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The Fitzpatrick-Neville-Type Algorithm for Multivariate Vector-Valued Osculatory Rational Interpolation

Journal of Systems Science & Complexity 2015年第1期28卷 222-242页

作者： XIA Peng ZHANG Shugong LEI Na KIM Zhangyong School of Mathematics Liaoning University Automated Reasoning and Cognition Key Laboratory of Chongqing School of Mathematics Key Laboratory of Symbolic Computation and Knowledge Engineering(Ministry of Education)Jilin University

In this paper,the authors first apply the Fitzpatrick algorithm to multivariate vectorvalued osculatory rational *** based on the Fitzpatrick algorithm and the properties of an Hermite interpolation basis,the authors present a Fitzpatrick-Neville-type algorithm for multivariate vector-valued osculatory rational *** may be used to compute the values of multivariate vector-valued osculatory rational interpolants at some points directly without computing the interpolation function explicitly.

关键词： Fitzpatrick algorithm GrSbner basis module vector-valued osculatory rational interpolation.

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Improve the quality of XML clustering by boosting theory

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Journal of Computational Information Systems 2013年第11期9卷 4247-4254页

作者： Liu, Zhaojun Li, Xiongfei Li, Wei Key Laboratory of Symbolic Computation and Knowledge Engineering for Ministry of Education Jilin University Changchun 130012 China

XML documents cluster analysis is a hot research topic. Researchers proposed a number of methods to cluster XML document collections. Boosting is successful well-known methods for improving the quality of clustering. This paper proposed a method named ICBQ which improve the clustering quality by boosting theory. The experiment runs on the both real dataset and synthetic dataset, we choose the Nierman's method, Dalamagas's method and Flesca's method to evaluate the ability of ICBQ. The experimental result shows that our approach is more precise than no boosting methods. © 2013 by Binary Information Press.

关键词： XML

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Verifying Diagnosability of Discrete Event System with Logical Formula

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Chinese Journal of Electronics 2020年第2期29卷 304-311页

作者： GENG Xuena OUYANG Dantong HAN Cheng College of Computer Science and Technology Changchun University of Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering for Ministry of Education Jilin University

Diagnosability is an important property in the field of fault diagnosis. In this paper, a novel approach based on logical formula is proposed to verify diagnosability of Discrete event systems(DESs). CNFFSM is defined to represent a new model for DES. Each transition in DES can be described as a clause. According to CNF-FSM, we construct a CNF-diagnoser. Based on the resolution principle and CNF-diagnoser, an algorithm is presented to test whether the failure events can be detected or not in a finite number of observable *** algorithm can be applied in both off-line diagnosis and on-line diagnosis. Experimental results show that our algorithm can solve the diagnosability problem efficiently.

关键词： Fault diagnosis Diagnosability Discrete event system Finite state machine Conjunctive normal form

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CLASCN： Candidate Network Selection for Efficient Top-κ keyword Queries over databases

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Journal of Computer Science & Technology 2007年第2期22卷 197-207页

作者：张俊彭朝晖王珊聂惠静 School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing 100872 China Computer Science and Technology College Dalian Maritime University Dalian 116026 China

keyword Search Over Relational databases （KSORD） enables casual or Web users easily access databases through free-form keyword queries. Improving the performance of KSORD systems is a critical issue in this area. In this paper, a new approach CLASCN （Classification, Learning And Selection of Candidate Network） is developed to efficiently perform top-κ keyword queries in schema-graph-based online KSORD systems. In this approach, the Candidate Networks （CNs） from trained keyword queries or executed user queries are classified and stored in the databases, and top-κ results from the CNs are learned for constructing CN Language Models （CNLMs）. The CNLMs are used to compute the similarity scores between a new user query and the CNs from the query. The CNs with relatively large similarity score, which are the most promising ones to produce top-κ results, will be selected and performed. Currently, CLASCN is only applicable for past queries and New All-keyword-Used （NAU） queries which are frequently submitted queries. Extensive experiments also show the efficiency and effectiveness of our CLASCN approach.

关键词： relational database keyword search top-κ query candidate network

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Air Pollution Monitoring By Integrating Local and Global Information in Self-Adaptive Multiscale Transform Domain

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IEEE Transactions on Multimedia 2025年

作者： Gu, Ke Liu, Yuchen Liu, Hongyan Liu, Bo Qiao, Junfei Lin, Weisi Zhang, Wenjun Beijing University of Technology School of Information Science and Technology Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Massey University School of Mathematical and Computational Sciences New Zealand Nanyang Technological University College of Computing and Data Science Singapore Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai China

