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DPABISurf:data processing&analysis for brain imaging on surface

Science Bulletin 2021年第24期66卷 2453-2455页

作者： Chao-Gan Yan Xin-Di Wang Bin Lu CAS Key Laboratory of Behavioral Science Institute of PsychologyBeijing 100101China Department of Psychology University of Chinese Academy of SciencesBeijing 100101China International Big-Data Center for Depression Research Institute of PsychologyChinese Academy of SciencesBeijing 100101China Magnetic Resonance Imaging Research Center Institute of PsychologyChinese Academy of SciencesBeijing 100101China McGill Centre for Integrative Neuroscience Montreal Neurological InstituteMcGill UniversityMontréal H3A 2B4Canada Beijing Intelligent Brain Cloud Inc.Beijing 100036China

Functional magnetic resonance imaging(fMRI)is a prevalent technology in brain research of cognition,emotion,development,and brain *** traditional fMRI analysis is based on volume-based preprocessing pipelines and algorithms,which means that the brain MRI data is to be registered to a 3-dimensional(3D)coordinate[1].However,the relatively low spatial resolution of fMRI may lead to partial-volume-effect(e.g.,a 3D region may contain signals from grey matter,white matter and even cerebrospinal fluid).Given the human brain function is organized in a brain surface mesh manner,therefore,a growing number of studies conducted surface-based preprocessing pipelines and ***-based methods reconstructed the brain grey matter into 2-dimensional cortical surface which better represent the curving structure of the ***-based method is superior to volume-based method on brain registration,signal–noise ratio and reproducibility of algorithms[2].Specifically,the traditional volume-based approach was reported with a spatial localization that is only 35%of the best surface-based method[2].

关键词：脑影像 coordinate registered

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Community-based Message Forwarding in Mobile Social Networks

Community-based Message Forwarding in Mobile Social Networks

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作者： Zhiming Chen Yang Xiang School of Computer Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security & Intelligent Processing

With the popularity of various smart devices and the application of sensor network technology, message transmission using mobile devices is becoming *** paper focuses on the forwarding in mobile social network(MSN).The MSN is a special Delay Tolerant Network(DTN) consisting of mobile *** MSN, nodes move and share information with each other through carried short-range wireless communication *** nodes in the MSN typically access some building areas more frequently, such as schools, companies, or apartments, while visiting other areas, such as the roads between buildings, less *** building areas that nodes frequently visit are called *** increase delivery ratio and reduce transmission time in MSN, this paper proposes a novel zero-knowledge multi-copy routing algorithm, Mixed Message Forwarding(MMF) which exploits and improves the metric, namely *** reflects the importance of a node in the *** improves copy diffusion by using different directions of node movement as *** facilities called boundary boxes are added to the network *** boxes are special throw *** boxes are relays with large storage space and fixed *** is designed and evaluated, which utilizes the aforementioned boundary boxes to reduce transmission *** simulation results show that the MMF can improve the delivery ratio and reduce the transmission delay, compared with other algorithms.

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Erratum to: Incremental algorithms for the maximum internal spanning tree problem

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Science China Information Sciences 2022年第10期65卷 1-1页

作者： Zhu, Xianbin Li, Wenjun Yang, Yongjie Wang, Jianxin School of Computer Science and Engineering Central South University Changsha China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha China Chair of Economic Theory Saarland University Saarbrücken Germany

In the original paper [1], there are two errors on page 5. The rules in the algorithm are not sufficient since we omitted one rule during publication. The following rule should be added to the paper. Rule 3: If P1 = (u, v) and P2 = (x, y) are two maximal leaf-paths both with two vertices in T, where u and x are leaves in T and (v, y) ∈ E(G), we remove (y′, y) and then add (v, y) to T where y′ is the non-leaf neighbor of y in T. The expression of Algorithm 2 is not accurate. It should be modified as follows. Line 3: Exhaustively apply Rules 1–3, and for i ∈ {2, 3} only apply Rule i when none of Rules j where 1 ⩽ j < i is applicable. Line 8: Determine the longest path P among all paths whose endpoints have nonempty neighbors in T \ V(T′).

