检索结果-内蒙古大学图书馆

arXiv 2017年

作者： Yang, Liu Zamani, Hamed Zhang, Yongfeng Guo, Jiafeng Bruce Croft, W. Center for Intelligent Information Retrieval University of Massachusetts Amherst AmherstMA United States CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

The recent boom of AI has seen the emergence of many human-computer conversation systems such as Google Assistant, Microsoft Cortana, Amazon Echo and Apple Siri. We introduce and formalize the task of predicting questions in conversations, where the goal is to predict the new question that the user will ask, given the past conversational context. This task can be modeled as a "sequence matching" problem, where two sequences are given and the aim is to learn a model that maps any pair of sequences to a matching probability. Neural matching models, which adopt deep neural networks to learn sequence representations and matching scores, have attracted immense research interests of information retrieval and natural language processing communities. In this paper, we first study neural matching models for the question retrieval task that has been widely explored in the literature, whereas the effectiveness of neural models for this task is relatively unstudied. We further evaluate the neural matching models in the next question prediction task in conversations. We have used the publicly available Quora data and Ubuntu chat logs in our experiments. Our evaluations investigate the potential of neural matching models with representation learning for question retrieval and next question prediction in conversations. Experimental results show that neural matching models perform well for both tasks. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting

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Building Extraction from High-Resolution Remotely Sensed Imagery Based on Multi-subgraph Matching

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Journal of the Indian Society of Remote Sensing 2018年第12期46卷 2003-2013页

作者： Wenzao Shi Zhengyuan Mao Jinqing Liu Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application College of Photonic and Electronic Engineering Fujian Normal University Fuzhou People’s Republic of China Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Provincial Key Laboratory of Photonics Technology Fujian Normal University Fuzhou People’s Republic of China Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education Fuzhou University Fuzhou People’s Republic of China National Engineering Research Centre of Geospatial Information Technology Fuzhou University Fuzhou People’s Republic of China Spatial Information Engineering Research Centre of Fujian Province Fuzhou University Fuzhou People’s Republic of China

Building extraction is still a difficult issue in the field of remote sensing. In order to extract the buildings with similar structures efficiently, an algorithm based on multi-subgraph matching is proposed using only the panchromatic high-resolution remotely sensed imagery (RSI). Firstly, scale-invariant feature transform feature is detected within both RSI and building template, and the corresponding graphs are constructed. Then, binary matching rules are defined to reconstruct the graphs to reduce the complexity. At last, according to the homogeneity of the building top, disconnected subgraphs are isolated from the reconstructed graphs. To improve the algorithm accuracy, the matched subgraphs are optimized on the basis of the differences in the structure and size. For verifying the validity of the proposed method, nine representatives are chosen from GF-2 images covering Guangzhou, China. Experimental results show that the precision and recall of the proposed method are 97.73% and 87.16%, respectively, and its overall performance F1 is higher than the three other similar methods.

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WISERNet: Wider Separate-then-reunion network for Steganalysis of Color Images

arXiv

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arXiv 2018年

作者： Zeng, Jishen Tan, Shunquan Liu, Guangqing Li, Bin Huang, Jiwu Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Guangdong Key Lab. of Intelligent Info. Processing and Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China Peng Cheng Laboratory Shenzhen518052 China

Until recently, deep steganalyzers in spatial domain have been all designed for gray-scale images. In this paper, we propose WISERNet (the wider separate-then-reunion network) for steganalysis of color images. We provide theoretical rationale to claim that the summation in normal convolution is one sort of "linear collusion attack" which reserves strong correlated patterns while impairs uncorrelated noises. Therefore in the bottom convolutional layer which aims at suppressing correlated image contents, we adopt separate channel-wise convolution without summation instead. Conversely, in the upper convolutional layers we believe that the summation in normal convolution is beneficial. Therefore we adopt united normal convolution in those layers and make them remarkably wider to reinforce the effect of "linear collusion attack". As a result, our proposed wide-and-shallow, separate-then-reunion network structure is specifically suitable for color image steganalysis. We have conducted extensive experiments on color image datasets generated from BOSSBase raw images and another large-scale dataset which contains 100,000 raw images, with di_erent demosaicking algorithms and downsampling algorithms. The experimental results show that our proposed network outperforms other state-of-the-art color image steganalytic models either hand-crafted or learned using deep networks in the literature by a clear margin. Specifically, it is noted that the detection performance gain is achieved with less than half the complexity compared to the most advanced deeplearning steganalyzer as far as we know, which is scarce in the literature. Copyright © 2018, The Authors. All rights reserved.

