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Small P values may not yield robust findings:an example using REST-meta-PD

Science Bulletin 2021年第21期66卷 2148-2152,M0003页

作者： Xi-Ze Jia Na Zhao Hao-Ming Dong Jia-Wei Sun Marek Barton Roxana Burciu Nicolas Carrière Antonio Cerasa Bo-Yu Chen Jun Chen Stephen Coombes Luc Defebvre Christine Delmaire Kathy Dujardin Fabrizio Esposito Guo-Guang Fan Federica Di Nardo Yi-Xuan Feng Brett W.Fling Saurabh Garg Moran Gilat Martin Gorges Shu-Leong Ho Fay BHorak Xiao Hu Xiao-Fei Hu Biao Huang Pei-Yu Huang Ze-Juan Jia Christina Jones Jan Kassubek Lenka Krajcovicova Ajay Kurani Jing Li Qing Li Ai-Ping Liu Bo Liu Hu Liu Wei-Guo Liu Renaud Lopes Yu-Ting Lou Wei Luo Tara Madhyastha Ni-Ni Mao Grainne McAlonan Martin J.McKeown Shirley Pang Andrea Quattrone Irena Rektorova Alessia Sarica Hui-Fang Shang James M.Shine Priyank Shukla Tomas Slavicek Xiao-Peng Song Gioacchino Tedeschi Alessandro Tessitore David Vaillancourt Jian Wang Jue Wang Z.Jane Wang Lu-Qing Wei Xia Wu Xiao-Jun Xu Lei Yan Jing Yang Wan-Qun Yang Nai-Lin Yao De-Long Zhang Jiu-Quan Zhang Min-Ming Zhang Yan-Ling Zhang Cai-Hong Zhou Chao-Gan Yan Xi-Nian Zuo Mark Hallett Tao Wu Yu-Feng Zang Center for Cognition and Brain Disorders the Affiliated HospitalHangzhou Normal UniversityHangzhou 310015China Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou 311121China National Basic Science Data Center Beijing 100190China State Key Laboratory of Cognitive Neuroscience and Learning&McGovern Institute for Brain Research Beijing Normal UniversityBeijing 100875China School of Information and Electronics Technology Jiamusi UniversityJiamusi 154007China Neuroscience Program Central European Institute of TechnologyCEITECMasaryk UniversityBrno 62500Czech Republic Department of Applied Physiology and Kinesiology University of FloridaGainesville FL 32611USA Univ.Lille InsermCHU LilleU1172-LilNCog-Lille Neuroscience&CognitionLille F-59000France The Institute for Biomedical Research and Innovation National Research CouncilMangone CS 87050Italy Department of Radiology The First Affiliated Hospital of China Medical UniversityShenyang 110001China Department of Radiology Guangdong Provincial Hospital of Chinese MedicineGuangzhou 510120China Department of Medicine Surgery and DentistryScuola Medica SalernitanaUniversity of SalernoFiscianoSA 132-84084Italy Department of Advanced Medical and Surgery Sciences University of Campania“Luigi Vanvitelli”Caserta 81100Italy Eye Center of the 2nd Affiliated Hospital Medical College of Zhejiang UniversityHangzhou 310020China Zhejiang Provincial Key Lab of Ophthalmology Hangzhou 310020China Department of Health and Exercise Science Colorado State UniversityFort Collins CO 80523USA Pacific Parkinson’s Research Centre University of British ColumbiaVancouver BC V6E 2M6Canada Department of Medicine(Neurology)University of British Columbia Vancouver BC V6T 1B7Canada Brain and Mind Center The University of SydneySydney NSW 2006Australia Department of Neurology Ulm UniversityUlm 89081Germany Division of Neurology Department of MedicineQueen Mary HospitalUniversity of Hong KongHong Kong 999077China Department of

Thousands of resting state functional magnetic resonance imaging(RS-f MRI)articles have been published on brain *** precise localization of abnormal brain activity,a voxel-level comparison is *** of the large number of voxels in the brain,multiple comparison correction(MCC)must be performed to reduce false positive rates,and a smaller P value(usually including either liberal or stringent MCC)is widely recommended[1].

