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Superposed Transparency Effect and Entanglement Generation with Hybrid System of Photonic Molecule and Dipole Emitter

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Chinese Physics Letters 2019年第3期36卷 41-44页

作者： Ji-Bing Yuan Zhao-Hui Peng Shi-Qing Tang Deng-Yu Zhang College of Physics and Electronic Engineering and Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal University Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and ApplicationsHunan Normal University Institute of Modern Physics and Department of Physics Hunan University of Science and Technology

We theoretically investigate the transparency effect with a hybrid system composed of a photonic molecule and dipole emitter. It is shown that the transparency effect incorporates both the coupled resonator-induced transparency(CRIT) effect and the dipole-induced transparency(DIT) effect. It is found that the superposed transparency windows are consistently narrower than the CRIT and DIT transparency windows. Benefiting from the superposed transparency effect, the photonic Faraday rotation effect could be realized in the photonic molecule system, which is useful for entanglement generation and quantum information processing.

关键词： DIT Transparency Photonic Molecule Emitter

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An improved Isomap method for manifold learning

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International Journal of intelligent Computing and Cybernetics 2017年第1期10卷 30-40页

作者： Taiguo Qu Zixing Cai College of Computer Science and Technology Hengyang Normal UniversityHengyangChina Hunan Provincial Key Laboratory of Intelligent Information Processing and Application HengyangChina School of Information Science and Engineering Central South UniversityChangshaChina

Purpose-Isometric feature mapping(Isomap)is a very popular manifold learning method and is widely used in dimensionality reduction and data *** most time-consuming step in Isomap is to compute the shortest paths between all pairs of data points based on a neighbourhood *** classical Isomap(C-Isomap)is very slow,due to the use of Floyd’s algorithm to compute the shortest *** purpose of this paper is to speed up ***/methodology/approach-Through theoretical analysis,it is found that the neighbourhood graph in Isomap is *** this case,the Dijkstra’s algorithm with Fibonacci heap(Fib-Dij)is faster than Floyd’s *** this paper,an improved Isomap method based on Fib-Dij is *** using Fib-Dij to replace Floyd’s algorithm,an improved Isomap method is presented in this ***-Using the S-curve,the Swiss-roll,the Frey face database,the mixed national institute of standards and technology database of handwritten digits and a face image database,the performance of the proposed method is compared with C-Isomap,showing the consistency with C-Isomap and marked improvements in terms of the high *** also demonstrate that Fib-Dij reduces the computation time of the shortest paths from O(N3)to O(N2lgN).Research limitations/implications-Due to the limitations of the computer,the sizes of the data sets in this paper are all smaller than 3,***,researchers are encouraged to test the proposed algorithm on larger data ***/value-The new method based on Fib-Dij can greatly improve the speed of Isomap.

关键词： Dijkstra’s algorithm Fibonacci heap Floyd’s algorithm Isomap Manifold learning Shortest path Paper type Research paper

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Some applications of nonlinear science methods and support vector machine methods to protein problems

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Mini-Reviews in Organic Chemistry 2015年第6期12卷 493-505页

作者： Hana, Guo Sheng Yu, Zu-Guo Zhao, Zhi-Qin Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University XiangtanHunan411105 China School of Mathematical Sciences Queensland University of Technology GPO Box 2434 BrisbaneQ4001 Australia

Protein folding, prediction of protein structure and functions are most important problems in bioinformatics. The protein fold process mainly reflects in the kinetic order of folding. Predicting the structural classes of low-homology protein is a difficult problem in the prediction of protein structure. In order to understanding the mechanism of programmed cell death, it is very necessary to obtain the information about subcellular locations and functions of apoptosis proteins. Predicting protein subnuclear localizations is a challenging problem which is harder than predicting protein subcellular locations. Predicting membrane protein types is related to the structure and function of proteins. In this review, we introduce some applications of nonlinear science methods and support vector machine methods to the above protein problems. The nonlinear science methods including the horizontal visibility network, kernel method, recurrence quantification analysis, global descriptor, Lempel-Ziv complexity, and Hilbert-Huang transform are used to extract features in these approaches. © 2015 Bentham Science Publishers.

