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Extracting Chinese polarity shifting patterns from massive text corpora

Lingua Sinica 2016年第1期2卷 1-22页

作者： Ge Xu Chu-Ren Huang Department of Computer Science Minjiang University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Internet Innovation Research Center of Humanities and Social Sciences Base of Colleges and Universities in Fujian Fuzhou China Department of Chinese and Bilingual Studies The Hong Kong Polytechnic University Kowloon Hong Kong

In sentiment analysis, polarity shifting means shifting the polarity of a sentiment clue that expresses emotion, evaluation, etc. Compared with other natural language processing (NLP) tasks, extracting polarity shifting patterns from corpora is a challenging one because the methods used to shift polarity are flexible, which often invalidates fully automatic approaches. In this study, which aimed to extract polarity shifting patterns that inverted, attenuated, or canceled polarity, we used a semi-automatic approach based on sequence mining. This approach greatly reduced the cost of human annotating, while covering as many frequent polarity shifting patterns as possible. We tested this approach on different domain corpora and in different settings. Three types of experiments were performed and the experimental results were analyzed, which will be reported in this paper.

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Voxel based treatment prediction using diffusion anisotropy indices and spatial information in Glioblastoma Multiform tumor

Voxel based treatment prediction using diffusion anisotropy ...

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Iranian Conference of Biomedical Engineering (ICBME)

作者： Hadi Sabahi Hamid Soltanian-Zadeh Lisa Scarpace Tom Mikkelsen Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Hospital Detroit MI USA Hermelin Brain Tumor Research Center Neurosurgery Department Henry Ford Health System Detroit MI USA

In this paper, we propose a method to predict the outcome of Bevacizumab therapy on Glioblastoma Multiform (GBM) tumors. The method uses diffusion anisotropy indices (DAI) and spatial information to predict the treatment response of each tumor voxel. These DAIs are Fractional Anisotropy, Mean Diffusivity, Relative Anisotropy, and Volume Ratio, extracted from Diffusion Tensor Imaging (DTI) data before treatment. The spatial information is considered as the distance of each tumor voxel from the tumor center, extracted from pre-treatment post-contrast T1-weighted Magnetic Resonance images (pc-T1-MRI). DAIs and spatial information of each tumor voxel are considered as feature vector. DTI and pc-T1-MRI are gathered before and after the treatment of seven GBM patients. First, DAIs of all brain voxels and the distance of each tumor voxel from the tumor center are calculated. Second, the method registers pre-treatment DAI maps and post-treatment pc-T1-MRI to pre-treatment pc-T1-MRI. Next, the tumor is segmented using thresholding technique from pc-T1-MRI. Then, Gd-enhanced voxels of the pre- and post-treatment pc-T1-MRI are compared to label the feature vectors. Three classifiers were evaluated, including Support Vector Machine, K-Nearest Neighbor, and Artificial Neural Network. Classification results show a preference for K-Nearest Neighbor based on well-established performance measures.

关键词： Tumors Anisotropic magnetoresistance Diffusion tensor imaging Support vector machines Feature extraction Artificial neural networks

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Research on Fast Adaptive Transmission Models for International Inland Port Based on Edge Intelligence

Research on Fast Adaptive Transmission Models for Internatio...

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IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)

作者： Liu Yiwen Zhu Zhirong Tangyan Wen Wenkan Peng Xiaoning Shi Yuanquan School of Computer and Artificial Intelligence Huaihua University Huaihua Hunan P. R. China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing Application in Hunan Province Universities Huaihua Hunan P. R. China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Hunan P. R. China

In this paper, we propose a logistics International Inland Port resource allocation method based on intelligent edge scheduling, and we do a reasonable pre-allocation of logistics resources by building an edge network in the urban system. By using multiple integrated learning models and regional prevalence classification algorithms, the distribution demand of each sub-distribution point is predicted. The proposed method is able to cope with uncertainties such as high distribution volume, variable distribution situations or long transportation distances. We use the HuaiHua International Inland Port as the simulation object, and the simulation results show that the proposed method has the highest efficiency in logistics distribution and is still highly adaptive in emergency situations.

