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Classification of Cognitive Ability of Healthy Elderly Individuals Using Resting-State Functional Connectivity Magnetic Resonance Imaging and An Extreme Learning Machine

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Research Square

Research Square 2021年

作者： Yang, Zekun Ge, Manling Abdelkader, Nasreddine Belkacem Fu, Xiaoxuan Zhang, Qirui Chen, Shenghua Xie, Chong Song, Zibo Chen, Chao State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300130 China Hebei Province Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability Hebei University of Technology Tianjin300130 China Department of Computer and Network Engineering College of Information Technology United Arab Emirates University Al Ain15551 United Arab Emirates Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Harvard Medical School CharlestownMA02129 United States Department of Medical Imaging Jinling Hospital Nanjing University School of Medicine General Hospital of Eastern Theater Nanjing210002 China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin300003 China

Purpose Quantitative determination of the correlation between cognitive ability and functional biomarkers in the elderly brain is essential. To identify biomarkers associated with cognitive performance in the elderly, this study combined an index model specific for resting-state functional connectivity (FC) magnetic resonance imaging (fcMRI) with a supervised machine learning method. Methods Performance scores on conventional cognitive test batteries and MRI data were obtained for 98 healthy elderly individuals and 90 healthy youth from two public databases. Based on the test scores, the elderly cohort was categorized into two groups: excellent and poor. An fcMRI index model was constructed for each elderly individual to determine the relative differences in FC among brain regions compared with that in the youth cohort. Brain areas sensitive to test scores could then be identified using the fcMRI indexes. To confirm the effectiveness of constructed model, the fcMRI indexes of these brain areas were used as feature matrix inputs for training an extreme learning machine. Classification accuracy was then tested in separate groups and confirmed by N-fold cross-validation. Results This learning study could effectively classify the cognitive status of healthy elderly individuals according to frontal lobe, temporal lobe, and parietal lobe FC values with a mean accuracy of 83.5%, which is substantially higher than that achieved using conventional correlation analysis. Conclusion This fcMRI classification study may facilitate early detection of age-related cognitive decline as well as help reveal the underlying pathological mechanisms. © 2021, CC BY.

关键词： Magnetic resonance imaging

Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for Multi-Label Classification

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arXiv 2019年

作者： Wu, Guoqiang Zheng, Ruobing Tian, Yingjie Liu, Dalian School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Computer Network Information Center Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge management Chinese Academy of Sciences Beijing100190 China Department of Basic Course Teaching Beijing Union University Beijing100101 China

Multi-label classification studies the task where each example belongs to multiple labels simultaneously. As a representative method, Ranking Support Vector Machine (Rank-SVM) aims to minimize the Ranking Loss and can also mitigate the negative influence of the class-imbalance issue. However, due to its stacking-style way for thresholding, it may suffer error accumulation and thus reduces the final classification performance. Binary Relevance (BR) is another typical method, which aims to minimize the Hamming Loss and only needs one-step learning. Nevertheless, it might have the class-imbalance issue and doesn’t take into account label correlations. To address the above issues, we propose a novel multi-label classification model, which joints Ranking support vector machineNovember 6, 2019 and Binary Relevance with robust Low-rank learning (RBRL). RBRL inherits the ranking loss minimization advantages of Rank-SVM, and thus overcomes the disadvantages of BR suffering the class-imbalance issue and ignoring the label correlations. Meanwhile, it utilizes the hamming loss minimization and one-step learning advantages of BR, and thus tackles the disadvantages of Rank-SVM including another thresholding learning step. Besides, a low-rank constraint is utilized to further exploit high-order label correlations under the assumption of low dimensional label space. Furthermore, to achieve nonlinear multi-label classifiers, we derive the kernelization RBRL. Two accelerated proximal gradient methods (APG) are used to solve the optimization problems efficiently. Extensive comparative experiments with several state-of-the-art methods illustrate a highly competitive or superior performance of our method RBRL. Copyright © 2019, The Authors. All rights reserved.

关键词： Classification (of information)

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SSRN

SSRN 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nannig China Guangxi Key Laboratory of Multimedia Communications and Network Technology China China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China College of Systems Engineering National University of Defense Technology Changsha China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGA also uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a high-quality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data tra

关键词： Profitability

Secrecy capacity bounds for visible light communications with signal-dependent noise