关键词： Support vector machines

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Secondary structure element alignment kernel method for prediction of protein structural classes

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Current Bioinformatics 2014年第3期9卷 253-257页

作者： Han, Guo-Sheng Yu, Zu-Guo Anh, Vo Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan 411105 China School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane Q 4001 Australia

In this paper, we aim at predicting protein structural classes for low-homology data sets based on predicted secondary structures. We propose a new and simple kernel method, named as SSEAKSVM, to predict protein structural classes. The secondary structures of all protein sequences are obtained by using the tool PSIPRED and then a linear kernel on the basis of secondary structure element alignment scores is constructed for training a support vector machine classifier without parameter adjusting. Our method SSEAKSVM was evaluated on two low-homology datasets 25PDB and 1189 with sequence homology being 25% and 40%, respectively. The jackknife test is used to test and compare our method with other existing methods. The overall accuracies on these two data sets are 86.3% and 84.5%, respectively, which are higher than those obtained by other existing methods. Especially, our method achieves higher accuracies (88.1% and 88.5%) for differentiating the α+ β class and the α/β class compared to other methods. This suggests that our method is valuable to predict protein structural classes particularly for low-homology protein sequences. The source code of the method in this paper can be downloaded at http://***/myphp/math/research/source/SSEAK_source_***. © 2014 Bentham Science Publishers.

关键词： Support vector machines

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Exploiting boundary-isolated multiple edge-spanning tree for node shielding-based network robustness enhancement

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Expert Systems with Applications 2025年 287卷

作者： Wei Wei Mingkun Jiang Qinghui Zhang Peng Li Tao Ma Weidong Li Key Laboratory of Grain Information Processing and Control (Henan University of Technology) Ministry of Education Zhengzhou 450001 China Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making Henan University of Technology Zhengzhou 450001 China Institute for Complexity Science Henan University of Technology Zhengzhou 450001 China Beijing Institute of Technology No. 5 Zhongguancun South Street Haidian District Beijing 100086 China Ultimate IOT (Henan) Technology Ltd Zhengzhou 450001 China

As a critical network protection strategy, shielding vulnerable nodes can help build robust networks, where robustness can be generally measured using network connectivity, or complex metrics adapted to specific scenarios. However, the general connectivity-oriented node shielding is NP-hard and few methods can be applied in large-scale networks. The existing limited one can only achieve a far from optimal solution, where the core problem is that it shields the vulnerable part of the graph to infinite connectivity. We analyzed the spanning tree strategy and found a special kind of structure that is a superset of node cut sets, and any combination of nodes in the structure cannot form node cut sets smaller than the threshold. By using the structure as a boundary and shielding separate components, the algorithm can further prevent a large percentage of nodes from shielding without affecting robustness, and thus significantly reduce the cost of shielding. Experimental results in small and large scale graphs reveals that it is more than 10 5 times efficient than the optimal method while only introducing about 2 % extra cost. The shield ratio in large graphs can be more than 99.9 % and the cost saving ratio can be more than 60 % compared to the existing one, where the ratio is much lower in other heuristics.

关键词： Trees (mathematics)

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Semantic Maximum Relevance and Modal Alignment for Cross-Modal Retrieval 11

Semantic Maximum Relevance and Modal Alignment for Cross-Mod...

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11th International Conference on Computing and Pattern Recognition, ICCPR 2022

作者： Sun, Pingping Qiang, Baohua Liu, Zhiguang Yang, Xianyi Xi, Guangyong Liu, Weigang Chen, Ruidong Zhang, Shihao Guilin University of Electronic Technology China The 54th Research Institute of Cetc Hebei Key Laboratory of Intelligent Information Perception and Processing China

ISBN: (纸本)9781450397056

With the increasing abundance of multimedia data resources, researches on mining the relationship between different modalities to achieve refined cross-modal retrieval are gradually emerging. In this paper, we propose a novel Semantic Maximum Relevance and Modal Alignment (SMR-MA) for Cross-Modal Retrieval, which utilizes the pre-trained model with abundant image text information to extract the features of each image text, and further promotes the modal information interaction between the same semantic categories through the modal alignment module and the multi-layer perceptron with shared weights. In addition, multi-modal embedding is distributed to the normalized hypersphere, and angular edge penalty is applied between feature embedding and weight in angular space to maximize the classification boundary, thus increasing both intra-class similarity and inter-class difference. Comprehensive analysis experiments on three benchmark datasets demonstrate that the proposed method has superior performance in cross-modal retrieval tasks and is significantly superior to the state-of-the-art cross-modal retrieval methods. © 2022 ACM.

关键词： Alignment

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Abstractive Text Summarization Model Based on BERT Vectorization and Bidirectional Decoding

Abstractive Text Summarization Model Based on BERT Vectoriza...

