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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Yuan Gao Chenglong Li Yabin Zhu Jin Tang Tao He Futian Wang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Institute of Physical Science and Information Technology Anhui University Hefei China Key Laboratory of Industrial Image Processing and Analysis of Anhui Province Hefei China Anhui COWAROBOT CO. Ltd Wuhu China

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

Due to the complementarity of RGB and thermal data, RGBT tracking has received more and more attention in recent years because it can effectively solve the degradation of tracking performance in dark environments and bad weather conditions. How to effectively fuse the information from RGB and thermal modality is the key to give full play to their complementarities for effective RGBT tracking. In this paper, we propose a high performance RGBT tracking framework based on a novel deep adaptive fusion network, named DAFNet. Our DAFNet consists of a recursive fusion chain that could adaptively integrate all layer features in an end-to-end manner. Due to simple yet effective operations in DAFNet, our tracker is able to reach the near-real-time speed. Comparing with the state-of-the-art trackers on two public datasets, our DAFNet tracker achieves the outstanding performance and yields a new state-of-the-art in RGBT tracking.

关键词： Feature extraction Target tracking Robustness Fuses Convolution Adaptive systems

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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 broadband ±45◦ dual-polarized magneto-electric dipole antenna for 2G/3G/LTE/5G/WiMAX applications

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Progress In Electromagnetics Research C 2018年 86卷 153-165页

作者： Lu, Zongzheng Sun, Yufa Zhu, Haoran Huang, Fei Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei230601 China

A novel broadband ±45◦ dual-polarized magneto-electric (ME) dipole antenna is proposed for 2G/3G/LTE/5G (3.3–3.6 GHz)/WiMAX applications. The proposed antenna has Γ-shaped feeding strips to impart a wide impedance bandwidth for its special structure. Stable antenna gain and radiation pattern are realized by using a rectangular box-shaped reflector instead of planar one. The antenna is fabricated and measured. The measured results show that a common impedance bandwidth is 83% with standing-wave ratio (SWR) ≤ 1.5 from 1.59 to 3.83 GHz and port-to-port isolation larger than 25 dB within the bandwidth. The measured antenna gains vary from 8 to 10.8 dBi and from 8 to 10.6 dBi for port 1 and port 2, respectively. The antenna has nearly symmetrical radiation patterns with low back lobe radiation both in horizontal and vertical planes, and broadside radiation patterns with narrow beam can also be obtained. The proposed antenna can be used for multiband base stations in next generation communication systems. © 2018, Electromagnetics Academy. All rights reserved.

关键词： Bandwidth

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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...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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

ISBN: (纸本)9781665429825

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).

关键词： Drugs Proteins Deep learning Solid modeling Three-dimensional displays Conferences Predictive models

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Dog Recognition in Public Places Based on Convolutional Neural Network

Dog Recognition in Public Places Based on Convolutional Neur...

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2019中国信息产业创新发展大会

作者： Ouyang Jianquan He Hao He Yi Tang Huanrong Xiangtan University College of Information Engineering 411105China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education.College of Information Engineering.Xiangtan UniversityXiangtan 411105China

With the increase in the number of dogs in the city,the scene of the dogs in public places can be seen *** the same time,more and more stray dogs appear in public places where dogs are prohibited,which have a certain impact on the city environment and personal *** view of this,we propose a novel algorithm that combines Dense-SIFT and CNN to solve dog recognition problems in public ***,the image is divided into several grids;then the Dense-SIFT algorithm is used to split and combine the descriptors,and the channel information of the eight directions of the image is extracted as the input of the CNN;finally,we design a CNN based on Adam optimization algorithm and cross-entropy to identify the dog *** Experimental results show that the algorithm can fully combine the advantages of Dense-SIFT and CNN to achieve dog recognition in public places,and the correct rate is 94.2%.

