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A Note on the Conditional Fault-Tolerant Strong Menger Edge Connectivity of Regular Graphs∗

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SSRN

SSRN 2023年

作者： Li, Pingshan Xu, Min Cheng, Eddie Key Laboratory of Intelligent Computing & Information Processing Ministry of Education School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China Laboratory of Mathematics and Complex Systems Ministry of Education Beijing100875 China Department of Mathematics and Statistics Oakland University RochesterMI48309 United States

A graph G is said to be strongly Menger edge-connected (SM-λ for short) if for every two of its vertices u and v, there are min{dG(u),dG(v)}) edge-disjoint paths between them, where dG(u) is the degree of u in G. The maximum conditional edge-fault-tolerant number of order r with respect to the SM-λ property of G, denoted by smrλ(G), is the maximum integer m such that G − F remains SM-λ for every edge set F with |F| ≤ m and δ(G − F) ≥ r. So far, most of the exact values of smrλ(G) are for the case when r ≤ 2 for special class of graphs. In this paper, we give a sufficient condition to give a bound of smrλ(G) for general r on regular graphs. As an application,we determine the exact value of) for 1 ≤ r ≤ n − 2 where AQn is the n-dimensional augmented cube. © 2023, The Authors. All rights reserved.

关键词： Graphic methods

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Triple Point Masking

IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Liu, Jiaming Kong, Linghe Wu, Yue Gong, Maoguo Li, Hao Miao, Qiguang Ma, Wenping Qin, Can The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Technology Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China The School of Electronic Engineering Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China The College of Mathematics Science Inner Mongolia Normal University Hohhot010022 China The School of Artificial Intelligence Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi’an710071 China The Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

Existing 3D mask learning methods encounter performance bottlenecks under limited data, and our objective is to overcome this limitation. In this paper, we introduce a triple point masking scheme, named TPM, which serves as a scalable plug- and-play framework for MAE pre-training to achieve multi-mask learning for 3D point clouds. Specifically, we augment the baseline methods with two additional mask choices (i.e., medium mask and low mask) as our core insight is that the recovery process of an object can manifest in diverse ways. Previous high-masking schemes focus on capturing the global representation information but lack fine-grained recovery capabilities, so that the generated pre-training weights tend to play a limited role in the fine-tuning process. With the support of the proposed TPM, current methods can exhibit more flexible and accurate completion capabilities, enabling the potential autoencoder in the pre-training stage to consider multiple representations of a single 3D point cloud object. In addition, during the fine-tuning stage, an SVM-guided weight selection module is proposed to fill the encoder parameters for downstream networks with the optimal weight, maximizing linear accuracy and facilitating the acquisition of intricate representations for new objects. Extensive experimental results and theoretical analysis show that five baselines equipped with the proposed TPM achieve comprehensive performance improvements on various downstream tasks. © 2025 IEEE. All rights reserved.

关键词： 3D modeling

Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method

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arXiv 2024年

作者： Liu, Zili Chen, Hao Bai, Lei Li, Wenyuan Chen, Keyan Wang, Zhengyi Ouyang, Wanli Zou, Zhengxia Shi, Zhenwei The Image Processing Center School of Astronautics The Beijing Key Laboratory of Digital Media The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China The Shanghai Artificial Intelligence Laboratory Shanghai200232 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China The Department of Geography University of Hong Kong Hong Kong The School of Oceanography Shanghai Jiao Tong University Shanghai200030 China The Department of Guidance Navigation and Control School of Astronautics Beihang University Beijing100191 China

Downscaling (DS) of meteorological variables involves obtaining high-resolution states from low-resolution meteorological fields and is an important task in weather forecasting. Previous methods based on deep learning treat downscaling as a super-resolution task in computer vision and utilize high-resolution gridded meteorological fields as supervision to improve resolution at specific grid scales. However, this approach has struggled to align with the continuous distribution characteristics of meteorological fields, leading to an inherent systematic bias between the downscaled results and the actual observations at meteorological stations. In this paper, we extend meteorological downscaling to arbitrary scattered station scales, establish a brand new benchmark and dataset, and retrieve meteorological states at any given station location from a coarse-resolution meteorological field. Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors. Building on this foundation, we propose a new downscaling model based on hypernetwork architecture, namely HyperDS, which efficiently integrates different observational information into the model training, achieving continuous scale modeling of the meteorological field. Through extensive experiments, our proposed method outperforms other specially designed baseline models on multiple surface variables. Notably, the mean squared error (MSE) for wind speed and surface pressure improved by 67% and 19.5% compared to other methods. We will release the dataset and code subsequently. Copyright © 2024, The Authors. All rights reserved.