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2023 International Conference on Cyber Security, Artificial Intelligence, and Digital Economy, CSAIDE 2023

作者： Teng, Keliang Qiang, Baohua Wang, Yufeng Yang, Xianyi Wang, Yuemeng Wang, Chen Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China Hebei Key Laboratory of Intelligent Information Perception and Processing The 54th Research Institute of CETC Shijiazhuang050081 China

ISBN: (纸本)9781510666597

Facing the pressure of the extremely inflated network information and data overload surplus, it is extremely important to locate "valuable information" efficiently and accurately. Text summarization technology in the field of Natural Language processing (NLP) is an effective means to analyze and process network information. In this paper, we propose an abstractive text summarization model based on bidirectional encoder representations from transformers (BERT) vectorization and bidirectional decoding. The BERT is adopted to obtain a more global vector representation, which helps the subsequent encoder and decoder to fuse the full-text information to generate a summary with high generality. The decoding phase adopts a bidirectional decoding structure and combines the attention mechanism to maintain the bilateral decoding result to generate summaries. The bidirectional decoding structure can be fine-tuned according to the bidirectional results, which can overcome the tilt problem of the unidirectional structure, and the generated summaries are more consistent. The experimental results on the NLPCC2017 text summarization dataset show that the summaries generated by our model have the higher coherence at the word and sentence level, and the stronger generalization of the full text. © 2023 SPIE.

关键词： Decoding

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On the parameterized complexity of minimum/maximum degree vertex deletion on several special graphs

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Frontiers of Computer Science 2023年第4期17卷 97-107页

作者： Jia LI Wenjun LI Yongjie YANG Xueying YANG School of Information Engineering Hunan Industry PolytechnicChangsha 410036China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangsha 410015China Chair of Economic Theory Saarland UniversitySaarbrücken 66123Germany

In the minimum degree vertex deletion problem,we are given a graph,a distinguished vertex in the graph,and an integer κ,and the question is whether we can delete at most κ vertices from the graph so that the distinguished vertex has the unique minimum *** maximum degree vertex deletion problem is defined analogously but here we want the distinguished vertex to have the unique maximum *** is known that both problems areΨ-hard and fixed-parameter intractable with respect to some natural *** this paper,we study the(parameterized)complexity of these two problems restricted to split graphs,p-degenerate graphs,and planar *** study provides a comprehensive complexity landscape of the two problems restricted to these special graphs.

关键词： minimum degree maximum degree vertex deletion split graphs planar graphs parameterized complexity

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A Multimodal Semantic Model of Packaging Sorting Field Based on Deep Learning

A Multimodal Semantic Model of Packaging Sorting Field Based...

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International Conference on Cyberspace Data and Intelligence, CyberDI 2020 and the International Conference on Cyber-Living, Cyber-Syndrome and Cyber-Health, CyberLife 2020

作者： Shen, Xuehao Xiao, Falong Wu, Yuezhong Li, Changyun Dai, Honggang School of Traffic Engineering Hunan University of Technology Zhuzhou China Intelligent Information Perception and Processing Technology Hunan Province Key Laboratory Zhuzhou China China Quality Mark Certification Group Shandong CO. LTD. Jinan China

ISBN: (纸本)9789813343351

In order to solve the practical problem of modal gap between different packaging and sorting data in multimodal environment, and not applying traditional machine learning methods directly, by introducing a deep learning feature extraction method, a semantic model of feature representation of multimodal data in packaging and sorting field is proposed based on deep learning. This method proposes a multimodal neural network. For each modal, there is an independent multi-layer sub-neural network corresponding to it. First, converting features under different modalities into features of the same modal, and passing a network layer shared by all modes above these sub-neural networks to establish connections between different modes;then adopting a structured sparse feature selection method to obtain the importance weights of different modalities, and selecting the most important features for the current learning task, so as to eliminate redundant information and noise;finally constructing a multimodal joint shared feature semantic model and applying in the field of packaging sorting. Experimental results show that the proposed model has significant effects and is significantly better than other similar algorithms. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Extraction

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A Novel Binary Firefly Algorithm for the Minimum Labeling Spanning Tree Problem

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Computer Modeling in Engineering & Sciences 2020年第10期125卷 197-214页

作者： Mugang Lin Fangju Liu Huihuang Zhao Jianzhen Chen College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002China School of Computer Science University of South ChinaHengyang421001China

Given a connected undirected graph G whose edges are labeled,the minimumlabeling spanning tree(MLST)problemis to find a spanning tree of G with the smallest number of different *** is anNP-hard combinatorial optimization problem,which is widely applied in communication networks,multimodal transportation networks,and data *** approximation algorithms and heuristics algorithms have been proposed for the *** algorithm is a new meta-heuristic *** of its simplicity and easy implementation,it has been successfully applied in various ***,the basic firefly algorithm is not suitable for discrete *** this end,a novel discrete firefly algorithm for the MLST problem is proposed in this paper.A binary operation method to update firefly positions and a local feasible handling method are introduced,which correct unfeasible solutions,eliminate redundant labels,and make the algorithm more suitable for discrete *** results show that the algorithm has good *** algorithm can be extended to solve other discrete optimization problems.

关键词： Minimum labeling spanning tree problem binary firefly algorithm meta-heuristics discrete optimization

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