could benefit NAS for STSF remains unexplored. In this paper, we propose a novel NAS method for STSF based on LLM. Instead of directly generate architectures with LLM, We inspire the LLM’s capability with a multi-level enhancement mechanism. Specifically, on the step-level, we decompose the generation task into decision steps with powerful prompt engineering and inspire LLM to serve as instructor for architecture search based on its internal knowledge. On the instance-level, we utilize a one-step tuning framework to quickly evaluate the architecture instance and a memory bank to cumulate knowledge to improve LLM’s search ability. On the task-level, we propose a two-stage architecture search, balancing the exploration stage and optimization stage, to reduce the possibility of being trapped in local optima. Extensive experimental results demonstrate that our method can achieve competitive effectiveness with superior efficiency against existing NAS methods for STSF. © 2025, CC BY-NC-ND.

关键词： Spatio-temporal data

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