关键词： CNN Dense-SIFT Dog recognition

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Magnetotelluric signal-noise separation method based on SVM–CEEMDWT

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Applied Geophysics 2019年第2期16卷 160-170,252-253页

作者： Li Jin Cai Jin Tang Jing-Tian Li Guang Zhang Xian Xu Zhi-Min Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and EngineeringHunan Normal UniversityChangsha 410081China School of Geosciences and Info-Physics Key Laboratory of Metallogenic Prediction of Non-Ferrous Metals and Geological Environment MonitorMinistry of EducationCentral South UniversityChangsha 410083China State Key Laboratory Breeding Base of Nuclear Resources and Environment East China University of TechnologyNanchang 330013China

To better retain useful weak low-frequency magnetotelluric(MT)signals with strong interference during MT data processing,we propose a SVM-CEEMDWT based MT data signal-noise separation method,which extracts the weak MT signal affected by strong ***,the approximate entropy,fuzzy entropy,sample entropy,and Lempel-Ziv(LZ)complexity are extracted from the magnetotelluric ***,four robust parameters are used as the inputs to the support vector machine(SVM)to train the sample library and build a model based on the different complexity of *** on this model,we can only consider time series with strong interference when using the complementary ensemble empirical mode decomposition(CEEMD)and wavelet threshold(WT)for noise *** results suggest that the SVM based on the robust parameters can distinguish the time periods with strong interference well before noise *** with the CEEMD WT,the proposed SVM-CEEMDWT method retains more low-frequency low-variability information,and the apparent resistivity curve is smoother and more ***,the results better reflect the deep electrical structure in the field.

关键词： SVM-CEEMDWT magnetotelluric signal-noise separation MT data processing

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Secure Edge Caching Placement and Delivery for Ultra-Reliable and Low-Latency Vehicular Networks

Secure Edge Caching Placement and Delivery for Ultra-Reliabl...

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IEEE Globecom Workshops

作者： Haojun Yang Kuan Zhang Kan Zheng Yi Qian Intelligent Computing and Communications (IC) Lab Wireless Signal Processing and Networks (WSPN) Lab Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering University of Nebraska-Lincoln Omaha NE USA

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

To meet the urgent requirements of ultra-reliable and low-latency communications (URLLCs) in vehicular networks, vehicular edge caching (VEC) becomes a promising solution to reduce backhaul costs and mitigate network congestion. However, the data requested by vehicular users may be maliciously tampered by attackers during the caching delivery phase. In this paper, we investigate the problem of secure caching placement and delivery for a VEC URLLC system. Specifically, we first analyze the lower bound of spectral efficiency for URLLCs and security metric, i.e., vulnerability. Then, an optimization problem of minimizing the maximum vulnerability among all vehicular users is formulated based on the analysis. Subsequently, we propose a secure caching placement and delivery algorithm to obtain the solutions of our formulated problem by reasonable complexity. Simulation results show that the proposed algorithm can significantly improve the performance of security for the VEC URLLC system.

关键词： Security Optimization Roads Reliability Fading channels signal to noise ratio Antennas

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Nonlinearity in the Dark: Broadband Terahertz Generation with Extremely High Efficiency

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Physical Review Letters 2019年第2期122卷 027401-027401页

作者： Ming Fang Nian-Hai Shen Wei E. I. Sha Zhixiang Huang Thomas Koschny Costas M. Soukoulis Ames Laboratory—U.S. DOE and Department of Physics and Astronomy Iowa State University Ames Iowa 50011 USA Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230039 China Key Laboratory of Micro-nano Electronic Devices and Smart Systems of Zhejiang Province College of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China Institute of Electronic Structure and Lasers (IESL) FORTH 71110 Heraklion Crete Greece