关键词： Weather forecasting

Investigation of Biaxial Prestrain Influence on Bending-Under-Tension Springback 14th

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Investigation of Biaxial Prestrain Influence on Bending-Unde...

14th International Conference on Technology of Plasticity, ICTP 2023

作者： Yang, Y. Baudouin, C. Chalal, H. Vincze, G. Balan, T. State Key Lab of Solidification Processing School of Materials Science and Engineering Northwestern Polytechnical University Xi’an710072 China Arts et Metiers Institute of Technology Université de Lorraine LCFC Metz57000 France Center for Mechanical Technology and Automation Department of Mechanical Engineering University of Aveiro Campus Universitário de Santiago Aveiro3810-193 Portugal LASI – Intelligent Systems Associate Laboratory Guimarães4800-058 Portugal Arts et Metiers Institute of Technology Université de Lorraine LEM3 Metz57000 France

ISBN: (纸本)9783031409196

Springback notoriously depends on the strain-path history of the sheet material during forming. The bending-under-tension (BUT) test is used in this work for a discriminant characterization of springback in various conditions. In order to generate non-linear strain-paths, tensile prestrain is usually applied in the literature. In this work, an original biaxial prestrain device was designed and build. It allowed for the uniform prestrain of large samples of 500 mm diameter in DP600, up to 9.5% of strain. Subsequent samples for bending-under-tension were cut and tested at different combinations of tool radius and backforce. Classical models were used to describe the material’s behavior within finite element simulations of the BUT test. These experiments allowed for a discriminant comparison between the springback predictions of competing constitutive models. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Bending-Under-Tension Biaxial prestrain DP600 Springback

Remote Sensing Temporal Vision-Language Models: A Comprehensive Survey

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arXiv 2024年

作者： Liu, Chenyang Zhang, Jiafan Chen, Keyan Wang, Man Zou, Zhengxia Shi, Zhenwei The Image Processing Center School of Astronautics The Beijing Key Laboratory of Digital Media The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China The Shanghai Artificial Intelligence Laboratory Shanghai200232 China Shen Yuan Honors College of Beihang University Beijing100191 China The Department of Guidance Navigation and Control School of Astronautics Beihang University Beijing100191 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China

Temporal image analysis in remote sensing has traditionally centered on change detection, which identifies regions of change between images captured at different times. However, change detection remains limited by its focus on visual-level interpretation, often lacking contextual or descriptive information. The rise of Vision-Language Models (VLMs) has introduced a new dimension to remote sensing temporal image analysis by integrating visual information with natural language, creating an avenue for advanced interpretation of temporal image changes. Remote Sensing Temporal VLMs (RSTVLMs) allow for dynamic interactions, generating descriptive captions, answering questions, and providing a richer semantic understanding of temporal images. This temporal vision-language capability is particularly valuable for complex remote sensing applications, where higher-level insights are crucial. This paper comprehensively reviews the progress of RSTVLM research, with a focus on the latest VLM applications for temporal image analysis. We categorize and discuss core methodologies, datasets, and metrics, highlight recent advances in temporal vision-language tasks, and outline key challenges and future directions for research in this emerging field. This survey fills a critical gap in the literature by providing an integrated overview of RSTVLM, offering a foundation for further advancements in remote sensing temporal image understanding. We will keep tracing related works at https://***/Chen-Yang-Liu/Awesome-RS-Temporal-VLM. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

Transition in collective motion decision making

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Physical Review E 2022年第1期106卷 014611-014611页

作者： Biao Wang Yue Wu Guangwei Wang Lan Liu Guanrong Chen Hai-Tao Zhang School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan 430074 China National Engineering Laboratory for Educational Big Data Central China Normal University Wuhan 430079 China Department of Electrical Engineering City University of Hong Kong Hong Kong SAR China

Collective decision making in a biological motion group requires fast and robust transmission of information. Typically, directional switching information propagation across the whole group obeys a linear dispersion law. However, conventional dynamic collective motion models, such as the Vicsek model and the Couzin model did not take into account ultrafast directional synchronous motions. In the present paper, a multiparticle model is proposed based on inertial spin self-propel action, which can provide adequate description of such group motion. By considering both spin mechanism and collision avoidance, the proposed self-propelled particle spin model can nicely describe collective motion with fast directional switching. By analyzing the order parameter of the group-velocity synchronization, a mechanism of group decision making is revealed, which is based on the difference between two clusters of divergent leaders, showing a transition from the compromising phase (i.e., following the group average) to the preferred phase (i.e., aligning to a leader cluster). The finding provides new insight to the decision-making process of followers when they face with divergent leaders in group motion.