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arXiv 2021年

作者： Wang, Jin-Yuan Fu, Xian-Tao Wang, Jun-Bo Lin, Min Cheng, Julian Alouini, Mohamed-Slim Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Nanjing210003 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China College of Telecommunications & Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China National Mobile Communications Research Laboratory Southeast University Nanjing211111 China School of Engineering The University of British Columbia KelownaBCV1V 1V7 Canada Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In physical-layer security, one of the most fundamental issues is the secrecy capacity. The objective of this paper is to determine the secrecy capacity for an indoor visible light communication system consisting of a transmitter, a legitimate receiver and an eavesdropping receiver. In such a system, both signal-independent and signal-dependent Gaussian noises are considered. Under non-negativity and average optical intensity constraints, lower and upper secrecy capacity bounds are first derived by the variational method, the dual expression of the secrecy capacity, and the concept of "the optimal input distribution that escapes to infinity". Numerical results show that the secrecy capacity upper and lower bounds are tight. By an asymptotic analysis at large optical intensity, there is a small performance gap between the asymptotic upper and lower bounds. Then, by adding a peak optical intensity constraint, we further analyze the exact and asymptotic secrecy capacity bounds. Finally, the tightness of the derived bounds is verified by numerical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Asymptotic analysis

Lattice QCD calculation of the Ds* radiative decay with (2+1)-flavor Wilson-clover ensembles

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Physical Review D 2024年第7期109卷 074511-074511页

作者： Yu Meng Jin-Long Dang Chuan Liu Zhaofeng Liu Tinghong Shen Haobo Yan Ke-Long Zhang School of Physics and Laboratory of Zhongyuan Light Zhengzhou University Zhengzhou Henan 450001 China School of Physics Peking University Beijing 100871 China Center for High Energy Physics Peking University Beijing 100871 China Collaborative Innovation Center of Quantum Matter Beijing 100871 China Institute of High Energy Physics Chinese Academy of Sciences Beijing 100049 People’s Republic of China Hubei Nuclear Solid Physics Key Laboratory School of Physics and Technology Wuhan University Wuhan Hubei 430072 People’s Republic of China Computer Network Information Center Chinese Academy of Sciences

We perform a lattice calculation on the radiative decay of Ds* using the (2+1)-flavor Wilson-clover gauge ensembles generated by CLQCD collaboration. A method allowing us to calculate the form factor with zero transfer momentum is proposed and applied to the radiative transition Ds*→Dsγ and the Dalitz decay Ds*→Dse+e−. After a continuum extrapolation using three lattice spacings, we obtain Γ(Ds*→Dsγ)=0.0549(54) keV, where the error is purely statistical. The result is consistent with previous lattice calculations but with a error reduced to only a fifth of the before. The Dalitz decay rate is also calculated for the first time and the ratio with the radiative transition is found to be Ree=0.624(3)%. A total decay width of Ds* can then be determined as 0.0587(54) keV taking into account the experimental branching fraction. Combining with the most recent experimental measurement on the branching fraction of the purely leptonic decay Ds+,*→e+νe, we obtain the quantity fDs*|Vcs|=(190.5−41.7stat+55.1±12.6syst) MeV, where the stat. is only the statistical error from the experiment, and syst. results from the experimental systematic uncertainty and the lattice statistical error. Our result leads to an improved systematic uncertainty compared to 42.7syst obtained using previous lattice prediction of total decay width 0.070(28) keV as the input.

关键词： Lattice field theory

Real-Time Near-Term Iterative Assimilation and Forecasting System of Terrestrial Ecosystem Carbon Cycle (Cafs 1.0)

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SSRN

SSRN 2023年

作者： Ren, Xiaoli He, Honglin Wan, Meng Nie, Ningming Liu, Weihua Xu, Qian Shan, Rui Lin, Zining Cao, Rongqiang Wang, Yangang Cao, Naixun Wu, Xiaojing Ge, Rong Yang, Qinmeng Tang, Xinzhai Zhang, Li Zhang, Qianmei Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China National Ecosystem Science Data Center Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China College of Resources and Environment University of Chinese Academy of Sciences Beijing China Computer Network Information Center Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Technology Co. Ltd China Institute of Natural Resources and Environmental Audits School of Government Audit Nanjing Audit University Nanjing China Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystem South China Botanical Garden Chinese Academy of Sciences Guangzhou China

The recent development of iterative forecasting provides an opportunity to inform management by forecasting ecosystem dynamics in advance, yet the iterative prediction focusing on ecosystem carbon dynamics is still poorly studied. In this study, we developed a real-time near-term iterative Assimilation and Forecasting System of terrestrial ecosystem Carbon cycle (CAFS 1.0) to realize the near-term iterative prediction of the site scale carbon dynamics (in the next 7 days), combining the pathway of real-time field observation, model simulation, and iterative forecating to provide support for carbon management. The system has been successfully deployed and applied in four forest field sites in China, helping assess the upcoming carbon sink under climatic changes. This provides an example of the transformation of field site scientific research mode from observation to forecasting, and can be applied in more sites, thereby facilitating regional real-time near-term carbon forecasting in the future. © 2023, The Authors. All rights reserved.