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Finite-size scaling of geometric renormalization flows in complex networks

arXiv

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arXiv 2020年

作者： Chen, Dan Su, Housheng Wang, Xiaofan Pan, Gui-Jun Chen, Guanrong School of Artificial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan430074 China Department of Automation Shanghai University Shanghai200072 China Faculty of Physics and Electronic Science Hubei University Wuhan430062 China Department of Electrical Engineering City University of Hong Kong Hong Kong Hong Kong

Recently, the concept of geometric renormalization group provides a good approach for studying the structural symmetry and functional invariance of complex networks. Along this line, we systematically investigate the finite-size scaling of structural and dynamical observables in geometric renormalization flows of synthetic and real evolutionary networks. Our results show that these observables can be well characterized by a certain scaling function. Specifically, we show that the critical exponent implied by the scaling function is independent of these observables but only depends on the small-world properties of the network, namely, all networks located in the small-world phase have a uniform scaling exponent, while those located in the non-small-world phase and in their critical regions have another uniform scaling. More importantly, we perform extensive experiments on real evolutionary networks with small-world characteristics, and our results show that these observables also have uniform scaling in their geometric renormalization flows. Therefore, in a sense this exponent can be used as an effective measure for classifying universal small-world and non-small-world network models. © 2020, CC BY-SA.

关键词： Geometry

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NMF: an Efficient Method for Detecting the Fallen Leaves Using Cleaning Robots on the Road

NMF: an Efficient Method for Detecting the Fallen Leaves Usi...

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IEEE International Conference on Robotics and Biomimetics

作者： Yanzi Miao Zongwei Zhang Hesheng Wang Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China China University of Mining and Technology Xuzhou China Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

ISBN: (纸本)9781665405362

The improvement of urban greening level makes sanitary work more complicated and difficult, especially, for the seasonal leaf cleaning on roads in the park, campus etc.. With the rapid development of computer vision, it is a feasible and innovative way to detect and clean the fallen leaves effectively with intelligent robot. However, since the irregular shape and size, uneven distribution of fallen leaves and complex outdoor conditions, especially the densely stacked leaves, even current advanced object detection algorithm has no satisfactory effect on detection of fallen leaves. To deal with the dense leaves detection problem and improve navigation efficiency, we propose a Non-Maximum Fusion(NMF) algorithm. NMF scales the high-confidence box with the pre-defined δ to fuse intersecting and adjacent boxes instead of supressing these boxes. The experiments on the fallen leaves data set shows that NMF improves the fallen leaves detection coverage significantly and the coverage reaches to 95%. Also, NMF greatly reduces the number of goal nodes for path planning. Since NMF functions at the back end of detector to process detection boxes without training, it can be intergrated into any fallen leaves detection pipeline easily.

关键词： Training Navigation Shape Roads Pipelines Green products Object detection

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A non commutative generalization BL-rings

arXiv

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arXiv 2021年

作者： Tsafack, Surdive Atamewoue Tchinda, Arnaud Fobasso Feng, Yuming Ndjeya, Selestin Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Wanzhou Chongqing404100 China Department of Mathematics University of Yaounde 1 Cameroon Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology Chongqing Three Gorges University Wanzhou Chongqing404100 China Department of Mathematics Higher Teacher Training College University of Yaounde 1 Cameroon

The purpose of this work is to extend the study of the commutative rings whose lattice of ideals can be a structure of BL-algebra as carry out by Heubo et al in 2018, to non commutative rings appointed in the work as pseudo BL-rings. We study and characterize rings whose ideals form a pseudo BL-algebra, we describe them in terms of their subdirectly irreductible factors. We obtain that these are (up to isomorphism) to a subring of a direct sums of unitary special primary rings and discrete valuation *** Codes 03B50, 06D99. © 2021, CC BY.

关键词： Algebra

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Wavelet sparse representation based beam-forming for ultrasound imaging: Theory and simulation

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Metallurgical and Mining Industry 2015年第9期7卷 777-781页

作者： Zhang, Qiong Shen, Minfen Li, Bin Cheng, Cheng College of Engineering Shantou University Shantou Guangdong China Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Techniques Shantou Guangdong China Shantou Polytechnic Shantou Guangdong China Shantou Institute of Ultrasonic Instruments Co. Ltd. Shantou Guangdong China Dept of Intelligent Multimedia Technology Research Center Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

In this paper, a wavelet sparse representation based beamforming is proposed to improve the resolution and contrast of plane wave emission ultrasound imaging. First, the received signals are described as the convolution of the target scatterers with the point spread function of the system. And then, the wavelet sparse representation model is deduced according to the fact that the target scatterers can be sparse represented in wavelet domain. The proposed algorithm was tested with simulated ultrasound data in plane wave emission. And the results demonstrated that the resolution was clearly improved and contrast ratio gains of 9.8 dB, 4.3 dB and 3.7 dB were obtained compared to delay-and-sum beamformer, minimum variance beamformer and phase coherence factor, respectively.

