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A Multi-Modal Behavior Quantitative Analysis Model for Autism Early Screening

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A Multi-Modal Behavior Quantitative Analysis Model for Autis...

IEEE International Conference on Systems, Man and Cybernetics

作者： Jiayi Lei E Zhang Yingying She Xin Wang Yuhan Liao Bin Hu Hang Wu Minqiang Yang Jiajia Tian Yong Wang School of Informatics Xiamen University China Department of Occupational Therapy Education University of Kansas Medical Center USA School of Film National Institute for Data Science in Health and Medicine Xiamen University China School of Computer Science and Technology Beijing Institute of Technology China Together Education Institute China School of Information Science and Engineering Lanzhou University China Pediatric Department Fujian Medical University Union Hospital China

Human-computer Interaction (HCI) and Machine Learning (ML) technologies have potential for the behavioral screening of autistic children but how to design a tool and analyse behavior reliably is challenging. Based on psychophysiological computation, this paper proposes an interactive behavior perception analytical model for autism screening. We presented the multi-scenario reactive behavior paradigms that designed based on the atypical characteristics of autistic children. We recorded the eye movement data and facial data of 91 participants, and performed multi-modal feature extraction, used machine learning to train classification model. We conducted comparative experiments, and the experimental results verified the advantages of multi-scenario paradigms and multi-modal feature groups, which indicates that our analysis methods and screening models are effective and reliable and have real research significance.

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Inter- and sub-harmonics estimation: Treatise on raptor-inspired Harris Hawks collective wisdom

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International Journal of Modern Physics C 2024年第12期35卷

作者： Malik, Naveed Ahmed Chang, Ching-Lung Chaudhary, Naveed Ishtiaq Raja, Muhammad Asif Zahoor Shu, Chi-Min Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology University Road Douliu 123Section 3 Yunlin 64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology University Road Douliou 123Section 3 Yunlin 64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology University Road Douliou 123Section 3 Yunlin 64002 Taiwan Department of Safety Health and Environmental Engineering National Yunlin University of Science and Technology University Road Douliou 123Section 3 Yunlin 64002 Taiwan

In contemporary power systems, harmonics as undesired electrical phenomena, have drawn considerable attention of the research community investigating in design and development of optimal electrical networks. Harmonics affect the power electronics devices with a number of negative impacts, including higher power losses, worse power quality, and interference with other delicate grid connected equipment. An objective function of inter- and sub-harmonics is constructed for joint estimation of amplitude and phase with known frequency. Accurate estimation of the harmonics model is carried out with stupendous knacks of raptor-inspired Harris Hawks optimization algorithm (HHOA), a revolutionary population-based, nature-motivated optimization approach. The primary sources of inspiration for HHOA are Harris Hawks cooperative demeanor and surprise pounce chasing style in nature. The HHOA's performance is assessed for harmonics parameter estimation with several noise conditions, differing particle sizes and iterations. The proposed HHOA-based estimation technique provides accurate, convergent and robust performance for harmonics estimation. The reliable level of fitness is achieved consistently in the range of 10^-3 to 10^-10, an evidence of valuable precision, stability and robustness of the proposed algorithm. © 2024 World Scientific Publishing Company.

关键词： harmonics analysis Harris Hawks optimization parameter estimation Power quality swarm intelligence

DeepSSN: a deep convolutional neural network to assess spatial scene similarity

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arXiv 2022年

作者： Guo, Danhuai Ge, Shiyin Zhang, Shu Gao, Song Tao, Ran Wang, Yangang Spatio-Temporal Data Intelligence Lab College of Information Science and Technology Beijing University of Chemical Technology Beijing China Computer Network Information Center Chinese Academy of Sciences Beijing China Geospatial Data Science Lab Department of Geography University of Wisconsin-Madison WI United States School of Geosciences University of South Florida FL United States