As the deadliest form of pollution, air pollution had a prolonged severe damage to the human health and life safety of nearly 99% of the world's population. Facing to the problem that billions of tons of pollutants discharged into the air per year by industrial activities, this paper proposed a novel image-based air pollution monitor (IAPM) by incorporating local and global information in the self-adaptive multiscale transform domain, so as to achieve the timely and effective leakage detection of typical air pollutants from a single image. To be specific, this paper first developed a screen-shaped module according to two significant findings in visual neuroscience, which include the high sensitivity of human eyes to horizontal and vertical stimuli and the center-surround inhibition, by designing and fusing the square module, horizontal strip module and vertical strip module parallelly for simulating the behaviour of human eyes to extract local features. Second, the learnable weights and proportional mapping were applied to incorporate the screen-shaped module and lightweight vision transformer as backbone, towards more richly exploiting and fusing local and global information just as the way a brain perceives external stimuli. Third, a new self-adaptive multiscale transform domain method was devised based on two motivations from the visual characteristics of multiscale perception and the brain characteristics of self-adaptive domain transform to modify the backbone by using the operations of pooling and pointwise convolution. Extensive experiments implemented on the datasets of carbon particulate matters and ethylene leakage confirmed the superior monitoring performance of the proposed IAPM model beyond the state-of-the-art (SOTA) peers by an accuracy gain of about 4%. Furthermore, the proposed IAPM model only required 0.089 GFLOPs and 0.15 million model parameters, remarkably outperforming SOTA competitors in computational efficiency and storage resources. © 1999-2

关键词： Information leakage

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Delving into Invisible Semantics for Generalized One-shot Neural Human Rendering

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IEEE Transactions on Visualization and Computer Graphics 2025年

作者： Lin, Yihong Xu, Xuemiao Zhang, Huaidong Xu, Cheng Li, Weijie Xie, Yi Qin, Jing He, Shengfeng South China University of Technology Guangdong China Guangdong Engineering Center for Large Model and GenAI Technology State Key Laboratory of Subtropical Building and Urban Science Ministry of Education Key Laboratory of Big Data and Intelligent Robot Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information China South China University of Technology China Hong Kong Polytechnic University Centre for Smart Health Hong Kong CAS-Hong Kong Joint Laboratory for Multimodal Medical Molecular Imaging Hong Kong Singapore Management University School of Computing and Information Systems Singapore

Traditional human neural radiance fields often overlook crucial body semantics, resulting in ambiguous reconstructions, particularly in occluded regions. To address this problem, we propose the Super-Semantic Disentangled Neural Renderer (SSD-NeRF), which employs rich regional semantic priors to enhance human rendering accuracy. This approach initiates with a Visible-Invisible Semantic Propagation module, ensuring coherent semantic assignment to occluded parts based on visible body segments. Furthermore, a Region-Wise Texture Propagation module independently extends textures from visible to occluded areas within semantic regions, thereby avoiding irrelevant texture mixtures and preserving semantic consistency. Additionally, a view-aware curricular learning approach is integrated to bolster the model's robustness and output quality across different viewpoints. Extensive evaluations confirm that SSD-NeRF surpasses leading methods, particularly in generating quality and structurally semantic reconstructions of unseen or occluded views and poses. © 2025 IEEE.

关键词： Semantics

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MSPL: Multi-modal Statistical Prompt Learning for New Energy Equipment Defect Recognition

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IEEE Internet of Things Journal 2025年

作者： Wang, Zhenwei Wang, Pengfei Han, Guangjie Zhou, Chengjie Lin, Qing Zhang, Jianxin Fan, Guangjie Zhang, Qiang Dalian University of Technology School of Computer Science and Technology Dalian116024 China Dalian116024 China Shenyang Huayan Electric Technology Co Ltd Shenyang110180 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China Deputy Director of the Yantai Big Data Center and Senior Engineer Yantai264003 China Dalian Minzu University School of Computer Science and Engineering Dalian116600 China

Monitoring renewable energy devices is crucial for the timely detection of faults and the improvement of system stability, making it a key component of the Internet of Things (IoT) ecosystem. However, existing intelligent algorithms and IoT methods rely on edge devices with limited computational capacity, which requires balancing real-time performance and accuracy. Additionally, most methods require initial training on large-scale data using cloud platforms or high-performance servers, leading to high initial costs. To address these challenges, we propose Multi-modal Statistical Prompt Learning (MSPL) for new energy equipment defect recognition on IoT edge devices. This method enables rapid learning with only a few samples, avoiding the need for large-scale centralized training. Specifically, MSPL uses the pre-trained Contrastive Language-Image Pre-training model as its backbone, leveraging text-based conceptual information to enhance the understanding of visual inputs. A Statistical Query (SQ) module is implemented at the end of the backbone to extract distinctive features from the outputs, integrating these features with soft prompts to customize them for the defect recognition task. Since the learnable parameters are limited to soft prompts added at the end of the backbone, MSPL restricts gradient backpropagation to this point. This improves parameter and memory efficiency, making it more suitable for scenarios with limited computational capacity on IoT edge devices. Experimental results of MSPL on two renewable energy equipment defect datasets and edge devices indicate that it meets real-time processing requirements while maintaining high accuracy, outperforming other methods. © 2025 IEEE.