关键词： Steganography

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Extraction of Interactions of Genes2Genes Related to Breast Cancer

Extraction of Interactions of Genes2Genes Related to Breast ...

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International Conference on Software Engineering Research, Management and Applications (SERA)

作者： Lejun Gong Daoyu Huang Shixing Sun Zhihong Gao Chuandi Pan Ronggen Yang Yongmin Li Geng Yang Jiangsu Key Lab of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Zhejiang Engineering Research Center of Intelligent Medicine The First Affiliated Hospital of Wenzhou Medical University Wenzhou China College of Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing China

ISBN: (纸本)9781538658871

Breast cancer is the most prevalent disease to females in the worldwide. Its pathology remains unclear. Genetics factors is the ways to understand the molecular mechanism. This paper proposed a computational approach to explore the interactions of genes2genes related to breast cancer. We first defined the interactions of genes2genes, and described the representation of interactions of genes2genes. Using the experimental dataset, we implemented the proposed approach for extracting the interactions of genes2genes. Moreover, we also represented the interactions of genes2genes in two forms: relationship matrix and network visualization. By manual analysis, we extracted the interactions of top 10 genes2genes is related to breast cancer, which show the approach is promising for studying molecular mechanism related to breast cancer.

关键词： Breast cancer Pipelines data mining Manuals Genetics

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Sparse Word Embeddings Using 1 Regularized Online Learning

Sparse Word Embeddings Using 1 Regularized Online Learning

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Sun, Fei Guo, Jiafeng Lan, Yanyan Xu, Jun Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

Recently, Word2Vec tool has attracted a lot of interest for its promising performances in a variety of natural language processing (NLP) tasks. However, a critical issue is that the dense word representations learned in Word2Vec are lacking of interpretability. It is natural to ask if one could improve their interpretability while keeping their performances. Inspired by the success of sparse models in enhancing interpretability, we propose to introduce sparse constraint into Word2Vec. Specifically, we take the Continuous Bag of Words (CBOW) model as an example in our study and add the l regularizer into its learning objective. One challenge of optimization lies in that stochastic gradient descent (SGD) cannot directly produce sparse solutions with 1 regularizer in online training. To solve this problem, we employ the Regularized Dual Averaging (RDA) method, an online optimization algorithm for regularized stochastic learning. In this way, the learning process is very efficient and our model can scale up to very large corpus to derive sparse word representations. The proposed model is evaluated on both expressive power and interpretability. The results show that, compared with the original CBOW model, the proposed model can obtain state-of-the-art results with better interpretability using less than 10% non-zero elements. © 2016 International Joint Conferences on Artificial Intelligence. All rights reserved.

关键词： Optimization

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ChaLearn looking at people: IsoGD and ConGD large-scale RGB-D gesture recognition

arXiv

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arXiv 2019年

作者： Wan, Jun Lin, Chi Wen, Longyin Li, Yunan Miao, Qiguang Escalera, Sergio Anbarjafari, Gholamreza Guyon, Isabelle Guo, Guodong Li, Stan Z. National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China JD Finance Mountain ViewCA United States University of Southern California Los AngelesCA90089-0911 United States School of Computer Science and Technology Xidian University & Xi'an Key Laboratory of Big Data and Intelligent Vision 2nd South Taibai Road Xi'an710071 China Universitat de Barcelona Computer Vision Center Spain iCV Lab Institute of Technology University of Tartu Estonia Faculty of Engineering Hasan Kalyoncu University Gaziantep Turkey Institute of Digital Technologies Loughborough University London United Kingdom ChaLearn United States University Paris-Saclay France institute of Deep Learning Baidu Research National Engineering Laboratory for Deep Learning Technology and Application China