关键词：帕金森病 precise correction

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Triangle extension: Efficient localizability detection in wireless sensor networks

arXiv

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arXiv 2018年

作者： Wu, Hejun Ding, Ao Liu, Weiwei Li, Lvzhou Yang, Zheng Guangdong Key Laboratory of Big Data Analysis and Processing Department of Computer Science Sun Yat-sen University Guangzhou China Horizon Robotics Beijing China School of Software Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing China

Determining whether nodes can be localized, called localizability detection, is essential for wireless sensor networks (WSNs). This step is required for localizing nodes, achieving low-cost deployments, and identifying prerequisites in location-based applications. Centralized graph algorithms are inapplicable to a resource-limited WSN because of their high computation and communication costs, whereas distributed approaches may miss a large number of theoretically localizable nodes in a resource-limited WSN. In this paper, we propose an efficient and effective distributed approach in order to address this problem. Furthermore, we prove the correctness of our algorithm and analyze the reasons our algorithm can find more localizable nodes while requiring fewer known location nodes than existing algorithms, under the same network configurations. The time complexity of our algorithm is linear with respect to the number of nodes in a network. We conduct both simulations and real-world WSN experiments to evaluate our algorithm under various network settings. The results show that our algorithm significantly outperforms the existing algorithms in terms of both the latency and the accuracy of localizability *** Codes 68T40 Copyright © 2018, The Authors. All rights reserved.

关键词： Sensor nodes

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Varying control intensity of synchronized chaotic system with time delay

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Babatunde O. Onasanya Shiping Wen Yuming Feng Wei Zhang Ning Tang Adeleke T. Ademola Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Wanzhou Chongqing 404100 China Department of Mathematics University of Ibadan Ibadan Nigeria Australian AI Institute University of Technology Sydney Ultimo NSW 2007 Australia School of Computer Science and Engineering Chongqing Three Gorges University Wanzhou Chongqing 404100 China Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology Chongqing Three Gorges University Wanzhou Chongqing 404100 China School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Wanzhou Chongqing 404100 China Department of Mathematics Obafemi Awolowo University Post Code 220005 Ile-Ife Nigeria

Hyperchaotic system is a very useful tool in secure and encrypted communications. But situations arise when engineers and scientists seek to synchronize two hyperchaotic systems. This gives another (error) system. The goal is to minimize the error as much as can be in order to make one system look like the other by synchronization. This is a particularly challenging situation. In this paper, two hyperchaotic systems are synchronized by impulsive control. Also, the condition for uniform asymptotic stability of the synchronized error system was given. Finally, the simulation results to justify the reliability of this method is also presented.

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Research of simulation of creative stage scene based on the 3DGans technology

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Journal of information Hiding and Multimedia Signal processing 2018年第6期9卷 1430-1443页

作者： Zhang, Fuquan Ding, Gangyi Lin, Qing Xu, Lin Li, Zuoyong Li, Lijie Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University School of Computer Science & Technology Beijing Institute of Technology Room A9-301 Juyuan Jinshan JiyuanFuzhou350008 China School of Computer Science & Technology Beijing Institute of Technology Room 611 Software Building 5 South Zhongguancun Street Haidian Beijing100081 China College of Foreign Languages Minjiang University Room 303 Building A13 No. 15 Binzhou road Cangshan district JiyuanFuzhou350008 China Innovative Information Industry Research Institute Fujian Normal University Fuqing Brunch of Fujian Normal University Campus County No.1 Longjiang Road FuqingFuzhou350300 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University 200 Xi-yuan-gong Road Room B201 Administrative Building Minhou350121 China School of Computer Science & Technology Beijing Institute of Technology Room 613 Software Building 5 South Zhongguancun Street Haidian Beijing100081 China

Aiming to the feature of the modern stage scenery, combined with 3DGANs technology, we make research into the creation of creative stage scenes to rapidly display problem scene design, reduce the cost of stage design and the data error of virtual and actual simulated effect of the stage. We improve the data of the creative stage scene at the beginning of the design to increase efficiency of the rehearsal stage to enable the simulation scene more intuitive to the displaying of the stage scene and provide the basis for dynamic management of the stage. In this paper, simulation of virtual reality stage scene will be realized through 3DGANs technology. Firstly, the stage data will be extracted and the data of the creative stage scene will be modified. Secondly, the data of the registration of 3D surface image data on the creative scene in the data will be modified by using genetic algorithm. Finally, the simulation experiments will be conducted by using 3DGANs technology to generate creative stage scenes. The results indicated that simulation of the creative stage scene generated by 3DGANs technology would synchronize with the real-time stage effect in which multi-thread processing can improve processor utilization, shorten the reading time of the video image data, effectively reduce the switching time between different tasks, improve the amount of the amount of throughput and concurrency system and to offer a key support for virtual simulation and dynamic management of stage creative scene based on the 3DGANs technology. © 2018, Ubiquitous International. All rights reserved.