关键词： Zero-shot learning

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Are Your Apps Accessible? A GCN-based Accessibility Checker for Low Vision Users

arXiv

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arXiv 2025年

作者： Zhang, Mengxi Liu, Huaxiao Song, Shenning Chen, Chunyang Huang, Pei Zhao, Jian College of Computer Science and Technology Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Jilin Changchun130012 China Department of Computer Science Technical University of Munich Heilbronn85354 Germany Department of Computer Science Stanford University CA94305 United States College of Computer Science Changchun University Jilin Changchun130012 China

Context: Accessibility issues (e.g., small size and narrow interval) in mobile applications (apps) lead to obstacles for billions of low vision users in interacting with Graphical User Interfaces (GUIs). Although GUI accessibility scanning tools exist, most of them perform rule-based check relying on complex GUI hierarchies. This might make them detect invisible redundant information, cannot handle small deviations, omit similar components, and is hard to extend. Objective: In this paper, we propose a novel approach, named ALVIN (Accessibility Checker for Low Vision), which represents the GUI as a graph and adopts the Graph Convolutional Neural Networks (GCN) to label inaccessible components. Method: ALVIN removes invisible views to prevent detecting redundancy and uses annotations from low vision users to handle small deviations. Also, the GCN model could consider the relations between GUI components, connecting similar components and reducing the possibility of omission. ALVIN only requires users to annotate the relevant dataset when detecting new kinds of issues. Results: Our experiments on 48 apps demonstrate the effectiveness of ALVIN, with precision of 83.5%, recall of 78.9%, and F1-score of 81.2%, outperforming baseline methods. In RQ2, the usefulness is verified through 20 issues submitted to open-source apps. The RQ3 also illustrates the GCN model is better than other models. Conclusion: To summarize, our proposed approach can effectively detect accessibility issues in GUIs for low vision users, thereby guiding developers in fixing them efficiently. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A compensation method for the packet loss deviation in system identification with event-triggered binary-valued observations

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Science China(Information Sciences) 2020年第12期63卷 266-268页

作者： Jing-Dong DIAO Jin GUO Changyin SUN School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education School of Automation Southeast University Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education

Dear editor,With the rapid development of network and communication technology, group systems interrelated in terms of both time and space are commonly encountered, such as sensor networks, multi-agent systems, and sm... 详细信息

关键词： system identification event-triggered binary-valued observations packet loss convergence

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Organometallic-Inorganic Hybrid MXenes with Tunable Superconductivity

arXiv

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arXiv 2025年

作者： Fan, Qi Bo, Tao Guo, Wei Chen, Minghua Tang, Qing Yang, Yicong Li, Mian Chen, Ke Ge, Fangfang Li, Jialu Qiao, Sicong Wang, Changda Song, Li Yu, Lijing Guo, Jinghua Naguib, Michael Chai, Zhifang Huang, Qing Dun, Chaochao Kang, Ning Gogotsi, Yury Liang, Kun Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Key Laboratory of Special Energy Materials and Chemistry Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China University of Chinese Academy of Sciences 19 A Yuquan Rd Shijingshan District Beijing100049 China Qianwan Institute of CNITECH Ningbo315336 China Key Laboratory for the Physics and Chemistry of Nanodevices Department of Electronics Peking University Beijing100871 China Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Synchrotron Radiation Laboratory Key Laboratory of Precision and Intelligent Chemistry School of Nuclear Science and Technology University of Science and Technology of China Hefei230029 China Key Laboratory of Materials and Surface Technology Ministry of Education Sichuan Energy Equipment Intelligent Engineering Research Center School of Materials Science and Engineering Xihua University Chengdu610039 China Department of Physics and Engineering Physics Tulane University New OrleansLA70118 United States Department of Materials Science and Engineering A. J. Drexel Nanomaterials Institute Drexel University PhiladelphiaPA19104 United States

Ti-based two-dimensional transition-metal carbides (MXenes) have attracted attention due to their superior properties and are being explored across various applications1,2. Despite their versatile properties, superconductivity has never been demonstrated, not even predicted, for this important group of 2D materials. In this work, we have introduced an electrochemical intercalation protocol to construct versatile organometallic-inorganic hybrid MXenes and achieved tunable superconductivity in the metallocene-modified layered crystals. Through structural editing of MXene matrix at atomic scale and meticulously modulated intercalation route, Ti3C2Tx intercalated with metallocene species exhibits a superconductive transition temperature (Tc) of 10.2 K. Guest intercalation induced electron filling and strain engineering are responsible for the emerging superconductivity in this intrinsically non-superconducting material. Theoretically, simulated electron-phonon interaction effects further elucidate the nature of the changes in Tc. Furthermore, the Tc of crafted artificial superlattices beyond Ti-based MXenes have been predicted, offering a general strategy for engineering superconductivity and magnetism in layered hybrid materials. Copyright © 2025, The Authors. All rights reserved.

关键词： Electron-phonon interactions

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