The ChaLearn large-scale gesture recognition challenge has been run twice in two workshops in conjunction with the International Conference on Pattern Recognition (ICPR) 2016 and International Conference on Computer Vision (ICCV) 2017, attracting more than 200 teams around the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. This paper describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. We discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition, and provide a detailed analysis of the current state-of-the-art methods for large-scale isolated and continuous gesture recognition based on RGB-D video sequences. In addition to recognition rate and mean jaccard index (MJI) as evaluation metrics used in our previous challenges, we also introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) baseline method, determining the video division points based on the skeleton points extracted by convolutional pose machine (CPM). Experiments demonstrate that the proposed Bi-LSTM outperforms the state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

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Building area extraction from the high spatial resolution remote sensing imagery

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Earth science Informatics 2018年第1期12卷 19-29页

作者： Shi, Wenzao Mao, Zhengyuan Liu, Jinqing Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application Fujian Normal University Fuzhou People’s Republic of China Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Provincial Key Laboratory for Photonics Technology Fujian Normal University Fuzhou People’s Republic of China College of Photonic and Electronic Engineering Fujian Normal University Fuzhou China Key Lab of Spatial Data Mining & Information Sharing of Ministry of Education Fuzhou University Fuzhou People’s Republic of China National Engineering Research Centre of Geospatial Information Technology Fuzhou University Fuzhou People’s Republic of China Spatial Information Engineering Research Centre of Fujian Province Fuzhou University Fuzhou People’s Republic of China

An approach to building area extraction from high-resolution remote sensing imagery is proposed based on the local gradient orientation density function (LGODF) and the bimodal density function (BDF). Firstly, the LGODF is calculated by moving the window of 35 × 35 based on the gradient magnitude and the gradient direction of each pixel in the image. Then, the BDF is obtained by multiplying the movable bimodal Gaussian mixture function and LGODF in each window. Finally, peaks with difference of 90° are searched for in the BDF, and the central point of the corresponding windows are determined as building pixels. For the validity of the proposed method, seven representative sub-images from PLEIADES images covering Shenzhen China are selected. Experimental results reveal that the precision achieve 94.08% and the recall up to 96.70%.

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Deterministic realization of collective measurements via photonic quantum walks

Deterministic realization of collective measurements via pho...

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第十八届全国量子光学学术会议

作者：侯志博 Jun-Feng Tang Yuan Yuan Kang-Da Wu Guo-Yong Xiang Chuan-Feng Li Guang-Can Guo Jiangwei Shang Huangjun Zhu Jian Li 中科大 Key Laboratory of Quantum Information University of Science and Technology of ChinaCAS Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Naturwissenschaftlich-Technische Fakult?t Universit?t Siegen Beijing Key Laboratory of Nanophotonics and Ultrafine Optoelectronic Systems School of PhysicsBeijing Institute of Technology Institute for Theoretical Physics University of Cologne Department of Physics and Center for Field Theory and Particle Physics Fudan University State Key Laboratory of Surface Physics Fudan University Collaborative Innovation Center of Advanced Microstructures Institute of Signal Processing Transmission Nanjing University of Posts and Telecommunications Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and TelecommunicationsMinistry of Education

Collective measurements on identically prepared quantum systems can extract more information than local measurements, thereby enhancing information-processing efficiency. Although this nonclassical phenomenon has been known for two decades, it has remained a challenging task to demonstrate the advantage of collective measurements in experiments. Here, we introduce a general recipe for performing deterministic collective measurements on two identically prepared qubits based on quantum walks. Using photonic quantum walks, we realize experimentally an optimized collective measurement with fidelity 0.9946 without post selection. As an application, we achieve the highest tomographic efficiency in qubit state tomography to date. Our work offers an effective recipe for beating the precision limit of local measurements in quantum state tomography and metrology. In addition, our study opens an avenue for harvesting the power of collective measurements in quantum information processing and for exploring the intriguing physics behind this power.