关键词： Genetic algorithms

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FPGA demonstration for WLAN-OFDM baseband receiver

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Journal of information Hiding and Multimedia Signal processing 2018年第5期9卷 1281-1292页

作者： Wu, Chih-Feng Shiue, Muh-Tian Sung, Tien-Wen Pan, Jeng-Shyang School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China Department of Electrical Engineering National Central University Taoyuan32001 Taiwan College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

The FPGA demonstration for WLAN-OFDM baseband receiver is presented in this paper. In this OFDM baseband receiver, the CORDIC computing is employed to realize various arithmetics of baseband signal process and then to reduce the computational complexity of baseband receiver. For a large open office channel with 100 ns RMS delay spread, the measured EVMs of WLAN-OFDM baseband receiver are −13.3, −16.6, −25.4 and −31.0 dB for BPSK, QPSK, 16-QAM and 64-QAM, respectively. Finally, the physical design of WLAN-OFDM baseband receiver with 0.18 µm 1P6M CMOS technology indicates that the core area and power consumption are 1.2 mm2 (1094 µm × 1092 µm) and 33.2 mW, respectively, with supply voltage of 1.8 V and operating clock of 40 MHz. © 2018, Ubiquitous International. All rights reserved.

关键词： Signal to noise ratio

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The classification and retrieval of the image affective semantics based on integration of multi features and svm

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Journal of information Hiding and Multimedia Signal processing 2018年第4期9卷 864-873页

作者： Chen, Hui Xu, Lin Zhang, Fu Quan College of Electronics and Information Science Fujian Jiangxia University Room 4#608Jincheng Garden Xindian Fuzhou350012 China Innovative Information Industry Research Institute Fujian Normal University Fuqing Brunch of Fujian Normal University Campus County 1# Longjiang Road Fuqing Fuzhou350300 China School of Computer Science & Technology Beijing Institute of Technology Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Room A9#301 Juyuan Jinshan Jiyuan Fuzhou350008 China

Images contain rich emotions. The traditional image classification and retrieval methods mainly classify images according to the bottom visual features of the images. Most of them ignore the influence and effect of emotions. The image affective classification and retrieval method in this paper combines the bottom visual features with the high-level affective semantics. Aiming at the insufficiency of the retrieval based on the single bottom visual features, it combines the bottom image color feature with the shape feature, and establishes the image feature space. Aiming at the semantic gap between low-level visual features and high-level semantics of the image, it establishes the emotional semantic space through cognitive psychology experiments and factor analysis. The method in this paper uses SVM to fill the semantic blanks to achieve the affective semantics annotation of the image, maps feature space to the emotional space of the image to realize the classification and retrieval of the image affective semantics. The result of the experiment shows that this method improves the efficiency and performance of image retrieval, and provides an effective solution to the affective semantics research of images. © 2018, Ubiquitous International. All rights reserved.

关键词： Semantics

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Learning knowledge representation across knowledge graphs 31

Learning knowledge representation across knowledge graphs

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31st AAAI Conference on Artificial Intelligence, AAAI 2017

作者： Cai, Pengshan Li, Wei Feng, Yansong Wang, Yuanzhuo Jia, Yantao CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Science Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing China Institute of Computer Science and Technology Peking University Beijing China