Spatial-query-by-sketch is an intuitive tool to explore human spatial knowledge about geographic environments and to support communication with scene database queries. However, traditional sketch-based spatial search methods perform insufficiently due to their inability to find hidden multi-scale map features from mental sketches. In this research, we propose a deep convolutional neural network, namely Deep Spatial Scene network (DeepSSN), to better assess the spatial scene similarity. In DeepSSN, a triplet loss function is designed as a comprehensive distance metric to support the similarity assessment. A positive and negative example mining strategy using qualitative constraint networks in spatial reasoning is designed to ensure a consistently increasing distinction of triplets during the training process. Moreover, we develop a prototype spatial scene search system using the proposed DeepSSN, in which the users input spatial query via sketch maps and the system can automatically augment the sketch training data. The proposed model is validated using multi-source conflated map data including 131,300 labeled scene samples after data augmentation. The empirical results demonstrate that the DeepSSN outperforms baseline methods including k-nearest-neighbors, multilayer perceptron, AlexNet, DenseNet, and ResNet using mean reciprocal rank and precision metrics. This research advances geographic information retrieval studies by introducing a novel deep learning method tailored to spatial scene queries. © 2022, CC BY-NC-SA.

关键词： Deep neural networks

CATD: Unified Representation Learning for EEG-to-fMRI Cross-Modal Generation

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arXiv 2024年

作者： Yao, Weiheng Lyu, Zhihan Mahmud, Mufti Zhong, Ning Lei, Baiying Wang, Shuqiang Southern University of Science and Technology Shenzhen518055 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Department of Game Design Faculty of Arts Uppsala University Sweden Department of Information and Computer Science SDAIA-KFUPM Joint Research Center for AI Interdisciplinary Research Center for Biosystems and Machines King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia The Faculty of Engineering Maebashi Institute of Technology Gunma Japan Chongqing University of Posts and Telecommunications Chongqing China School of Biomedical Engineering Shenzhen University Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging Health Science Center Guangdong Shenzhen518000 China

Multi-modal neuroimaging analysis is crucial for a comprehensive understanding of brain function and pathology, as it allows for the integration of different imaging techniques, thus overcoming the limitations of individual modalities. However, the high costs and limited availability of certain modalities pose significant challenges. To address these issues, this paper proposes the Condition-Aligned Temporal Diffusion (CATD) framework for end-to-end cross-modal synthesis of neuroimaging, enabling the generation of functional magnetic resonance imaging (fMRI)-detected Blood Oxygen Level Dependent (BOLD) signals from more accessible Electroencephalography (EEG) signals. By constructing Conditionally Aligned Block (CAB), heterogeneous neuroimages are aligned into a latent space, achieving a unified representation that provides the foundation for cross-modal transformation in neuroimaging. The combination with the constructed Dynamic Time-Frequency Segmentation (DTFS) module also enables the use of EEG signals to improve the temporal resolution of BOLD signals, thus augmenting the capture of the dynamic details of the brain. Experimental validation demonstrates that the framework improves the accuracy of brain activity state prediction by 9.13% (reaching 69.8%), enhances the diagnostic accuracy of brain disorders by 4.10% (reaching 99.55%), effectively identifies abnormal brain regions, enhancing the temporal resolution of BOLD signals. The proposed framework establishes a new paradigm for cross-modal synthesis of neuroimaging by unifying heterogeneous neuroimaging data into a latent representation space, showing promise in medical applications such as improving Parkinson’s disease prediction and identifying abnormal brain regions. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

Convolution-enhanced Evolving Attention networks

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arXiv 2022年

作者： Wang, Yujing Yang, Yaming Li, Zhuo Bai, Jiangang Zhang, Mingliang Li, Xiangtai Yu, Jing Zhang, Ce Huang, Gao Tong, Yunhai Key Laboratory of Machine Perception MOE School of Intelligence Science and Technology Peking University China Department of Computer Science & Technology Engineering Research Center of Microprocessor & System Peking University China Institute of Information Engineering Chinese Academy of Sciences China ETH Zürich Switzerland The Department of Automation Tsinghua University China

Attention-based neural networks, such as Transformers, have become ubiquitous in numerous applications, including computer vision, natural language processing, and time-series analysis. In all kinds of attention networks, the attention maps are crucial as they encode semantic dependencies between input tokens. However, most existing attention networks perform modeling or reasoning based on representations, wherein the attention maps of different layers are learned separately without explicit interactions. In this paper, we propose a novel and generic evolving attention mechanism, which directly models the evolution of inter-token relationships through a chain of residual convolutional modules. The major motivations are twofold. On the one hand, the attention maps in different layers share transferable knowledge, thus adding a residual connection can facilitate the information flow of inter-token relationships across layers. On the other hand, there is naturally an evolutionary trend among attention maps at different abstraction levels, so it is beneficial to exploit a dedicated convolution-based module to capture this process. Equipped with the proposed mechanism, the convolution-enhanced evolving attention networks achieve superior performance in various applications, including time-series representation, natural language understanding, machine translation, and image classification. Especially on time-series representation tasks, Evolving Attention-enhanced Dilated Convolutional (EA-DC-) Transformer outperforms state-of-the-art models significantly, achieving an average of 17% improvement compared to the best SOTA. To the best of our knowledge, this is the first work that explicitly models the layer-wise evolution of attention maps. Our implementation is available at https://***/pkuyym/EvolvingAttention. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