Plasmonic metamaterials and metasurfaces offer new opportunities in developing high performance terahertz emitters and detectors beyond the limitations of conventional nonlinear materials. However, simple meta-atoms for second-order nonlinear applications encounter fundamental trade-offs in the necessary symmetry breaking and local-field enhancement due to radiation damping that is inherent to the operating resonant mode and cannot be controlled separately. Here we present a novel concept that eliminates this restriction obstructing the improvement of terahertz generation efficiency in nonlinear metasurfaces based on metallic nanoresonators. This is achieved by combining a resonant dark-state metasurface, which locally drives nonlinear nanoresonators in the near field, with a specific spatial symmetry that enables destructive interference of the radiating linear moments of the nanoresonators, and perfect absorption via simultaneous electric and magnetic critical coupling of the pump radiation to the dark mode. Our proposal allows eliminating linear radiation damping, while maintaining constructive interference and effective radiation of the nonlinear components. We numerically demonstrate a giant second-order nonlinear susceptibility ∼10−11 m/V, a one order improvement compared with the previously reported split-ring-resonator metasurface, and correspondingly, a 2 orders of magnitude enhanced terahertz energy extraction should be expected with our configuration under the same conditions. Our study offers a paradigm of high efficiency tunable nonlinear metadevices and paves the way to revolutionary terahertz technologies and optoelectronic nanocircuitry.

关键词： Metamaterials

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Mapping Heavy Metals in Cultivated Soils Based on Land Use Types and Cokriging 5th

Mapping Heavy Metals in Cultivated Soils Based on Land Use T...

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5th International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2017

作者： Zhao, Jinling Liu, Chuang Song, Qixiang Jiang, Yan Hong, Qi Huang, Linsheng Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Anhui Engineering Laboratory of Agro-Ecological Big Data Anhui University Hefei230601 China Information Engineering Institute Suzhou University Suzhou234000 China

ISBN: (纸本)9789811308925

It is extremely important to explore the heavy metal content and spatial distribution in different cultivated soils. In our study, cokriging (COK) method was used to investigate the relationship between heavy metals and land use types. A total of six heavy metals including Zn, Fe, Cu, Mn, B, S were selected to forecast the heavy metals in cultivated soils. Five types of cultivated land were considered in the study area, including vegetable land, irrigated land, irrigated paddy field, dry land, orchard. Test of normality was firstly carried out to assure that higher prediction accuracy can be obtained, and then correlation analysis and analysis of variance (ANOVA) were performed to find out the most sensitive heavy metals for spatial interpolation. The analysis results showed that Zn, Cu and Fe were the primary heavy metals in soil of the study area. It was obvious that the effect of Mn and Fe were much greater caused by land use types, while they were smaller for B and S. Three errors of mean standard (MS), root mean square error (RMSE), and root mean square standardized error (RMSSE) were derived to assess the prediction accuracy. In comparison with ordinary kriging (OK), the MS of COK approached to 1, the RMSE was much smaller and the RMSSE also approached to 1, which showed that COK can make better predictions than OK. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Heavy metals

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Doubling Algorithm for Nonsymmetric Algebraic Riccati Equations Based on a Generalized Transformation

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Advances in Applied Mathematics and Mechanics 2018年第6期10卷 1327-1343页

作者： Bo Tang Yunqing Huang Ning Dong School of Mathematics and Computational Science Hunan Key Laboratory for Computation and Simulation in Science and EngineeringKey Laboratory of Intelligent Computing and Information Processing of Ministry of EducationXiangtan UniversityXiangtan 411105HunanChina School of Science Hunan City UniversityYiyang 413000HunanChina School of Science Hunan University of TechnologyZhuzhou 412000HunanChina

We consider computing the minimal nonnegative solution of the nonsymmetric algebraic Riccati equation with *** is well known that such equations can be efficiently solved via the structure-preserving doubling algorithm(SDA)with the shift-and-shrink transformation or the generalized Cayley *** this paper,we propose a more generalized transformation of which the shift-and-shrink transformation and the generalized Cayley transformation could be viewed as two special ***,the doubling algorithm based on the proposed generalized transformation is presented and shown to be ***,the convergence result and the comparison theorem on convergent rate are *** numerical experiments show that the doubling algorithm with the generalized transformation is efficient to derive the minimal nonnegative solution of nonsymmetric algebraic Riccati equation with M-matrix.

关键词： Shift-and-shrink transformation generalized Cayley transformation doubling algorithm nonsymmetric algebraic Riccati equation

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