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Compressed sensing based on best wavelet packet basis for image processing

Journal of Computers (Finland) 2013年第8期8卷 1947-1950页

作者： Li, Zhe Tao Xie, Jing Xiong Yu, Zu Guo Tu, Deng Biao Choi, Young June The College of Information Engineering Xiangtan University Hunan 411105 China School of Computer National University of Defense Technology Hunan 410073 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan 411105 China School of Mathematics and Computational Science Xiangtan University Hunan 411105 China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing 100029 China Department of Information and Computer Engineering Ajou University Suwon 443749 Korea Republic of

In this paper, an algorithm named best wavelet packet tree decomposition (BWPTD) is proposed for image compression. In order to obtain better sparse representation of image, best wavelet packet basis is introduced to decompose image signal in the algorithm. Experimental results show that BWPTD is better than single layer wavelet decompression (SLWD) and original compressed sensing (OCS) in peak signal to noise ratio (PSNR) by 2db and 8db, respectively. In addition, the reconstruction time of BWPTD is only half as that of SLWD and OCS. © 2013 ACADEMY PUBLISHER.

关键词： Compressed sensing

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Interpretable multi-view attention network for drug-drug interaction prediction

Interpretable multi-view attention network for drug-drug int...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xuan Lin Qi Wen Sijie Yang Zu-Guo Yu Yahui Long Xiangxiang Zeng College of Computer Science Xiangtan University China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University China Singapore Immunology Network (SIgN) Agency for Science Technology and Research(A*STAR) Singapore College of Information Science and Engineering Hunan University China

Drug-drug interaction (DDI) plays an increasingly crucial role in drug discovery. Predicting potential DDI is also essential for clinical research. Given the high cost and risk of wet-lab experiments, in-silico DDI prediction is an alternative choice. Recently, deep learning methods have been developed for DDI prediction. However, most of existing methods focus on feature extraction from either molecular SMILES sequences or drug interactive networks, ignoring the valuable complementary information that can be derived from these two views. In this paper, we propose a novel interpretable Multi-View Attention network (MVA-DDI) for DDI prediction. MVA-DDI can effectively extracts drug representations from different perspectives to improve DDI prediction. Specifically, for a given drug, we design a transformer-based encoder and a graph convolutional networkbased encoder to learn sequence and graph representations from SMILES sequence and molecular graph, respectively. To fully exploit the complementary information between the sequence and molecular views, an attention mechanism is further adopted to adaptively aggregate the sequence and graph representations by taking the importance of different views into accounts, generating the final drug representations. Comparison experiments demonstrated that our MVA-DDI 1 model achieved superior performance to state-of-the-art models on DDI prediction.

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A cross layer collaborating cache scheme to improve performance of HTTP clients in MANETs

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International Journal of u- and e- Service, Science and Technology 2013年第6期6卷 51-57页

作者： Liu, Jin Ren, Hongmin Wang, Jin Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Shanghai International Shipping Institute Shanghai Maritime University Shanghai China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China College of Information Engineering Shanghai Maritime University Shanghai China

This paper proposes a novel cross layer collaborating cache scheme for HTTP clients. It is designed to accelerate the process that an HTTP client retrieves Web content in mobile ad hoc network environment. Unlike other techniques to exploit Web proxy caching or to build P2P protocol overlay to improve HTTP, our scheme incorporates collaboratively working capability into the HTTP client response cache, and thus there is no impact on the compatibility of upper level applications. In addition, as the sharing file information is diffused with routing information, there is no redundant traffic to maintain the cooperating cache mechanism. Using this scheme, cacheable Web content can be distributed among local peers and retrieved later by a client if needed. In our simulation results, this novel cache scheme significantly decreases the latency experienced by a client to download Web content. © 2013 SERSC.