ISBN: (纸本)9781577357865

Distributed knowledge representation learning (KRL) methods encode both entities and relations in knowledge graphs (KG) in a lower-dimensional semantic space, which model relatively dense knowledge graphs well and greatly improve the performance of knowledge graph completion and knowledge reasoning. However, existing KRL methods including Trans(E, H, R, D and Sparse) hardly obtain comparative performances on sparse KGs where most of entities and relations have very low frequencies. Furthermore, all existing methods target at KRL on one knowledge graph independently. The embeddings of different KGs are independent with each other. In this paper, we propose a novel cross-knowledge- graph (cross-KG) KRL method which learns embeddings for two different KGs simultaneously. Through projecting semantic related entities and relations in two KGs to a uniform semantic space, our method could learn better embeddings for sparse KGs by incorporating information from another relatively larger and denser KG. The learned embeddings are also helpful for downstream cross-KGs or cross-linguals tasks like ontology alignment. The experiment results show that our method could significandy outperform corresponding baseline methods on knowledge graph completion on single KG and cross-KG entity prediction and mapping tasks. © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Knowledge graph

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DEEP SEMANTIC SEGMENTATION OF AERIAL IMAGERY BASED ON MULTI-MODAL DATA

DEEP SEMANTIC SEGMENTATION OF AERIAL IMAGERY BASED ON MULTI-...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Kaiqiang Chen Kun Fu Xian Sun Michael Weinmann Stefan Hinz Boris Jutzi Martin Weinmann Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of Sciences Beijing P.R. China Institute of Computer Science II University of Bonn Bonn Germany Institute of Photogrammetry and Remote Sensing Karlsruhe Institute of Technology Karlsruhe Germany

In this paper, we focus on the use of multi-modal data to achieve a semantic segmentation of aerial imagery. Thereby, the multi-modal data is composed of a true orthophoto, the Digital Surface Model (DSM) and further representations derived from these. Taking data of different modalities separately and in combination as input to a Residual Shuffling Convolutional Neural Network (RSCNN), we analyze their value for the classification task given with a benchmark dataset. The derived results reveal an improvement if different types of geometric features extracted from the DSM are used in addition to the true orthophoto.

关键词： Semantics Image segmentation Feature extraction Labeling Vegetation mapping Buildings Benchmark testing

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A Trend Forecast of Import and Export Trade Total Volume based on LSTM

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IOP Conference Series: Materials Science and Engineering 2019年第1期646卷

作者： Qinglin Qu Zhao Li Juanjuan Tang Shiwei Wu Ruishuang Wang Shandong Business Information Center Qilu University of Technology (Shandong Academy of Sciences) Shandong Computer Science Center (National Supercomputer Center in Jinan) Shandong Provincial Key Laboratory of Computer Networks Big Data Engineering Technology Research Center of E-Government Shandong

The monthly import and export data are usually with the challenging characteristics including large scale, nonlinear and hard to fit, leading to their development trends can be hardly predicted. To tackle this problem, we propose to leverage the LSTM-based recurrent neural network to forecast the development trend in this paper. We demonstrate the effectiveness of our approach based on the monthly import and export data of Shandong Province from January 2001 to June 2018. In particular, we achieve the MSE score of 124.39, which outperforms the traditional time series model less than 12.2%.

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Predicting commercial activeness over urban big data

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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2017年第3期1卷 1-20页

作者： Yang, Su Wang, Minjie Wang, Wenshan Sun, Yi Gao, Jun Zhang, Weishan Zhang, Jiulong Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University 825 Zhangheng Rd Shanghai201203 China Department of Software Engineering China University of Petroleum Qingdao266580 China School of Computer Science Xian University of Technology Xian710048 China

This study aims at revealing how commercial hotness of urban commercial districts (UCDs) is shaped by social contexts of surrounding areas so as to render predictive business planning. We define social contexts for a given region as the number of visitors, the region functions, the population and buying power of local residents, the average price of services, and the rating scores of customers, which are computed from heterogeneous data including taxi GPS trajectories, point of interests, geographical data, and user-generated comments. Then, we apply sparse representation to discover the impactor factor of each variable of the social contexts in terms of predicting commercial activeness of UCDs under a linear predictive model. The experiments show that a linear correlation between social contexts and commercial activeness exists for Beijing and Shanghai based on an average prediction accuracy of 77.69% but the impact factors of social contexts vary from city to city, where the key factors are rich life services, diversity of restaurants, good shopping experience, large number of local residents with relatively high purchasing power, and convenient transportation. This study reveals the underlying mechanism of urban business ecosystems, and promise social context-aware business planning over heterogeneous urban big data. © 2017 Association for Computing Machinery.

关键词： Ecosystems

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