Efficient automatic segmentation for multi-level pulmonary arteries: The PARSE challenge

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arXiv 2023年

作者： Luo, Gongning Wang, Kuanquan Liu, Jun Li, Shuo Liang, Xinjie Li, Xiangyu Gan, Shaowei Wang, Wei Dong, Suyu Wang, Wenyi Yu, Pengxin Liu, Enyou Wei, Hongrong Wang, Na Guo, Jia Li, Huiqi Zhang, Zhao Zhao, Ziwei Gao, Na An, Nan Pakzad, Ashkan Rangelov, Bojidar Dou, Jiaqi Tian, Song Liu, Zeyu Wang, Yi Sivalingam, Ampatishan Punithakumar, Kumaradevan Qiu, Zhaowen Gao, Xin School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Computer and Data Science Department of Biomedical Engineering Case Western Reserve University Cleveland44106 United States Institute of Information and Computer Engineering NorthEast Forestry University Harbin150040 China Infervision Medical Technology Co. Ltd. Beijing China Sensetime Shanghai China Beijing Institute of Technology Beijing China Peking University Beijing China Northeastern University Shenyang China Shanghai Jiao Tong University Shanghai China London United Kingdom Center for Biomedical Imaging Research Tsinghua University Beijing China Philips Healthcare China Chongqing University Chongqing China University of Alberta Edmonton Canada Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology 4700 KAUST Thuwal23955 Saudi Arabia

Efficient automatic segmentation of multi-level (i.e. main and branch) pulmonary arteries (PA) in CTPA images plays a significant role in clinical applications. However, most existing methods concentrate only on main PA or branch PA segmentation separately and ignore segmentation efficiency. Besides, there is no public large-scale dataset focused on PA segmentation, which makes it highly challenging to compare the different methods. To benchmark multi-level PA segmentation algorithms, we organized the first Pulmonary ARtery SEgmentation (PARSE) challenge. On the one hand, we focus on both the main PA and the branch PA segmentation. On the other hand, for better clinical application, we assign the same score weight to segmentation efficiency (mainly running time and GPU memory consumption during inference) while ensuring PA segmentation accuracy. We present a summary of the top algorithms and offer some suggestions for efficient and accurate multi-level PA automatic segmentation. We provide the PARSE challenge as open-access for the community to benchmark future algorithm developments at https://***/Parse2022/. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

Diff5T: Benchmarking Human Brain Diffusion MRI with an Extensive 5.0 Tesla K-Space and Spatial Dataset

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arXiv 2024年

作者： Wang, Shanshan Yu, Shoujun Cheng, Jian Jia, Sen Tie, Changjun Zhu, Jiayu Peng, Haohao Dong, Yijing He, Jianzhong Zhang, Fan Xing, Yaowen Jia, Xiuqin Yang, Qi Tian, Qiyuan Guo, Hua Li, Guobin Zheng, Hairong Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Beihang University Beijing China United-Imaging Healthcare Shanghai China School of Biomedical Engineering Tsinghua University Beijing China Department of Radiology Beijing Chaoyang Hospital Capital Medical University Key Lab of Medical Engineering for Cardiovascular Disease Ministry of Education Beijing China Institute of Information Processing and Automation College of Information Engineering Zhejiang University of Technology China University of Electronic Science and Technology of China Chengdu China

