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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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Learning Target-Oriented Dual Attention for Robust RGB-T Tracking

Learning Target-Oriented Dual Attention for Robust RGB-T Tra...

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IEEE International Conference on Image processing

作者： Rui Yang Yabin Zhu Xiao Wang Chenglong Li Jin Tang Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China Institute of Physical Science and Information Technology Anhui University Hefei China

ISBN: (纸本)9781538662502;9781538662496

RGB-Thermal object tracking attempts to locate target object using complementary visual and thermal infrared data. Existing RGB-T trackers fuse different modalities by robust feature representation learning or adaptive modal weighting. However, how to integrate dual attention mechanism for visual tracking is still a subject that has not been studied yet. In this paper, we propose two visual attention mechanisms for robust RGB-T object tracking. Specifically, the local attention is implemented by exploiting the common visual attention of RGB and thermal data to train deep classifiers. We also introduce the global attention, which is a multimodal target-driven attention estimation network. It can provide global proposals for the classifier together with local proposals extracted from previous tracking result. Extensive experiments on two RGB-T benchmark datasets validated the effectiveness of our proposed algorithm.

关键词： Target tracking Visualization Training Feature extraction Proposals Object tracking Estimation

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Cross-Modality Coherent Registration of Whole Mouse Brains

Research Square

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Research Square 2021年

作者： Peng, Hanchuan Qu, Lei Li, Yuanyuan Xie, Peng Liu, Lijuan Wang, Yimin Wu, Jun Liu, Yu Wang, Tao Li, Longfei Guo, Kaixuan Wan, Wan Ouyang, Lei Xiong, Feng Zheng, Yefeng Xu, Fang Gong, Hui Dong, Hongwei Luo, Qingming Bi, Guo-Qiang Hawrylycz, Michael Zeng, Hongkui Allen Institute for Brain Science United States Key Laboratory of Intelligent Computation & Signal Processing Ministry of Education Anhui University China SEU-ALLEN Joint Center Institute for Brain Intelligence Southeast University Bangladesh Institute for Brain and Intelligence Southeast University Bangladesh Tencent Jarvis Lab China CAS Key Laboratory of Brain Connectome and Manipulation China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics MoE Key Laboratory for Biomedical Photonics Huazhong University of Science and Technology China Center for Integrative Connectomics Department of Neurobiology David Geffen School of Medicine UCLA United States Hainan University China Hefei National Laboratory for Physical Sciences at the Microscale School of Life Sciences University of Science and Technology of China China

Recent whole brain mapping projects are collecting large-scale 3D images using powerful and informative modalities, such as STPT, fMOST, VISoR, or MRI. Registration of these multi-dimensional whole-brain images onto a standard atlas is essential for characterizing neuron types and constructing brain wiring diagrams. However, cross-modality image registration is challenging due to intrinsic variations of brain anatomy and artifacts resulted from different sample preparation methods and imaging modalities. We introduced a cross-modality registration method, called mBrainAligner, which uses coherent landmark mapping as well as deep neural networks to align whole mouse brain images to the standard Allen Common Coordinate Framework atlas. We also built a single cell resolution atlas using the fMOST modality, and used our method to generate whole brain map of 3D full single neuron morphology and neuron cell types. © 2021, CC BY.

关键词： Brain mapping

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An Improved ACO with First-job Restriction and P-ANT for Batch Scheduling Problem

An Improved ACO with First-job Restriction and P-ANT for Bat...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

作者： Yang Yang Zhaohong Jia Yiwen Zhang Kai Li Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China School of Management Hefei University of Technology Hefei China

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

This research applies ant colony optimization (ACO) algorithm to minimizing the makespan and the maximum tardiness for scheduling the jobs with non-identical sizes and release time on parallel batch process machines. To improve search efficiency, a new first job selection strategy and a dynamic heuristic are provided. Additionally, a new role, called P-ANT, is designed to improve the search ability of the ant colony. To solve the studied problem, a novel multi-objective ACO algorithm with P-ANT (MACOP) is proposed. The MACOP is compared with several other state-of-the-art algorithms through extensive experiments. The results demonstrate the effectiveness of the proposed MACOP algorithm.

关键词： Heuristic algorithms Job shop scheduling Genetic algorithms signal processing algorithms Optimization Batch production systems Ant colony optimization

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A Design of Hign Gain Wideband Circular Polarization Antenna Based on All-Metal Vivaldi Antenna

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

作者： Xuemei Li Zhixiang Huang Dui Liu Chao Wang Yi Cheng Key Lab of Intelligent Computing & Signal Processing Ministry of Education AnHui University AnHui University Heifei China AnHui 230000 China

This paper designs a broadband circularly polarized antenna covering the 25 GHz to 31 GHz band. The Vivaldi antenna is used as the basic structure to generate broadband electromagnetic waves; circular polarization is achieved by feeding through a circularly polarized bridge; a metal choke ring is used at the bottom to ensure the low side lobes of the antenna. The antenna consists of four basic Vivaldi antennas, a metal choke ring, a grounded substrate, and a feed network. The results show that the antenna covers the entire frequency band when the axial ratio is lower than 3 dB, and the impedance matching is better. The use of a choke ring structure ensures that the antenna electromagnetic waves are radiated upward, resulting in higher gain and lower side lobes. The highest gain in the included frequency band is 11.4 dB, and the simulation result is good. The antenna has high gain and low sidelobe characteristics.