关键词： Collective behavior in networks Network phase transitions Pattern formation Self-organized systems

Dense Pixel-to-Pixel Harmonization via Continuous image Representation

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arXiv 2023年

作者： Chen, Jianqi Zhang, Yilan Zou, Zhengxia Chen, Keyan Shi, Zhenwei Image Processing Center School of Astronautics Beihang University Beijing100191 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Department of Guidance Navigation and Control School of Astronautics Beihang University Beijing100191 China

High-resolution (HR) image harmonization is of great significance in real-world applications such as image synthesis and image editing. However, due to the high memory costs, existing dense pixel-to-pixel harmonization methods are mainly focusing on processing low-resolution (LR) images. Some recent works resort to combining with color-to-color transformations but are either limited to certain resolutions or heavily depend on hand-crafted image filters. In this work, we explore leveraging the implicit neural representation (INR) and propose a novel image Harmonization method based on Implicit neural Networks (HINet), which to the best of our knowledge, is the first dense pixel-to-pixel method applicable to HR images without any handcrafted filter design. Inspired by the Retinex theory, we decouple the MLPs into two parts to respectively capture the content and environment of composite images. A Low-Resolution image Prior (LRIP) network is designed to alleviate the Boundary Inconsistency problem, and we also propose new designs for the training and inference process. Extensive experiments have demonstrated the effectiveness of our method compared with state-of-the-art methods. Furthermore, some interesting and practical applications of the proposed method are explored. Our code is available at https://***/WindVChen/INR-Harmonization. Copyright © 2023, The Authors. All rights reserved.

关键词： Pixels

Selection of Cancer Biomarkers from Microarray Gene Expression Data Utilizing the Bi-Objective Optimization Method

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Selection of Cancer Biomarkers from Microarray Gene Expressi...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hanjing Jiang Jianqing Lin Huachao Zhu Yabing Huang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Computer Science Wuhan University Wuhan China Department of Pathology Renmin Hospital of Wuhan University Wuhan China

Cancer-associated biomarker genes play an indispensable role in the intricate tapestry of cancer development and manifestation. The expression of biomarkers in different types of tumor cells has beneficial implications for shedding light on the development of various cancers, guiding clinical diagnosis, and treatment. Microarray technology enables the expression levels of thousands of genes in samples to be sequenced simultaneously. However, sparse and high-dimensional microarray data present a formidable challenge in identifying biomarker genes. This study presents EREF-NSGA2, a novel method for cancer biomarker selection from microarray data, employing a hybrid gene selection approach. Firstly, the combination of the wrapper and embedded gene selection methods is proposed to filter the microarray data, which efficiently decreases the search space of the algorithm. After that, the improved NSGA-II algorithm is used to search the genes subset obtained from the previous step to reach the optimal subset of cancer biomarker genes. The proposed EREF-NSGA2 is compared with other reported methods on six cancer benchmark gene expression datasets. A detailed biological analysis is performed to analyze the relationship between the selected genes and the cancer data sets they belong to. To summarize, EREF-NSGA2 proves its effectiveness in selecting a feature subset comprising the fewest genes while maintaining the highest classification accuracy.

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Minimal-time Deadbeat Consensus and Individual Disagreement Degree Prediction for High-order Linear Multi-agent systems

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arXiv 2023年

作者： Hu, Fu-Long Zhang, Hai-Tao Xu, Bowen Hu, Zhe Ren, Wei School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University Xi’an710072 China Department of Electrical and Computer Engineering University of California Riverside92521 United States

In this paper, a Hankel matrix-based fully distributed algorithm is proposed to address a minimal-time deadbeat consensus prediction problem for discrete-time high-order multi-agent systems (MASs). Therein, each agent can predict the consensus value with the minimum number of observable historical outputs of its own. Accordingly, compared to most existing algorithms only yielding asymptotic convergence, the present method can attain deadbeat consensus instead. Moreover, based on the consensus value prediction, instant individual disagreement degree value of MASs can be calculated in advance as well. Sufficient conditions are derived to guarantee both the minimal-time deadbeat consensus and the instant individual disagreement degree prediction. Finally, both the effectiveness and superiority of the proposed deadbeat consensus algorithm are substantiated by numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi agent systems