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A freeform dielectric metasurface modeling approach based on deep neural networks

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arXiv 2019年

作者： An, Sensong Zheng, Bowen Shalaginov, Mikhail Y. Tang, Hong Li, Hang Zhou, Li Ding, Jun Agarwal, Anuradha Murthy Rivero-Baleine, Clara Kang, Myungkoo Richardson, Kathleen A. Gu, Tian Hu, Juejun Fowler, Clayton Zhang, Hualiang Department of Electrical and Computer Engineering University of Massachusetts Lowell Lowell Massachusetts01854 United States Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge Massachusetts02139 United States Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200062 Lockheed Martin Corporation Orlando Florida32819 United States CREOL University of Central Florida Orlando Florida32816 United States

Metasurfaces have shown promising potentials in shaping optical wavefronts while remaining compact compared to bulky geometric optics devices. Design of meta-atoms, the fundamental building blocks of metasurfaces, relies on trial-and-error method to achieve target electromagnetic responses. This process includes the characterization of an enormous amount of different meta-atom designs with different physical and geometric parameters, which normally demands huge computational resources. In this paper, a deep learning-based metasurface/meta-atom modeling approach is introduced to significantly reduce the characterization time while maintaining accuracy. Based on a convolutional neural network (CNN) structure, the proposed deep learning network is able to model meta-atoms with free-form 2D patterns and different lattice sizes, material refractive indexes and thicknesses. Moreover, the presented approach features the capability to predict meta-atoms' wide spectrum responses in the timescale of milliseconds, which makes it attractive for applications such as fast meta-atom/metasurface on-demand design and optimization. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

GaitSet: Regarding gait as a set for cross-view gait recognition

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arXiv 2018年

作者： Chao, Hanqing He, Yiwei Zhang, Junping Feng, JianFeng Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Institute of Science and Technology for Brain-inspired Intelligence Fudan University Shanghai200433 China

As a unique biometric feature that can be recognized at a distance, gait has broad applications in crime prevention, forensic identification and social security. To portray a gait, existing gait recognition methods utilize either a gait template, where temporal information is hard to preserve, or a gait sequence, which must keep unnecessary sequential constraints and thus loses the flexibility of gait recognition. In this paper we present a novel perspective, where a gait is regarded as a set consisting of independent frames. We propose a new network named GaitSet to learn identity information from the set. Based on the set perspective, our method is immune to permutation of frames, and can naturally integrate frames from different videos which have been filmed under different scenarios, such as diverse viewing angles, different clothes/carrying conditions. Experiments show that under normal walking conditions, our single-model method achieves an average rank-1 accuracy of 95.0% on the CASIA-B gait dataset and an 87.1% accuracy on the OU-MVLP gait dataset. These results represent new state-of-the-art recognition accuracy. On various complex scenarios, our model exhibits a significant level of robustness. It achieves accuracies of 87.2% and 70.4% on CASIA-B under bag-carrying and coat-wearing walking conditions, respectively. These outperform the existing best methods by a large margin. The method presented can also achieve a satisfactory accuracy with a small number of frames in a test sample, e.g., 82.5% on CASIA-B with only 7 frames. The source code has been released at https://***/AbnerHqC/GaitSet. Copyright © 2018, The Authors. All rights reserved.

关键词： Gait analysis

CRWMS: Bipartite Network Embedding based on Constrained Random Walk and Mixed-Skipgram

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Journal of Physics: Conference Series 2020年第1期1642卷

作者： Yaling Ye Lina Ma Xia Zhang Qixuan Ni Yuyao Wang Zhan Bu Jiangsu Provincial Key Laboratory of E-Business Nanjing University of Finance and Economics Nanjing China WinGin Business-Intelligence Academy Nanjing Co. Ltd Nanjing China School of Artificial Intelligence Nanjing Vocational College of Information Technol ogy Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

A bipartite network is a basic representation model in recommendation systems, in which the explicit links (e.g., representing user-item rating information) only exist between heterogeneous nodes (e.g., users and items). Most traditional bipartite network embedding methods only consider the explicit network structure, but ignore semantic relations and rating information therein. To solve this deficiency, we designed a new bipartite network embedding approach based on the Constrained Random Walk and Mixed-Skipgram (CRWMS). Specifically, we propose a constrained random walk strategy that produces the mixed local sampling sequences, and further adopt the splitting operation to preserve both explicit and implicit structural information. Furthermore, we propose a novel and effective mixed-skipgram model to discern node representation vectors through joint training of explicit and implicit structural sequences. We compare CRWMS with five state-of-the-art network embedding methods on three real-world bipartite networks. Experiments show that our approach improves performance in edge classification, edge clustering, recommendation and link reconstruction tasks.