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Compact Broadband Absorptive Bandstop Filter Based on Microstrip

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

作者： Qi Lu Xianliang Wu Chao Wang Key Lab of Intelligent Computing & Signal Processing Ministry of Education AnHui University AnHui University Heifei China AnHui 230000 China

A compact broadband absorptive band rejection filter (ABSF) based on microstrip line is proposed. The filter consists of a pair of coupled wires and is loaded with a resistor. The coupling line of the filter has an open stub. The transmission zero point can be adjusted by adjusting the width of the coupling line. The center frequency can be adjusted by changing the length of the intermediate coupling line. Absorption performance can be achieved through loaded resistors. In this way, the signal not input in the filter will be dissipated in the filter and will not be reflected. When F0=3.8 GHz, 26% of the 20 dB bandwidth covers 3.15 GHz-4.2 GHz, the rejection depth of the stopband is less than -40 dB, and the in-band absorption is less than -10 dB.

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RGB-T image saliency detection via collaborative graph learning

arXiv

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arXiv 2019年

作者： Tu, Zhengzheng Xia, Tian Li, Chenglong Wang, Xiaoxiao Ma, Yan Tang, Jin Key Lab 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

Image saliency detection is an active research topic in the community of computer vision and multimedia. Fusing complementary RGB and thermal infrared data has been proven to be effective for image saliency detection. In this paper, we propose an effective approach for RGB-T image saliency detection. Our approach relies on a novel collaborative graph learning algorithm. In particular, we take superpixels as graph nodes, and collaboratively use hierarchical deep features to jointly learn graph affinity and node saliency in a unified optimization framework. Moreover, we contribute a more challenging dataset for the purpose of RGB-T image saliency detection, which contains 1000 spatially aligned RGB-T image pairs and their ground truth annotations. Extensive experiments on the public dataset and the newly created dataset suggest that the proposed approach performs favorably against the state-of-the-art RGB-T saliency detection methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Estimation and Correction of I/Q Imbalance in Wideband Zero-IF Receiver

Estimation and Correction of I/Q Imbalance in Wideband Zero-...

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Information Technology and Computer Application (ITCA), International Conference on

作者： Shujie Guan Delin Cai Bin Wu Key Lab of Computing Intelligent and Signal Processing (Ministry of Education) Anhui University China Measurement and Control Communication R & D Center The 38th Research Institute of China Electronics Technology Group China

ISBN: (数字)9781728164946

ISBN: (纸本)9781728164953

In this paper, a time domain compensation method is proposed for in-phase (I)/quadrature (Q) mismatch occurring in a wideband Zero-IF radio architecture. First, training sequences are used for error estimation and the FIR filter are developed to reduce the imbalance between the IQ paths. Then, we perform channel equalization on the signal to solve the channel distortion still in the passband. The simulation results of the point-frequency continuous wave (CW) signal and the Radar Chirp signals (LFM) prove the effectiveness of the method. We have established a receiver system level hardware implement platform to test the practicality of the method. The test results show that the method can reduce the IRR by about 29dB.

关键词： Receivers Finite impulse response filters Wideband Time-domain analysis Hardware Radar OFDM

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Edge-guided non-local fully convolutional network for salient object detection

arXiv

引用

arXiv 2019年

作者： Tu, Zhengzheng Ma, Yan Li, Chenglong Tang, Jin Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China Institute of Physical Science and Information Technology Anhui University Hefei230601 China

Fully Convolutional Neural Network (FCN) has been widely applied to salient object detection recently by virtue of high-level semantic feature extraction, but existing FCN-based methods still suffer from continuous striding and pooling operations leading to loss of spatial structure and blurred edges. To maintain the clear edge structure of salient objects, we propose a novel Edge-guided Non-local FCN (ENFNet) to perform edge-guided feature learning for accurate salient object detection. In a specific, we extract hierarchical global and local information in FCN to incorporate non-local features for effective feature representations. To preserve good boundaries of salient objects, we propose a guidance block to embed edge prior knowledge into hierarchical feature maps. The guidance block not only performs feature-wise manipulation but also spatial-wise transformation for effective edge embeddings. Our model is trained on the MSRA-B dataset and tested on five popular benchmark datasets. Comparing with the state-of-the-art methods, the proposed method achieves the best performance on all datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

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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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