关键词： HTTP

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Deepfake Detection: A Comprehensive Survey from the Reliability Perspective

arXiv

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arXiv 2022年

作者： Wang, Tianyi Liao, Xin Chow, Kam Pui Lin, Xiaodong Wang, Yinglong Department of Computer Science The University of Hong Kong Hong Kong College of Computer Science and Electronic Engineering Hunan University China School of Computer Science University of Guelph Canada Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China

The mushroomed Deepfake synthetic materials circulated on the internet have raised a profound social impact on politicians, celebrities, and individuals worldwide. In this survey, we provide a thorough review of the existing Deepfake detection studies from the reliability perspective. We identify three reliability-oriented research challenges in the current Deepfake detection domain: transferability, interpretability, and robustness. Moreover, while solutions have been frequently addressed regarding the three challenges, the general reliability of a detection model has been barely considered, leading to the lack of reliable evidence in real-life usages and even for prosecutions on Deepfake-related cases in court. We, therefore, introduce a model reliability study metric using statistical random sampling knowledge and the publicly available benchmark datasets to review the reliability of the existing detection models on arbitrary Deepfake candidate suspects. Case studies are further executed to justify the real-life Deepfake cases including different groups of victims with the help of the reliably qualified detection models as reviewed in this survey. Reviews and experiments on the existing approaches provide informative discussions and future research directions for Deepfake detection. Copyright © 2022, The Authors. All rights reserved.

关键词： Data reliability

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CSCL-DTI: predicting drug-target interaction through cross-view and self-supervised contrastive learning

CSCL-DTI: predicting drug-target interaction through cross-v...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xuan Lin Xi Zhang Zu-Guo Yu Yahui Long Xiangxiang Zeng Philip S. Yu College of Computer Science Xiangtan University China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University China Singapore Immunology Network (SIgN) Agency for Science Technology and Research (A*STAR) Singapore College of Information Science and Engineering Hunan University China Department of Computer Science University of Illinois Chicago USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Accurately predicting drug-target interactions (DTI) is a critical step in drug discovery. Existing methods of DTI prediction primarily employ Simplified Molecular-Input Line-Entry System (SMILES) sequences or molecular graphs to learn drug representations. However, the features learned by such single-view approach is prone to incomplete. While some multiview methods that consider the views of both SMILES sequences and molecular graphs have been developed, these methods often fall in short in capturing potential interactions between views. In this work, we propose a novel dual contrastive learning framework CSCL-DTI for DTI prediction. First, we design a contrastive-enhanced cross-view representation learning (CVRL) to learn representations for drugs. In this module, Transformer-based and graph convolutional network (GCN)-based encoders are separately adopted to learn view-specific representations, followed by contrastive learning to enrich the representations by accounting for the potential interplay between local chemical context and topological structure. Second, we combine Transformer with self-supervised contrastive learning (SSCL) to learn representations for targets by modelling protein amino acids sequences. The scheme allows to effectively preserve the intrinsic characteristics of the sequences. Finally, we introduce a bilinear attention network to obtain an integrated representation by adaptively incorporating drug and target representations. Benchmarking experiments on two datasets demonstrated that CSCL-DTI 1 outperforms six state-of-the-art methods.

关键词： Drugs Proteins Representation learning Protein engineering Adaptation models Contrastive learning Predictive models Transformers Amino acids Diffusion tensor imaging

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A New Logistic Map Based Chaotic Biogeography-based Optimization Approach for Cluster Analysis

A New Logistic Map Based Chaotic Biogeography-based Optimiza...

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2016 IEEE International Conference on Progress in Informatics and computing

作者： Shui-Hua Wang Hui-Min Lu Yu-Jie Li Yang Wang Zhi-Min Chen Yu-Min Wei Wen-Juan Jia Fang-Yuan Liu Yu-Dong Zhang School of Computer Science and Technology Nanjing Normal University Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Department of Electrical Engineering The city college of New YorkCUNY Department of Mechanical and Control Engineering Kyushu Institute of Technology School of Information Engineering Yangzhou University School of Physics and Electronic Engineering Taizhou University School of Electronic Information Shanghai Dianji University School of Energy and Mechanical Engineering Nanjing Normal University Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University

Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization(CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.

