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arXiv 2024年

作者： Zhou, Tiejun Zhang, Lei Zhang, Pingwen Shi, An-Chang Jiang, Kai Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing Ministry of Education School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China Beijing International Center for Mathematical Research Peking University Beijing100871 China School of Mathematics and Statistics Wuhan University Wuhan430072 China School of Mathematical Sciences Peking University Beijing100871 China Department of Physics and Astronomy McMaster University HamiltonL8S 4M1 Canada

In this work, we study the nucleation of quasicrystals from liquid or periodic crystals by developing an efficient order-order phase transition algorithm, namely the nullspace-preserving saddle search method. Specifically, we focus on nucleation and phase transitions of the decagonal quasicrystal (DQC) based on the Lifshitz-Petrich model. We present the nucleation path of DQC from the liquid and demonstrate one- and two-stage transition paths between DQC and periodic crystals. We provide a perspective of the group-subgroup phase transition and nucleation rates to understand the nucleation and phase transition mechanisms involving DQC. These results reveal the one-step and stepwise modes of symmetry breaking or recovery in the phase transition from DQC, where the stepwise modes are more probable. Copyright © 2024, The Authors. All rights reserved.

关键词： Quasicrystals

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A Progressive Image Restoration Network for High-order Degradation Imaging in Remote Sensing

arXiv

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arXiv 2024年

作者： Feng, Yujie Yang, Yin Fan, Xiaohong Zhang, Zhengpeng Bu, Lijing Zhang, Jianping School of Mathematics and Computational Science Xiangtan University Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory for Intelligent Computing and Information Processing of the Ministry of Education Xiangtan411105 China School of Mathematics and Computational Science Xiangtan University National Center for Applied Mathematics in Hunan Hunan International Scientific and Technological Innovation Cooperation Base of Computational Science Xiangtan411105 China College of Mathematical Medicine Zhejiang Normal University Jinhua321004 China School of Automation and Electronic Information Xiangtan University Xiangtan411105 China

Recently, deep learning methods have gained remarkable achievements in the field of image restoration for remote sensing (RS). However, most existing RS image restoration methods focus mainly on conventional first-order degradation models, which may not effectively capture the imaging mechanisms of remote sensing images. Furthermore, many RS image restoration approaches that use deep learning are often criticized for their lacks of architecture transparency and model interpretability. To address these problems, we propose a novel progressive restoration network for high-order degradation imaging (HDI-PRNet), to progressively restore different image degradation. HDI-PRNet is developed based on the theoretical framework of degradation imaging, offering the benefit of mathematical interpretability within the unfolding network. The framework is composed of three main components: a module for image denoising that relies on proximal mapping prior learning, a module for image deblurring that integrates Neumann series expansion with dual-domain degradation learning, and a module for super-resolution. Extensive experiments demonstrate that our method achieves superior performance on both synthetic and real remote sensing images. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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Two weight Zpk -codes, p odd prime

arXiv

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arXiv 2017年

作者： Shi, Minjia Sepasdar, Zahra Solé, Patrick Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University No. 3 Feixi Road Hefei Anhui Province230039 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China School of Mathematical Sciences of Anhui University Hefei230601 China Department of Pure Mathematics Ferdowsi University of Mashhad P. O. Box 1159 Mashhad91775 Iran CNRS/LTCI Telecom ParisTech University of Paris-Saclay Paris75 013 France Department of Mathematics Faculty of Science King Abdulaziz University Jeddah21589 Saudi Arabia

We show that regular homogeneous two-weight Zpk -codes where p is odd and k ≥ 2 with dual Hamming distance at least four do not exist. The proof relies on existence conditions for the strongly regular graph built on ... 详细信息

关键词： Hamming distance

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Timing-Driven High-Level Synthesis for Continuous-Flow Microfluidic Biochips

Timing-Driven High-Level Synthesis for Continuous-Flow Micro...

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IEEE International Symposium on Quality Electronic Design

作者： Zhengyang Ye Zhisheng Chen Youlin Pan Genggeng Liu Wenzhong Guo Tsung-Yi Ho Xing Huang College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China School of Informatics Xiamen University Xiamen China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China School of Computer Science Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798350309270

ISBN: (纸本)9798350309287

As continuous-flow microfluidic biochips technology rapidly advances, its automated synthesis methods require adapting to increasingly complex and stringent biochemical applications. In particular, with the introduction of time-sensitive bioassays, in this work, we present a timing-driven high-level synthesis method to optimize the resource efficiency of bioassays considering the time constraints. Specifically, we investigate the existing forms of time constraints and propose a corresponding flexible modeling approach based on reaction-control which can achieve downward compatibility for physical design. Additionally, we also analyze the impact of storage and wash on time constraints. The experimental results show that our method achieves efficient execution and low devices cost while satisfying time constraints.