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Automatic Recognition of Business Process Images

Automatic Recognition of Business Process Images

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2019 IEEE International Conference on Computer Science and Educational Informatization, CSEI 2019

作者： Kang, Qingyi Yu, Yong Dai, Fei School of Software Yunnan University Kunming650091 China School of Software Key Laboratory in Software Engineering of Yunnan Province Yunnan University Kunming650091 China School of Big Data and Intelligent Engineering Southwest Forestry University Kunming650224 China

ISBN: (纸本)9781728123080

The Business Process Modelling Notation (BPMN) is a defacto standard for modelling business processes in the early phases of systems development. The increased adoption of business process management approaches has led organizations to model large collections of BPMN business process models. The mix of constructs in BPMN business process models can lead to semantic error. The ability to take a picture of BPMN business process models and analyze the semantic correctness of BPMN business process images is thus a desirable feature. In this paper, we propose an automatic recognition method to transform BPMN business process images into Petri nets, for which efficient analysis techniques are available. The proposed recognition has been implemented as a tool, in conjunction with existing Petri net-based tools, that enables the soundness analysis of business process images. This transformation can led to the identification of deficiencies in the BPMN business process images. © 2019 IEEE.

关键词： Petri nets

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CT Scan Synthesis for Promoting Computer-Aided Diagnosis Capacity of COVID-19 16th

CT Scan Synthesis for Promoting Computer-Aided Diagnosis Cap...

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16th International Conference on intelligent Computing, ICIC 2020

作者： Li, Heng Hu, Yan Li, Sanqian Lin, Wenjun Liu, Peng Higashita, Risa Liu, Jiang School of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Tomey Corporation Nagoya451-0051 Japan Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen518055 China Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo China

ISBN: (纸本)9783030608019

Nowadays, with the rapid spread of Corona Virus Disease 2019 (COVID-19), this epidemic has become a threatening risk for global public health. Medical workers and researchers all over the world are struggling against the novel coronavirus in the front line. Because the computed tomography (CT) images from infected patients exposure characteristic abnormalities, automatic CT analyzers based on AI-based algorithms are extensively employed as effective weapons to aid clinicians. However, unbalanced data and lack of annotations obstruct AI-based algorithms applying in aided diagnosis because of their low performance. Therefore, in order to solve the above problems, a general-purpose solution is proposed to synthesize COVID-19 CT scans from non-COVID-19 data for providing high-quality negative-positive paired CT scans. Particularly, we introduce an elastic registration algorithm of CT images to manufacture paired training data. Then, a conditional Generative Adversarial Networks (GANs) based image-to-image translation model is implemented to synthesize COVID-19 CT scans from non-COVID-19 data. The effectiveness of our proposed algorithm used in COVID-19 aided diagnosis is verified in the experiments, and the identification and detection capacities of the classification models have been enhanced with the generated CT scans. Specifically, the precise lesion location is achieved by the generated data with a weakly supervised algorithm of class activation mapping (CAM). The model and code of this paper are publicly available at https://***/lihengbit/Synthesis-of-COVID-19-CT-Scan. © 2020, Springer Nature Switzerland AG.

关键词： Computer aided diagnosis

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An improved method of algal-bloom discrimination in Taihu Lake using Sentinel-1A data 6

An improved method of algal-bloom discrimination in Taihu La...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Wu, Lin Sun, Mengwei Min, Lin Zhao, Jianhui Li, Ning Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Kaifeng475004 China

ISBN: (纸本)9781728129129

The algal bloom is a prominent manifestation of water pollution. Synthetic aperture radar (SAR) shows an advantage in water monitoring due to its characteristic of all-time and all-weather. The water regions where algae gather present dark in SAR image. However, dark regions may also be caused by other factors, such as low wind. This paper proposes an improved algal bloom discrimination method based on Artificial Neural Network (ANN) to recognize the dark regions of algal bloom. Taihu Lake is chosen as the research area in this study because of its serious bloom in recent years. By means of quasi-synchronous optical images, the dark region database of SAR images labeled algal bloom and non-algal bloom are obtained. Then the segmentation algorithm and region growing algorithm are used to acquire the feature from dark regions, and divided into training feature set and testing feature set. Finally, the training and testing feature set are used for ANN-based discrimination model construction and verification. According the experimental results, the overall accuracy reaches 80%, which indicates that ANN model has a good applicability in algal bloom recognition of SAR image. © 2019 IEEE.