关键词： chaotic biogeography-based optimization cluster analysis centroid-based clustering simulation particle swarm optimization

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Joint power allocation and beamforming for non-orthogonal multiple access (NOMA) in 5G millimeter-wave communications

arXiv

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arXiv 2017年

作者： Xiao, Zhenyu Zhu, Lipeng Choi, Jinho Xia, Pengfei Xia, Xiang-Gen School of Electronic and Information Engineering Beihang University Beijing100191 China Beijing Key Laboratory for Network-Based Cooperative Air Traffic Management Beijing Laboratory for General Aviation Technology Beijing100191 China Collaborative Innovation Center of Geospatial Technology Wuhan430079 China Korea Republic of School of Electronics and Information Engineering Key Laboratory of Embedded System and Service Computing Tongji University Shanghai200092 China College of Information Engineering Shenzhen University Shenzhen China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

In this paper we explore non-orthogonal multiple access (NOMA) in millimeter-wave (mmWave) communications (mmWave-NOMA). In particular, we consider a typical problem, i.e., maximization of the sum rate of a 2-user mmWave-NOMA system. In this problem, we need to find the beamforming vector to steer towards the two users simultaneously subject to an analog beamforming structure, while allocating appropriate power to them. As the problem is non-convex and may not be converted to a convex problem with simple manipulations, we propose a suboptimal solution to this problem. The basic idea is to decompose the original joint beamforming and power allocation problem into two sub-problems which are relatively easy to solve: one is a power and beam gain allocation problem, and the other is a beamforming problem under a constant-modulus constraint. Extension of the proposed solution from 2-user mmWave-NOMA to more-user mmWave-NOMA is also discussed. Extensive performance evaluations are conducted to verify the rational of the proposed solution, and the results also show that the proposed sub-optimal solution achieve close-to-bound sum-rate performance, which is significantly better than that of time-division multiple access (TDMA). Copyright © 2017, The Authors. All rights reserved.

关键词： Millimeter waves

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Signal sparse representation based on the peak transform and modulus maximum of wavelet coefficients

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Tamkang Journal of Science and Engineering 2011年第3期14卷 217-224页

作者： Cen, Yi-Gang Xin, Rui Chen, Xiao-Fang Cen, Li-Hui Hu, Ming-Xing Chen, Shi-Ming Institute of Information Science Beijing Jiaotong University Beijing 100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing 100044 China School of Information Science and Engineering Central South University Hunan Changsha 410083 China Centre for Medical Image Computing University College London United Kingdom School of Electrical and Electronic Engineering East China Jiao Tong University Jiangxi Nanchang 330013 China

It is now well-known that one can reconstruct sparse or compressible signals accurately from a very limited number of measurements. This technique is known as "compressed sensing" or "compressive sampling" (CS). A basic requirement of CS is that a signal should be sparse or it can be sparsely represented in some orthogonal bases. Based on the Peak Transform (PT) and modulus maximum of wavelet coefficients, a new algorithm was proposed for the signals that are non-sparse themselves and can not be sparsely represented by wavelet transform such as the Linear Frequency Modulated signal. According to this algorithm, K-sparse wavelet coefficients can be obtained. For the peak sequence produced by the Peak Transform, value expansion approach of reversible watermarking is exploited such that the peak sequence can be embedded into the measurements of the signal, which avoids increasing additional points for transmission. By using the Peak Transform and modulus maximum, non-sparse wavelet coefficients can be transformed into K-sparse coefficients, which improves the reconstruction result of CS. Simulation results showed that our proposed algorithm achieved better performance comparing with the original CS algorithm.