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A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data

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arXiv 2018年

作者： Wang, Jingang Tian, Junfeng Qiu, Long Li, Sheng Lang, Jun Si, Luo Lan, Man Alibaba Group IDST School of Computer Science and Software Engineering East China Normal University Network Technology Co. Ltd. Shanghai Key Laboratory of Multidimensional Information Processing China

It is a challenging and practical research problem to obtain effective compression of lengthy product titles for Ecommerce. This is particularly important as more and more users browse mobile E-commerce apps and more merchants make the original product titles redundant and lengthy for Search Engine Optimization. Traditional text summarization approaches often require a large amount of preprocessing costs and do not capture the important issue of conversion rate in E-commerce. This paper proposes a novel multi-task learning approach for improving product title compression with user search log data. In particular, a pointer network-based sequence-to-sequence approach is utilized for title compression with an attentive mechanism as an extractive method and an attentive encoder-decoder approach is utilized for generating user search queries. The encoding parameters (i.e., semantic embedding of original titles) are shared among the two tasks and the attention distributions are jointly optimized. An extensive set of experiments with both human annotated data and online deployment demonstrate the advantage of the proposed research for both compression qualities and online business values. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

A parallel k-means clustering algorithm based on redundance elimination and extreme points optimization employing MapReduce

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A parallel k-means clustering algorithm based on redundance ...

作者： Tang, Zhuo Liu, Kunkun Xiao, Jinbo Yang, Li Xiao, Zheng College of Information Science and Engineering Hunan University Hunan410082 China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410004 China

When facing massive statistical data, the k-means algorithm is very difficult to satisfy the need of data processing as it lacks an effective parallel mechanism. This paper proposes an improved k-means algorithm (IMR-KCA) to conduct clustering analysis based on medical data employing MapReduce computing framework. Through analyzing the defects of vast redundancy in the traditional k-means algorithms, a selection model is firstly proposed to simplify the computations with multiple clustering centers. Based on several proposed theorems, we prove the correctness of this selection model. Second, this paper provides a method to calculate the distances from extreme points to central points, and the original Euclidean distance is replaced with Manhattan distance. For this simplification, a group of theorems are proposed to prove the correctness. Next, we provide a group of implementation algorithms to complete the parallelism of the clustering computation employing the MapReduce framework. Finally, the experimental results illustrate that IMR-KCA is more reliable and efficient than the direct parallelization of the traditional clustering algorithms based on MapReduce. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： MapReduce

Hierarchical routing strategy for wireless sensor network

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Journal of information Hiding and Multimedia Signal processing 2018年第1期9卷 256-264页

作者： Pan, Jeng-Shyang Kong, Ling Ping Sung, Tien-Wen Chu, Shu-Chuan Roddick, John F. Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Information Science and Engineering Fujian University of Technology Fujian China Innovative Information Industry Research Center Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Chaoyang University of Technology Taichung Taiwan School of Computer Science Engineering and Mathematics Flinders University Australia

Wireless sensor networks (WSNs) are designed for a large scale monitoring applications such as military surveillance, medical treatment, environmental monitoring and industry management. In this network, usually hundreds or thousands of low-cost sensor nodes are deployed. These sensing nodes detect the events in the environment and pass an upstream message towards a Sink node. The Sink node is responsible for processing data those are collected by sensor nodes. However, bottleneck exists for WSN: these inner nodes whose positions are close to the Sink node run out of power much earlier than those outer nodes. Because those inner nodes not only perform sensing operation, but also replay data originated from other nodes. In this paper, GAHL routing scheme based on genetic algorithm and Harel method is proposed to solve the problem. The scheme finds the small number of center heads in the network. Those center heads play a role in balancing the workload of inner nodes. With the involvement of center head, a three-tier network is built. From the simulation experiment, the scheme shows a good performance compared to other well-known algorithms. We observe a decrease in the number of center heads, as well as an improvement in the stability of the scheme. © 2018, Ubiquitous International. All rights reserved.