关键词： Costs Biochips Time factors Microfluidics Physical design

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Co-training based prediction of multi-label protein–protein interactions

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Computers in Biology and Medicine 2024年 177卷 108623-108623页

作者： Tang, Tao Zhang, Xiaocai Li, Weizhuo Wang, Qing Liu, Yuansheng Cao, Xiaofeng School of Modern Posts Nanjing University of Posts and Telecommunications 9 Wenyuan Rd Jiangsu Nanjing210023 China 1 Fusionopolis Way Singapore138632 Singapore School of Management Nanjing University of Posts and Telecommunications 9 Wenyuan Rd Jiangsu Nanjing210023 China College of Computer Science and Electronic Engineering Hunan University 2 Lushan Rd Hunan Changsha410086 China Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education Anhui University 111 Jiulong Road Anhui Hefei230601 China School of Artificial Intelligence Jilin University 2699 Qianjin St Changchun Jilin130012 China

Prediction of protein–protein interaction (PPI) types enhances the comprehension of the underlying structural characteristics and functions of proteins, which gives rise to a multi-label classification problem. The nominal features describe the physicochemical characteristics of proteins directly, establishing a more robust correlation with the interaction types between proteins than ordered features. Motivated by this, we propose a multi-label PPI prediction model referred to as CoMPPI (Co-training based Multi-Label prediction of Protein–Protein Interaction). This approach aims to maximize the utility of both ordered and nominal features extracted from protein sequences. Specifically, CoMPPI incorporates graph convolutional network (GCN) and 1D convolution operation to process the complementary subsets of features individually, leveraging both local and contextualized information in a more efficient way. In addition, two multi-type PPI datasets were constructed to eliminate the duplication in previous datasets. We compare the performance of CoMPPI with three state-of-the-art methods on three datasets partitioned using distinct schemes (Breadth-first search, Depth-first search, and Random), CoMPPI consistently outperforms the other methods across all cases, demonstrating improvements ranging from 3.81% to 32.40% in Micro-F1. The subsequent ablation experiment confirms the efficacy of employing the co-training framework for multi-label PPI prediction, indicating promising avenues for future advancements in this domain. © 2024 Elsevier Ltd

关键词： Proteins

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Optimal combination of conflicting evidence based on genetic algorithm

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ICIC Express Letters 2011年第4 A期5卷 1145-1150页

作者： Jiang, Wen Peng, Jinye Deng, Yong School of Electronics and Information Northwestern Polytechnical University No. 127 Youyixi Road Xi'an 710072 China Hangzhou Key Lab of E-Business and Information Security Hangzhou Normal University No. 16 Xuelin Street Xiasha Higher Education Zone Hanzhou 310036 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan University Xiangtan 411105 China School of Computer and Information Sciences Southwest University No. 2 Tiansheng Road Beibei District Chongqing 400715 China School of Electronics and Information Technology Shanghai Jiao Tong University No. 800 Dongchuan Road Shanghai 200240 China

The Dempster-Shafer (D-S) evidence theory is widely used in many fields of information fusion. However, counter-intuitive results may be obtained by the classical Dempster combination rule when collected evidences are highly in conflict with each other. So an optimal weighted average information fusion method is proposed. Firstly, the global distance of the weighted evidence is presented based on the distance function. Secondly, the optimal weight of every evidence body was acquired by genetic algorithm, while the global distance is the minimum. Finally, the weighted average of the masses is combined by classical Dempster's rule. Compared with existing methods, the proposed optimal method can effectively handle conflicting evidence with better performance of convergence. Numerical examples are illustrated the effectiveness of the proposed method. ICIC International © 2011.

关键词： Genetic algorithms

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Determination of relative significance factor of impact categories based on fuzzy multiattribute decision-making method

Determination of relative significance factor of impact cate...

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Chinese Control and Decision Conference, CCDC

作者： Xinyang Deng Yajuan Zhang Bingyi Kang Jiyi Wu Xiaohong Sun Yong Deng School of Computer and Information Sciences Southwest University Chongqing China Hangzhou Key Laboratory of E-Business and Information Security Hangzhou Normal University Hangzhou Zhejiang China College of Food Science and Technology Shanghai Ocean University Shanghai China Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan China School of Electronics & Information Technology Shanghai Jiaotong University Shanghai China Laboratory of National defense for Radioactive Waste and Environmental Security Southwest University of Science and Technology Mianyang China

A relative significance factor of an impact category is the external weight of the impact category and plays an important role in life cycle assessment(LCA). Many multiattribute decision-making (MADM) methods such as analytical hierarchy process(AHP), the rank-order centroid method and the fuzzy method are proposed to determine the relative significance factor of an impact category. However, in previous research, it is shown that, the AHP approach is difficult for experts rendering consistent judgment when the number of pairwise comparisons increases, the rank-order method has some insurmountable shortcomings, the key issue to use fuzzy set theory is its complexity. In order to address this problem, a new simple and efficient method is proposed to determine relative significance factor in a more efficient manner. An example to determine relative significance factor of impact categories is shown to illustrate the efficiency of the proposed method.