关键词： Compressed sensing

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A new logistic map based chaotic biogeography-based optimization approach for cluster analysis

A new logistic map based chaotic biogeography-based optimiza...

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IEEE International Conference on Progress in Informatics and computing (PIC)

作者： Shui-Hua Wang Hui-Min Lu Yu-Jie Li Yang Wang Zhi-Min Chen Yu-Min Wei Wen-Juan Jia Fang-Yuan Liu Yu-Dong Zhang Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Changsha Hunan China School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China Department of Electrical Engineering CUNY New York NY USA Department of Mechanical and Control Engineering Kyushu Institute of Technology Fukuoka Prefecture Japan School of Information Engineering Yangzhou University Yangzhou Jiangsu China School of Physics and Electronic Engineering Taizhou University Taizhou Zhejiang China School of Electronic Information Shanghai Dianji University Shanghai China School of Energy and Mechanical Engineering Nanjing Normal University Nanjing Jiangsu China Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University Quanzhou Fujian China

ISBN: (纸本)9781509034857

Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization (CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.

关键词： Genetic algorithms Optimization Clustering algorithms Particle swarm optimization Algorithm design and analysis Logistics Mathematical model

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Highly Stable Zinc Anodes Enabled by a Dual-Function Additive of In-Situ Film Formation and Electrostatic Shielding

SSRN

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SSRN 2024年

作者： Ji, Rui-Han Fu, Hao Wen, Qing Li, Pei-Yao Yang, Pei Chen, Yu-Jing Chen, He-Zhang Zhang, Jia-Feng Li, Ling-Jun Wang, Jie-Xi Wu, Qing Zhang, Xia-Hui Zheng, Jun-Chao Washington State University United States School of Metallurgy and Environment Central South University Hunan Changsha410083 China Engineering Research Center of the Ministry of Education for Advanced Battery Materials Central South University Hunan Changsha410083 China National Energy Metal Resources and New Materials Key Laboratory Central South University Hunan Changsha410083 China School of Chemistry and Chemical Engineering Hunan University of Science and Technology Hunan Xiangtan411201 China School of Materials Science and Engineering Changsha University of Science and Technology Changsha410114 China National Engineering Research Centre of Advanced Energy Storage Materials Changsha410205 China School of Information and Network Center Central South University Hunan Changsha410083 China Center for High-Performance Computing United States

The practical application of aqueous Zn-ion batteries (AZIBs) is significantly hindered by the short lifespan and low utilization of zinc metal anodes caused by the notorious hydrogen evolution reaction (HER), corrosion, and dendritic growth. The in-situ formation of a dense Zn-ion conducting solid electrolyte interphase (SEI) is an ideal approach to handle these problems. Herein, we present a twofold-protective strategy involving the dense in-situ SEI layer and the electrostatic shielding layer by incorporating 0.01 M yttrium nitrate (YN) additive into 2 M zinc sulfate electrolyte. The robust oxidizing characteristic of the nitrate induces rapid passivation on the surface of Zn anodes, by which these inevitable passivation byproducts are utilized for in-situ fabricating a dense SEI protection layer with excellent wettability. Besides, the infusion of high-valence rare-earth Y3+ forms a durable, dynamic electrostatic shielding layer around the growth tip of the protuberances. This can regulate Zn2+ deposition and aid in rectifying the irregular and sharp surface on zinc anodes. Benefiting from the synergistic effect of the static film protection and the dynamic electrostatic rectification, zinc anodes exhibit outstanding stability, as evidenced by remarkable cycling lifespan of exceeding 2000 h at 1 mAh cm-2 and 600 h at 10 mAh cm-2 in Zn symmetric cells and the preeminent CE of 99.83% with stably cycling over 2200 times in half-cells at 4 mA cm-2. This study reveals a straightforward yet pragmatic avenue of synergistic dual-protection effect to achieve anti-corrosion and dendrite-free Zn anodes. © 2024, The Authors. All rights reserved.

关键词： Passivation

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