关键词： Genetic algorithms

Learning epidemic threshold in complex networks by convolutional neural network

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arXiv 2019年

作者： Ni, Qi Kang, Jie Tang, Ming Liu, Ying Zou, Yong School of Communication and Electronic Engineering East China Normal University Shanghai200241 China School of Mathematical Sciences Shanghai Key Laboratory of PMMP East China Normal University Shanghai200241 China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China School of Computer Science Southwest Petroleum University Chengdu610500 China Big Data Research center University of Electronic Science and Technology of China Chengdu610054 China School of Physics and Electronic Science East China Normal University Shanghai200241 China

Deep learning has taken part in the competition since not long ago to learn and identify phase transitions in physical systems such as many body quantum systems, whose underlying lattice structures are generally regular as they’re in euclidean space. Real networks have complex structural features which play a significant role in dynamics in them, and thus the structural and dynamical information of complex networks can not be directly learned by existing neural network models. Here we propose a novel and effective framework to learn the epidemic threshold in complex networks by combining the structural and dynamical information into the learning procedure. Considering the strong performance of learning in Euclidean space, Convolutional Neural Network (CNN) is used and, with the help of confusion scheme, we can identify precisely the outbreak threshold of epidemic dynamics. To represent the high dimensional network data set in Euclidean space for CNN, we reduce the dimensionality of a network by using graph representation learning algorithms and discretize the embedded space to convert it into an image-like structure. We then creatively merge the nodal dynamical states with the structural embedding by multi-channel images. In this manner, the proposed model can draw the conclusion from both structural and dynamical information. A large number of simulations show a great performance in both synthetic and empirical network data set. Our end-to-end machine learning framework is robust and universally applicable to complex networks with arbitrary size and topology. Copyright © 2019, The Authors. All rights reserved.

关键词： Complex networks

CNN-based invertible wavelet scattering for the investigation of diffusion properties of the in vivo human heart in diffusion tensor imaging

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arXiv 2019年

作者： Deng, Zeyu Wang, Lihui Kuai, Zixiang Chen, Qijian Cheng, Xinyu Yang, Feng Yang, Jie Zhu, Yuemin Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province College of Computer Science and Technology Guizhou University Guiyang550025 China Imaging Center Harbin Medical University Cancer Hospital Harbin150081 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China University Lyon INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 LyonF-69621 France

In vivo diffusion tensor imaging (DTI) is a promising technique to investigate noninvasively the fiber structures of the in vivo human heart. However, signal loss due to motions remains a persistent problem in in vivo cardiac DTI. We propose a novel motion-compensation method for investigating in vivo myocardium structures in DTI with free-breathing acquisitions. The method is based on an invertible Wavelet Scattering achieved by means of Convolutional Neural Network (WSCNN). It consists of first extracting translation-invariant wavelet scattering features from DW images acquired at different trigger delays and then mapping the fused scattering features into motion-compensated spatial DW images by performing an inverse wavelet scattering transform achieved using CNN. The results on both simulated and acquired in vivo cardiac DW images showed that the proposed WSCNN method effectively compensates for motion-induced signal loss and produces in vivo cardiac DW images with better quality and more coherent fiber structures with respect to existing methods, which makes it an interesting method for measuring correctly the diffusion properties of the in vivo human heart in DTI under free breathing. Copyright © 2019, The Authors. All rights reserved.

关键词： Diffusion tensor imaging

Research on the Brain-inspired Cross-modal Neural Cognitive Computing Framework

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arXiv 2018年

作者： Liu, Yang Key Laboratory of Big Data Analysis and Processing of Henan province Intelligent Technology and Application Engineering Research Centre of Henan province College of Computer Science and Information Engineering Henan University Kaifeng475004 China

To address modeling problems of brain-inspired intelligence, this thesis is focused on researching in the semantic-oriented framework design for multimedia and multimodal information. The Multimedia Neural Cognitive Computing (MNCC) model was designed based on the nervous mechanism and cognitive architecture. Furthermore, the semantic-oriented hierarchical Cross-modal Neural Cognitive Computing (CNCC) framework was proposed based on MNCC model, and formal description and analysis for CNCC framework was given. It would effectively improve the performance of semantic processing for multimedia and cross-modal information, and has far-reaching significance for exploration and realization brain-inspired computing. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics