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Erratum to: Multi-party quantum key agreement with Bell states and Bell measurements

Quantum Information processing 2012年第2期12卷 933-933页

作者： Shi, Run-Hua Zhong, Hong Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education Anhui University Hefei China School of Computer Science and Technology Anhui University Hefei China

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A co-evolutionary particle swarm optimization with dynamic topology for solving multi-objective optimization problems

Advances in Modelling and Analysis A

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Advances in Modelling and Analysis A 2016年第1期53卷 145-159页

作者： Wu, Daqing Tang, Lixiang Li, Haiyan Ouyang, LiJun Computer Science and Technology Institute University of South China HangyangHunan China Antai College of Economics and Management Shanghai Jiao Tong University Shanghai200240 China Zigong643000 China Key Laboratory of Guangxi High Schools for Complex System and Computational Intelligence Guangxi University for Nationalities Nanning530006 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University HefeiAnhui Province230039 China Department of Business Administration Hunan University of Finance and Economics Hunan410205 China

This paper proposes a multi-objective with dynamic topology particle swarm optimization (PSO) algorithm for solving multi-objective problems, named DTPSO. One of the main drawbacks of classical multi-objective particle swarm optimization algorithm is low diversity. To overcome this disadvantage, DTPSO uses two dynamic local best particles to lead the search particles with multiple populations to deal with multiple objectives, and maintains diversity of new found non-dominated solutions via partitioned the searching space into fixed number of cells. The proposed DTPSO is validated through comparisons with other two multi-objective algorithms using established benchmarks and metrics. Simulation results demonstrated that DTPSO shows competitive, if not better, performance as compared to the other algorithms. © 2016, AMSE Press. All rights reserved.

关键词： Multiobjective optimization

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A Novel Tri-band Frequency Selective Surface

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Journal of Physics: Conference Series 2020年第1期1651卷

作者： Shenbing Wu Mingsheng Chen Chao Wang Xianliang Wu Key Lab of Ministry of Education of Intelligent Computing & Signal Processing Anhui University Hefei Anhui 230601 China School of Electronic Engineering Huainan Normal University Huainan Anhui 232001 China

This paper presents a novel tri-band frequency miniaturized frequency selection surface (FSS). The proposed FSS consists of single-node folding and single-node folding bending. The single-layer FSS exhibits three stop bands centered at 2.45, 3.61 and 5.79 GHz, with bandwidths of 0.42, 0.48 and 0.70 GHz, respectively. The proposed FSS has better miniaturization characteristics, and its cell size is 0.126 λ×0.126 λ. The three stop band frequencies of the FSS can be independently controlled by simply changing the length of the corresponding branch. In addition, the proposed FSS is symmetrical about the center and exhibits good resonance stability in both TE and TM polarization. The prototype of the proposed FSS was fabricated and tested, compared with the simulation results, the FSS showed stable performance. This method provides ideas for designing multiband FSS in the future.

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Interactive Prototype Learning and Self-Learning for Few-Shot Medical Image Segmentation

SSRN

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SSRN 2024年

作者： Song, Yuhui Xu, Chenchu Wang, Boyan Du, Xiuquan Chen, Jie Zhang, Yanping Li, Shuo School of Computer Science and Technology Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230051 China Department of Computer Science and Technology Tsinghua University Beijing100084 China School of Engineering Case Western Reserve University Cleveland44106 United States

Few-shot learning alleviates the heavy dependence of medical image segmentation tasks on large-scale labeled data, but it shows a strong performance gap when dealing with new targets and new tasks compared with traditional deep learning. Existing few-shot medical image segmentation models mainly realize segmentation by learning the semantic class knowledge of a few known (support) samples and extending it to unknown (query) samples. However, the neglect of the differences between known and unknown samples leads to a certain deviation in the transfer of class knowledge, and the trained model tends to overfit the class knowledge of training classes, which greatly reduces the segmentation and generalization performance of few-shot model. In this work, we propose an interactive prototype learning and self-learning network to alleviate the above problems. Specifically, we propose an interactive prototype learning network to promote feature consistency between support images and query images to improve the accuracy of prototype metric learning. In addition, to avoid overfitting the training class knowledge and reduce the over-dependence of query segmentation on the class knowledge, we introduce a query features-guided self-learning network to achieve the finer segmentation of query images through its rich feature information. Our model obtained the competitive segmentation results on three multi-organ and multi-modal public datasets that show a substantial improvement in generalization ability. © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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A Secure and Efficient Scheme for Digital Gift Certificates

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Journal of Information Hiding and Multimedia signal processing 2015年第3期6卷 416-429页

作者： Liu, Yanjun Chang, Chin-Chen Sun, Chin-Yu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Department of Electrical Engineering Anhui University 111 Jiu-Long road Hefei230039 China Department of Computer Science and Information Engineering Asia University 500 Liou-feng Road Taichung413 Taiwan Department of Information Engineering and Computer Science Feng Chia University 100 Wen-hwa Road Taichung407 Taiwan Department of Computer Science National Tsing-Hua University 101 Kuang-Fu Road Hsinchu300 Taiwan

With the increasingly rapid developments in e-commerce, schemes for digital gift certificates have become prevalent electronic payment systems due to their practicality and simplicity. In 2002, Chan and Chang introduced the concept of digital gift certificates and proposed the first scheme associated with them. Recently, Chang and Chang (2005) proposed a more practical scheme, called DGC-US (digital gift certificates with unconstrained spending), which can reduce the computational cost of all entities involved in Chan and Chang‘s scheme, especially the computational cost of the electronic department store. However, the asymmetric encryption/decryption operations required in the DGC-US scheme still waste a significant amount of time. In this paper, we propose a novel authentication scheme for digital gift certificates based on Chebyshev chaotic maps and the blind signature. Our proposed scheme can satisfy the essential security requirements and withstand various well-known attacks. Our analyses have shown that our proposed scheme maintains all of the merits of the DGC-US scheme but is more efficient in terms of computational cost. © 2015, Ubiquitous International. All rights reserved.

关键词： Authentication

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Drug3D-DTI: Improved Drug-target Interaction Prediction by Incorporating Spatial Information of Small Molecules

Drug3D-DTI: Improved Drug-target Interaction Prediction by I...

引用

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Liao, Zhirui Huang, Xiaodi Mamitsuka, Hiroshi Zhu, Shanfeng Fudan University School of Computer Science Shanghai200433 China Institute of Artificial Intelligence Biomedicine Nanjing University Nanjing China School of Computing Mathematics and Engineering Charles Sturt University AlburyNSW2640 Australia Bioinformatics Center Institute for Chemical Research Kyoto University Kyoto Uji611-0011 Japan Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Ministry of Education China MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China Zhangjiang Fudan International Innovation Center Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai200433 China

ISBN: (纸本)9781665401265

A number of machine learning (ML) approaches for drug discovery have been available that rely only on sequential (1D) and planar (2D) information without effectively using the 3D information for generating features of drugs. However, 3D information of small molecules can reflect relative position of atoms more directly, which affects molecular properties. In this work, we present a new deep learning model called Drug3D-DTI for drug-target interaction prediction. Drug3D-DTI takes advantage of molecular spatial information, i.e., atom proximity in three-dimensional (3D) structures. We comprehensively evaluated the performance of Drug3D-DTI on two datasets with two tasks of regression and classification. In particular, we compared Drug3D-DTI with several existing methods including the two cutting-edge methods for compound-protein interaction prediction. From the experimental results, Drug3D-DTI clearly outperformed other methods under all settings. Further, this performance improvement was validated by ablation experiments and a case study. The implementation of Drug3D-DTI is available at (https://***/zhiruiliao/Drug3D-DTI). © 2021 IEEE.

关键词： Molecules

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On self-dual and LCD double circulant and double negacirculant codes over Fq+ uFq

arXiv

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arXiv 2018年

作者： Shi, Minjia Zhu, Hongwei Qian, Liqin Sok, Lin Sol, Patrick Key Laboratory of Intelligent Computing Signal Processing Ministry of Education Anhui University No.3 Feixi Road Hefei Anhui230039 China School of Mathematical Sciences Anhui University Hefei Anhui230601 China National Mobile Communications Research Laboratory Southeast University China Department of Mathematics Royal University of Phnom Penh Phnom Penh12156 Cambodia CNRS LAGA University of Paris 8 Saint-Denis93526 France

Double circulant codes of length 2n over the semilocal ring R = Fq + uFq, u2 = u, are studied when q is an odd prime power, and-1 is a square in Fq. Double negacirculant codes of length 2n are studied over R when n is even and q is an odd prime power. Exact enumeration of self-dual and LCD such codes for given length 2n is given. Employing a duality-preserving Gray map, self-dual and LCD codes of length 4n over Fq are constructed. Using random coding and the Artin conjecture, the relative distance of these codes is bounded below. The parameters of examples of the modest length are computed. Several such codes are optimal. Copyright © 2018, The Authors. All rights reserved.

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On Error Performance and Concatenated Coding of Polar Codes in AWGN Channels

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IOP Conference Series: Materials Science and Engineering 2020年第7期768卷

作者： Jing Jin Rui Deng Tiansheng Liu Liping Li Key Laboratory of Intelligent Computing and Signal Processing (Ministry of Education) Anhui University Hefei China Department of Electronic Engineering and Information Science University of Science and Technology Hefei China

In additive white Gaussian noise (AWGN) channels, construction of polar codes is needed for every operating signal-to-noise ratio (SNR). Recently, the proposal of the design-SNR reduces the computation effort in constructing polar codes. In this paper, we prove that although the BER performance of the design-SNR construction is not affected, the packet-error-rate (PER) performance is degraded compared with the point-by-point construction. Therefore, a concatenation scheme is proposed to improve the degraded PER performance. Results show the validity of the proposed concatenation scheme when employing the design-SNR construction.

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A new distance-regular graph of diameter 3 on 1024 vertices

arXiv

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arXiv 2018年

作者： Shi, Minjia Krotov, Denis S. Solé, Patrick Key Laboratory of Intelligent Computing Signal Processing Ministry of Education Anhui University No.3 Feixi Road Hefei Anhui230039 China School of Mathematical Sciences Anhui University Hefei Anhui230601 China Sobolev Institute of Mathematics pr. Akademika Koptyuga 4 Novosibirsk630090 Russia 4CNRS/LAGA University of Paris 8 2 rue de la Liberté Saint-Denis93 526 France

The dodecacode is a nonlinear additive quaternary code of length 12. By puncturing it at any of the twelve coordinates, we obtain a uniformly packed code of distance 5. In particular, this latter code is completely regular but not completely transitive. Its coset graph is distance-regular of diameter three on 210 vertices, with new intersection array -33, 30, 15;1, 2, 15}. The automorphism groups of the code, and of the graph, are determined. Connecting the vertices at distance two gives a strongly regular graph of (previously known) parameters (210, 495, 238, 240). Another strongly regular graph with the same parameters is constructed on the codewords of the dual code. A non trivial completely regular binary code of length 33 is constructed.05E30, 94B05 Copyright © 2018, The Authors. All rights reserved.

关键词： Additives

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