关键词： Forecasting

BAR: Blockwise adaptive recoding for batched network coding

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arXiv 2021年

作者： Yin, Hoover H.F. Yang, Shenghao Zhou, Qiaoqiao Yung, Lily M.L. Ng, Ka Hei The Institute of Network Coding The Chinese University of Hong Kong Hong Kong Hong Kong The School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen China Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology Shenzhen Research Institute of Big Data Shenzhen China The Department of Information Engineering The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Physics The Chinese University of Hong Kong Hong Kong Hong Kong

Multi-hop networks become popular network topologies in various emerging Internet of things applications. Batched network coding (BNC) is a solution to reliable communications in such networks with packet loss. By grouping packets into small batches and restricting recoding to the packets belonging to the same batch, BNC has a much smaller computational and storage requirements at the intermediate nodes compared with a direct application of random linear network coding. In this paper, we propose a practical recoding scheme called blockwise adaptive recoding (BAR) which learns the latest channel knowledge from short observations so that BAR can adapt to the fluctuation of channel conditions. We focus on investigating practical concerns such as the design of efficient BAR algorithms. We also design and investigate feedback schemes for BAR under imperfect feedback systems. Our numerical evaluations show that BAR has significant throughput gain for small batch size compared with the existing baseline recoding scheme. More importantly, this gain is insensitive to inaccurate channel knowledge. This encouraging result suggests that BAR is suitable to be realized in practice as the exact channel model and its parameters could be unknown and subject to change from time to time. Copyright © 2021, The Authors. All rights reserved.

关键词： network coding

ReGO: Reference-Guided Outpainting for Scenery Image

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arXiv 2021年

作者： Wang, Yaxiong Wei, Yunchao Qian, Xueming Zhu, Li Yang, Yi The School of Software Engineering Xi’an Jiaotong University Xi'An710049 China Institute of Information Science Beijing Jiaotong University Beijing100000 China The Key Laboratory for Intelligent Networks and Network Security Ministry of Education Xi’an Jiaotong University Xi'An710049 China The SMILES Laboratory Xi’an Jiaotong University Xi'An710049 China Zhibian Technology Co. Ltd. Taizhou317000 China The School of Software Xi’an Jiaotong University Xi'An710049 China School of Computer Science and Technology Zhejiang University Hangzhou310000 China

We aim to tackle the challenging yet practical scenery image outpainting task in this work. Recently, generative adversarial learning has significantly advanced the image outpainting by producing semantic consistent content for the given image. However, the existing methods always suffer from the blurry texture and the artifacts of the generative part, making the overall outpainting results lack authenticity. To overcome the weakness, this work investigates a principle way to synthesize texture-rich results by borrowing pixels from its neighbors (i.e. , reference images), named Reference-Guided Outpainting (ReGO). Particularly, the ReGO designs an Adaptive Content Selection (ACS) module to transfer the pixel of reference images for texture compensating of the target one. To prevent the style of the generated part from being affected by the reference images, a style ranking loss is further proposed to augment the ReGO to synthesize style-consistent results. Extensive experiments on two sceneary benchmarks, NS6K [3], NS8K [9] and SUN Attribute [25], well demonstrate the effectiveness of our ReGO. Our code is available at https://***/wangyxxjtu/ReGOPytorch. © 2021, CC BY.

关键词： Textures

224-Gb/s 16QAM SV-DD Transmission Using Pilot-Assisted Polarization Recovery with Integrated Receiver

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224-Gb/s 16QAM SV-DD Transmission Using Pilot-Assisted Polar...

Optical Fiber Communication (OFC) Conference

作者： Sen Zhang Liwang Lu Linsheng Fan Bin Chen Tingting Zhang Tianjian Zuo Lei Liu Jin Tang Li Zeng Pengxin Chen Jiahao Huo Xian Zhou Alan Pak Tao Lau Chao Lu Liu Liu Changjian Guo Network Technology Laboratory Huawei Technologies Co. Ltd. Shenzhen China Guangdong Provincial Key Laboratory of Optical Information Materials and Technology South China Academy of Advanced Optoelectronics South China Normal University Guangzhou China State Key Laboratory for Modern Optical Instrumentation College of Optical Science and Engineering Centre for Optical & Electromagnetic Research International Research Center for Advanced Photonics Zhejiang University Hangzhou China B&P Laboratory Huawei Technologies Co. Ltd. Shenzhen China School of Computer & Communication Engineering Beijing University of Science and Technology Beijing China The Hong Kong Polytechnic University Hong Kong China The Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen

We propose and experimentally demonstrate a low-complexity pilot-assisted analytical method for polarization demultiplexing in SV-DD systems, achieving a 224-Gb/s 16QAM transmission over 40-km SSMF and can tolerate up... 详细信息

ISBN: (纸本)9781943580866

关键词： Demultiplexing Receivers Optical fiber communication