关键词： Deterministic realization of collective measurements via photonic quantum walks

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STATE OF THE CLIMATE IN 2021

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BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 2022年第8期103卷 S1-S1页

作者： Blunden, Jessica NOAA/NESDIS National Centers for Environmental Information Asheville NC United States NOAA NESDIS National Centers for Environmental Information Silver Spring MD United States Met Office Hadley Centre Exeter United Kingdom Cooperative Institute for Satellite Earth System Studies North Carolina State University Asheville NC United States Communications and Outreach NOAA/NESDIS National Centers for Environmental Information Asheville NC United States Innovative Consulting and Management Services LLC NOAA/NESDIS National Centers for Environmental Information Asheville NC United States European Centre for Medium-Range Weather Forecasts Reading United Kingdom CMNS-Earth System Science Interdisciplinary Center University of Maryland College Park MD United States Department of Meteorology National Centre for Earth Observation University of Reading Reading United Kingdom Department of Atmospheric and Planetary Science Hampton University Hampton VA United States National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Life and Environmental Sciences Osaka Prefecture University Osaka Sakai Japan University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder CO United States Centro de Investigaciones sobre Desertificación Spanish National Research Council (CIDE CSIC-UV-Generalitat Valenciana) Climate Atmosphere and Ocean Laboratory (Climatoc-Lab) Moncada Valencia Spain Instituto de Geografía Pontificia Universidad Católica de Valparaíso Valparaíso Chile Climatic Research Unit School of Environmental Sciences University of East Anglia Norwich United Kingdom Joint Research Centre of the European Commission Ispra Italy GloH2O Almere Netherlands University of Reading Reading United Kingdom School of Engineering Newcastle University Newcastle-upon-Tyne United Kingdom Université de Paris Cité Institut de physique du globe de Paris CNRS IGN Paris

Editors note: For easy download the posted pdf of the State of the Climate in 2021 is a low-resolution file. A high-resolution copy of the report is available by clickinghere. Please be patient as it may take a few mi... 详细信息

关键词： Greenhouse gases

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Running Time Analysis of the (1+1)-EA for OneMax and LeadingOnes under Bit-wise Noise

arXiv

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arXiv 2017年

作者： Qian, Chao Bian, Chao Jiang, Wu Tang, Ke Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Hefei230027 China Shenzhen Key Lab of Computational Intelligence Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

In many real-world optimization problems, the objective function evaluation is subject to noise, and we cannot obtain the exact objective value. Evolutionary algorithms (EAs), a type of general-purpose randomized optimization algorithm, have been shown to be able to solve noisy optimization problems well. However, previous theoretical analyses of EAs mainly focused on noise-free optimization, which makes the theoretical understanding largely insufficient for the noisy case. Meanwhile, the few existing theoretical studies under noise often considered the one-bit noise model, which flips a randomly chosen bit of a solution before evaluation;while in many realistic applications, several bits of a solution can be changed simultaneously. In this paper, we study a natural extension of one-bit noise, the bit-wise noise model, which independently flips each bit of a solution with some probability. We analyze the running time of the (1+1)-EA solving OneMax and LeadingOnes under bit-wise noise for the first time, and derive the ranges of the noise level for polynomial and super-polynomial running time bounds. The analysis on LeadingOnes under bit-wise noise can be easily transferred to one-bit noise, and improves the previously known results. Since our analysis discloses that the (1+1)-EA can be efficient only under low noise levels, we also study whether the sampling strategy can bring robustness to noise. We prove that using sampling can significantly increase the largest noise level allowing a polynomial running time, that is, sampling is robust to noise. Copyright © 2017, The Authors. All rights reserved.

关键词： Computational complexity

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