关键词： Optical transfer function

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Self-supervised denoising for multimodal structured illumination microscopy enables long-term super-resolution live-cell imaging

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PhotoniX 2024年第1期5卷 654-675页

作者： Xingye Chen Chang Qiao Tao Jiang Jiahao Liu Quan Meng Yunmin Zeng Haoyu Chen Hui Qiao Dong Li Jiamin Wu Department of Automation Tsinghua UniversityBeijing 100084China Research Institute for Frontier Science Beihang UniversityBeijing 100083China Institute for Brain and Cognitive Sciences Tsinghua UniversityBeijing 100084China National Laboratory of Biomacromolecules New Cornerstone Science LaboratoryCAS Center for Excellence in BiomacromoleculesInstitute of BiophysicsChinese Academy of SciencesBeijing 100101China College of Life Sciences University of Chinese Academy of SciencesBeijing 100049China Laboratory of Image Processing and Intelligent Control of Ministry of Education of China School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China

Detection noise significantly degrades the quality of structured illumination microscopy(SIM)images,especially under low-light *** supervised learning based denoising methods have shown prominent advances in eliminating the noise-induced artifacts,the requirement of a large amount of high-quality training data severely limits their *** we developed a pixel-realignmentbased self-supervised denoising framework for SIM(PRS-SIM)that trains an SIM image denoiser with only noisy data and substantially removes the reconstruction *** demonstrated that PRS-SIM generates artifact-free images with 20-fold less fluorescence than ordinary imaging conditions while achieving comparable super-resolution capability to the ground truth(GT).Moreover,we developed an easy-to-use plugin that enables both training and implementation of PRS-SIM for multimodal SIM platforms including 2D/3D and linear/nonlinear *** PRS-SIM,we achieved longterm super-resolution live-cell imaging of various vulnerable bioprocesses,revealing the clustered distribution of Clathrin-coated pits and detailed interaction dynamics of multiple organelles and the cytoskeleton.

关键词： Super-resolution microscopy Structured illumination microscopy Deep learning Self-supervised learning Live-cell imaging

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image Encryption Using 2D Sine-Piecewise Linear Chaotic Map

Image Encryption Using 2D Sine-Piecewise Linear Chaotic Map

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International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

作者： Yan-Ru Zhong Hua-Yi Liu Xi-Yan Sun Ru-Shi Lan Xiao-Nan Luo School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China School of Information and Communication Guilin University of Electronic Technology Guilin China Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin China

In this paper, a new image encryption algorithm is proposed by integrating Sine and piecewise linear chaotic maps. In order to realize the effect of encrypted image, the security key and 2D Sine-piecewise linear chaotic map (SPLCM) are used to encrypt the image by using random sequence and random matrix, and then using the replacement operation and the diffusion operation of the original image. The proposed image encryption algorithm is simple and practical, and the simulation results show that this algorithm is able to encrypt different types of digital images into unidentifiable random images. The security analysis also shows that this algorithm has a higher security level.

关键词： Encryption Two dimensional displays Digital images Chaotic communication

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Remote Sensing Novel View Synthesis with Implicit Multiplane Representations

arXiv

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arXiv 2022年

作者： Wu, Yongchang Zou, Zhengxia Shi, Zhenwei Image Processing Center School of Astronautics Beihang University Beijing100191 China Beijing Key Laboratory of Digital Media Beihang University Beijing100191 China State Key Laboratory of Virtual Reality Technology and Systems School of Astronautics Beihang University Beijing100191 China Department of Guidance Navigation and Control School of Astronautics Beihang University Beijing100191 China

Novel view synthesis of remote sensing scenes is of great significance for scene visualization, human-computer interaction, and various downstream applications. Despite the recent advances in computer graphics and photogrammetry technology, generating novel views is still challenging particularly for remote sensing images due to its high complexity, view sparsity and limited view-perspective variations. In this paper, we propose a novel remote sensing view synthesis method by leveraging the recent advances in implicit neural representations. Considering the overhead and far depth imaging of remote sensing images, we represent the 3D space by combining implicit multiplane images (MPI) representation and deep neural networks. The 3D scene is reconstructed under a self-supervised optimization paradigm through a differentiable multiplane renderer with multi-view input constraints. images from any novel views thus can be freely rendered on the basis of the reconstructed model. As a by-product, the depth maps corresponding to the given viewpoint can be generated along with the rendering output. We refer to our method as Implicit Multiplane images (ImMPI). To further improve the view synthesis under sparse-view inputs, we explore the learning-based initialization of remote sensing 3D scenes and proposed a neural network based Prior extractor to accelerate the optimization process. In addition, we propose a new dataset for remote sensing novel view synthesis with multi-view real-world google earth images. Extensive experiments demonstrate the superiority of the ImMPI over previous state-of-the-art methods in terms of reconstruction accuracy, visual fidelity, and time efficiency. Ablation experiments also suggest the effectiveness of our methodology design. Our dataset and code can be found at https://***/wyc-Chang/ImMPI Copyright © 2022, The Authors. All rights reserved.

关键词： Remote sensing

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