关键词： Decision making educational institutions Complexity theory Pragmatics Uncertainty Fuzzy set theory Fuzzy sets

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Aggregation of interval numbers in group decision making

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ICIC Express Letters 2011年第4 A期5卷 1057-1062页

作者： Deng, Yong Wu, Jiyi Sun, Xiaohong Xu, Peida Zhang, Yajuan College of Computer and Information Sciences Southwest University Tiansheng Road Beibei District Chongqing 400715 China Hangzhou Key Lab of E-Business and Information Security Hangzhou Normal University No. 16 Xuelin Street Xiasha Higher Education Zone Hanzhou 310036 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan University Xiangtan 411105 China College of Food Science and Technology Shanghai Ocean University Hu Cheng Loop-road Lingang New City Shanghai 201306 China School of Electronics and Information Technology Shanghai Jiao Tong University Dongchuan Rd. Shanghai 200240 China

A group of decision-makers may differ in their choice of alternatives while taking a decision. So, in any decision-making problem concerning decisions made by a group, the question arises how best we can aggregate individual choices into a general consensus choice. Due to the inherent uncertain property of the problems in the ambiguous decision domain, the decision makers may use interval numbers to represent their opinions in some situations. In this paper, a simple method is proposed to aggregate interval numbers. Some desirable properties of this method are also discussed. Finally, a numerical example is given to show the use of the proposed method. ICIC International © 2011.

关键词： Aggregates

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Bilingual Word Embedding with Sentence Combination CNN for 1-to-N Sentence Alignment 20

Bilingual Word Embedding with Sentence Combination CNN for 1...

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Proceedings of the 4th International Conference on Natural Language processing and Information Retrieval

作者： Xinyuan Ren Xiangling Fu Xuesi Zhou Chunsheng Liu Songfeng Gao Lei Peng School of Software Engineering Beijing University of Posts and Telecommunications Beijing China and Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China Department of Electronic Engineering Tsinghua University Hai Dian Beijing China and Multimedia Signal and Intelligent Information Processing Lab Tsinghua University Hai Dian Beijing China BUPT and Huarong Joint lab of Smart Finance Beijing P. R. China and HuaRong RongTong (Beijing) Technology Co. Ltd Beijing P. R. China

ISBN: (纸本)9781450377607

Sentence alignment, as one of the most active and fundamental tasks in the field of natural language processing (NLP), is usually realized in two categories of methods. One is traditional methods which are firstly proposed, the other, which are adopted later, is based on the Neural Network method. Presently, under the limitation that the existing mainstream data corpora are mostly in the form of 1-to-1, the alignment models with relatively good performance mainly apply to the cases of 1-to-1 sentence alignment. However, under the circumstance that a sentence contains too much information, 1-to-N sentence alignment can actually have a better effect on sentence translation tasks, compared with the 1-to-1 form, since it is more flexible and can reduce the complexity of the original sentence. As a result, we attempt to exploit neural networks with relatively good performance in the cases of 1-to-1 to fit in the cases of 1-to-N. In this paper, a novel 1-N Bilingual word Embedding with Sentence Combination CNN Improved Framework (1-NBESCC) is proposed in order to align 1-to-N sentences more precisely. Experiments show that our proposed model performs as good as the traditional methods such as BLEUALIGN in 1-to-1 situation, but much better in 1-to-N situation.

关键词： Convolutional Neural Network

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Inverse system internal model control of DIFG wind power generator system based on matrix converter

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Taiyangneng Xuebao/Acta Energiae Solaris Sinica 2012年第11期33卷 1983-1991页

作者： Chen, Caixue Xie, Yunxiang Ma, Xueliang Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan 411105 China School of Electric Power South China University of Technology Guangzhou 510641 China Guangdong MingYang Wind Power Industry Group Co. Ltd Zhongshan 528437 China

Aim to solve the poor dynamic performance and the low disturbance resistance of PI power decoupling controller for DFIG power generator system based on matrix converter, this paper propose a nonlinear control algorithm for the AC-excitation DFIG system. First, we establish the mathematical model of the DFIG, based on the model, use the inverse system theory and deduce the inverse model, add the inverse model in series to the front of the original system model, develop the whole system to a pseudo linear system. Consider the system performance influenced by the parameters of the linearized model, we use the predictive control to improve the robustness. The simulation results show that the control strategy has fast response and good dynamic performance. The off-grid and cut-grid experimental data validate that under sub-synchronous or super-synchronous state, the stator voltage and frequency can maintain stability and achieve variable speed constant frequency operation.

关键词： Model predictive control

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