关键词： Neural networks

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A Study on the Differences of Attitude Learning and Learning Behavior Sequences for University Computer MOOC+ SPOC Course

A Study on the Differences of Attitude Learning and Learning...

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International Conference on Computer Science & Education (ICCSE)

作者： Zhao Changhua Mi Chunqiao School of Computer Science and Engineering Huaihua University Huaihua P. R. China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua P. R. China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua P. R. China

ISBN: (数字)9781728172675

ISBN: (纸本)9781728172682

The outbreak of COVID-19 make University Computer MOOC+SPOC course face opportunities and challenges. There are some problems in MOOC+SPOC course teaching, such as learners' curiosity decreasing, lack of accomplishment and etc. In order to deal with these problems, this study explores the correlation and differences in the learning behavior sequences of learners with different attitudes to high happiness, medium happiness, and low happiness. Through the learners' comments on University Computer MOOC+SPOC course, the learner's attitude information is tapped. By analyzing learning behavior sequences, this study finds out the degree of learning behavior change of learners with different attitudes. The result of this study can improve the existing University Computer MOOC+SPOC instructional design and promote the learning mode to move towards intelligent personalized adaptive learning.

关键词： Interviews Electronic learning data analysis Encoding Correlation

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A branch-and-price algorithm for the trucking problem using trailer-swapping operation mode

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Transportation Research Part C: Emerging Technologies 2025年 178卷

作者： Peng, Wenxiang Chen, Xiangsheng Xue, Zhaojie You, Jintao College of Civil and Transportation Engineering Shenzhen University Shenzhen518060 China Ministry of Education Shenzhen518060 China State Key Laboratory of Subtropical Building and Urban Science Shenzhen518060 China School of Advanced Engineering Great Bay University Dongguan523000 China Great Bay Institute for Advanced Study Great Bay University Dongguan523000 China Dongguan Key Laboratory of Intelligent Equipment and Smart Industry School of Advanced Engineering Great Bay University Dongguan523000 China Shenzhen Research Institute of Big Data Shenzhen518172 China

This study investigates the trucking problem using the trailer-swapping operation mode (TP-TSOM), where trucks swap trailers at network nodes during transit and then continue to pull the swapped trailer to its destination. Traditionally, trucking involves transport tasks distributed across the network, completed by trucks pulling trailers directly from origins to destinations. The trailer-swapping operation mode (TSOM), which involves collaboration among trucks, can enhance trucking efficiency. Accordingly, a mixed-integer linear programming model for TP-TSOM is developed to determine the optimal truck trailer-swapping plan, and an integer linear programming model for the two-truck swapping case is also constructed. To solve the TP-TSOM efficiently, a branch-and-price algorithm utilizing a trailer-swapping group-based set partitioning model is designed. The pricing sub-problem is formulated as multiple elementary shortest cycle problems with resource constraints (ESCPRC) based on different sub-graphs. By iteratively fixing virtual terminals, a new representation of the ESCPRC is constructed, enabling the ESCPRC to be solved using the labeling algorithm. To accelerate the solution process, two tailored refinement strategies are proposed, and a heuristic pricing algorithm based on shortest path faster algorithm is applied. Extensive numerical experiments are conducted to validate performance. The computational results show that the proposed exact algorithm can solve instances with up to 200 tasks and that TSOM significantly reduces transportation costs, travel time, and empty load rates. © 2025 Elsevier Ltd

关键词： Travel time

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A Deep Learning Model Incorporating Knowledge Representation Vectors and Its Application in Diabetes Prediction

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Disease Markers 2022年第1期2022卷 7593750页

作者： Xu, He Zheng, Qunli Zhu, Jingshu Xie, Zuoling Cheng, Haitao Li, Peng Ji, Yimu School of Computer Science Nanjing University of Posts and Telecommunications Nanjing 210023 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Jiangsu Province Nanjing 210023 China Institute of High Performance Computing and Big Data Nanjing University of Posts and Telecommunications Nanjing 210023 China Nanjing Center of HPC China Nanjing 210023 China Jiangsu Research Engineering of HPC and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing 210023 China Department of Endocrinology Zhongda Hospital Southeast University Nanjing 210009 China