Diffusion magnetic resonance imaging (dMRI) provides critical insights into the microstructural and connectional organization of the human brain. However, the availability of high-field, open-access datasets that include raw k-space data for advanced research remains limited. To address this gap, we introduce Diff5T, a first comprehensive 5.0 Tesla diffusion MRI dataset focusing on the human brain. This dataset includes raw k-space data and reconstructed diffusion images, acquired using a variety of imaging protocols. Diff5T is designed to support the development and benchmarking of innovative methods in artifact correction, image reconstruction, image preprocessing, diffusion modelling and tractography. The dataset features a wide range of diffusion parameters, including multiple b-values and gradient directions, allowing extensive research applications in studying human brain microstructure and connectivity. With its emphasis on open accessibility and detailed benchmarks, Diff5T serves as a valuable resource for advancing human brain mapping research using diffusion MRI, fostering reproducibility, and enabling collaboration across the neuroscience and medical imaging communities. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Leveraging Probe Data and Machine Learning to Derive and Interpret Macroscopic Fundamental Diagrams Across U.S. Cities

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Leveraging Probe Data and Machine Learning to Derive and Int...

2023 IEEE International Conference on Big Data, BigData 2023

作者： Jin, Ling Xu, Xiaodan Wang, Yuhan Sadabadi, Kaveh Farokhi Lazar, Alina Don, Duleep Rathgamage Needell, Zachary Spurlock, C Anna Amirgholy, Mahyar Asudegi, Mona Energy Technology Area Berkeley National Laboratory BerkeleyCA94720 United States University of Maryland Center for Advanced Transportation Technology College ParkMD20742 United States Youngstown State University Department of Computer Science and Information YoungstownOH44555 United States Kennesaw State University School of Data Science and Analytics MariettaGA30060 United States Energy Analysis and Environmental Impacts Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Kennesaw State University Civil and Environmental Engineering Department MariettaGA30060 United States Office of Transportation Policy Studies Federal Highway Administration DC WashingtonDC20590 United States

ISBN: (纸本)9798350324457

Macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. Understanding network-wide traffic through MFDs can optimally allocate demand to existing networks, improving performance by maximizing network production and avoiding congestion. However, due to historical data limitations, empirically derived MFD models are sparse in the literature, especially for the U.S. cities. Leveraging a large-scale and granular census-tract-level flow and density derived from vehicle probe data, this research is the first to develop a machine learning approach to both derive MFD models and interpret their underlying difference among urban networks across the entire United States. Among the four machine learning methods tested here XGBoost is found to deliver the best performance to predict the network traffic flow for given vehicular density and location attributes. Interaction Shapley Additive explanation (SHAP) values are used to interpret the factors, such as land use, transportation infrastructure, and network topology, that influence the flow-density relationships among locations. The analysis framework developed in this work can generate datadriven MFDs and a deeper understanding of their shape dependence on network, infrastructure, and land use characteristics, which can be used by transportation authorities to derive and optimize location-specific MFDs facilitating more informed management and planning decisions at the network level. © 2023 IEEE.

关键词： Interaction Shapley values machine learning models macroscopic fundamental diagram TreeExplainer United States vehicle probe data

Chances and Challenges of ChatGPT and Similar Models for Education in M&S

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Chances and Challenges of ChatGPT and Similar Models for Edu...

2023 Winter Simulation Conference, WSC 2023

作者： Tolk, Andreas Barry, Philip Loper, Margaret L. Rabadi, Ghaith Scherer, William T. Yilmaz, Levent The Mitre Corporation Modeling and Analysis Innovation Center 1001 Research Park Blvd #200 CharlottesvilleVA22911 United States L3Harris Corporation Mission Analysis Group 2235 Monroe Street HerndonVA20171 United States Georgia Tech Research Institute Georgia Institute of Technology AtlantaGA30308 United States University of Central Florida School of Modeling Simulation and Training 3100 Technology Pkwy OrlandoFL32826 United States University of Virgina Department of Systems and Information Engineering CharlottesvilleVA22904 United States Auburn University Samuel Ginn College of Engineering Computer Science and Software Engineering AuburnAL36849 United States

ISBN: (纸本)9798350369663

This position paper summarizes the inputs of a group of experts from academia and industry presenting their view on chances and challenges of using ChatGPT within Modeling and Simulation education. The experts also address the need to evaluate continuous education as well as education of faculty members to address scholastic challenges and opportunities while meeting the expectation of industry. Generally, the use of ChatGPT is encouraged, but it needs to be embedded into an updated curriculum with more emphasis on validity constraints, systems thinking, and ethics. © 2023 IEEE.

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