The deep learning methods for various disease prediction tasks have become very effective and even surpass human experts. However, the lack of interpretability and medical expertise limits its clinical application. This paper combines knowledge representation learning and deep learning methods, and a disease prediction model is constructed. The model initially constructs the relationship graph between the physical indicator and the test value based on the normal range of human physical examination index. And the human physical examination index for testing value by knowledge representation learning model is encoded. Then, the patient physical examination data is represented as a vector and input into a deep learning model built with self-attention mechanism and convolutional neural network to implement disease prediction. The experimental results show that the model which is used in diabetes prediction yields an accuracy of 97.18% and the recall of 87.55%, which outperforms other machine learning methods (e.g., lasso, ridge, support vector machine, random forest, and XGBoost). Compared with the best performing random forest method, the recall is increased by 5.34%, respectively. Therefore, it can be concluded that the application of medical knowledge into deep learning through knowledge representation learning can be used in diabetes prediction for the purpose of early detection and assisting diagnosis. © 2022 He Xu et al.

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Abnormal Scanning Patterns Based on Eye Movement Entropy in Early Psychosis

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2024年

作者： Zhang, Dan Ma, Chunyan Xu, Lihua Liu, Xu Cui, Huiru Wei, Yanyan Zheng, Wensi Hong, Yawen Xie, Yuou Qian, Zhenying Hu, Yegang Tang, Yingying Li, Chunbo Liu, Zhi Chen, Tao Liu, Haichun Zhang, Tianhong Wang, Jijun Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine People's Republic of China Shanghai China First Clinical Medical College of Nanjing Medical University People's Republic of China Nanjing China Shanghai Institute for Advanced Communication and Data Science Shanghai University People's Republic of China Shanghai China School of Communication and Information Engineering Shanghai University People's Republic of China Shanghai China Labor and Worklife Program Harvard University Cambridge Massachusetts United States Big Data Research Laboratory University of Waterloo Waterloo ON Canada Niacin (Shanghai) Technology Co. Ltd. People's Republic of China Shanghai China Department of Automation Shanghai Jiao Tong University People's Republic of China Shanghai China Shanghai Mental Health Center Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention Shanghai Key Laboratory of Psychotic Disorders Shanghai Jiaotong University School of Medicine People's Republic of China Shanghai China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences People's Republic of China Shanghai China Institute of Psychology and Behavioral Science Shanghai Jiao Tong University People's Republic of China Shanghai China

Background: Restricted scan path mode is hypothesized to explain abnormal scanning patterns in patients with schizophrenia. Here, we calculated entropy scores (drawing on gaze data to measure the statistical randomness of eye movements) to quantify how strategical and random participants were when processing image stimuli. Methods: Eighty-six patients with first-episode schizophrenia (FES), 124 individuals at clinical high risk (CHR) for psychosis, and 115 healthy control participants (HCs) completed an eye-tracking examination while freely viewing 35 static images (each presented for 10 seconds) and cognitive assessments. We compared group differences in the overall entropy score, as well as entropy scores under various conditions. We also investigated the correlations between entropy scores and symptoms and cognitive function. Results: Increased overall entropy scores were noted in the FES and CHR groups compared with the HC group, and these differences were already apparent within 0 to 2.5 seconds. In addition, the CHR group exhibited higher entropy than the HC group when viewing low-meaning images. Moreover, the entropy within 0 to 2.5 seconds showed significant correlations with negative symptoms in the FES group, attention/vigilance scores in the CHR group, and speed of processing and attention/vigilance scores across all 3 groups. Conclusions: The results indicate that individuals with FES and those at CHR scanned pictures more randomly and less strategically than HCs. These patterns also correlated with clinical symptoms and neurocognition. The current study highlights the potential of the eye movement entropy measure as a neurophysiological marker for early psychosis. © 2024 Society of Biological Psychiatry

关键词： Biomarker Clinical high risk for psychosis Cognitive impairments Early psychosis Eye movement Schizophrenia

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