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arXiv 2023年

作者： Wang, Rongguang Erus, Guray Chaudhari, Pratik Davatzikos, Christos Department of Electrical and Systems Engineering University of Pennsylvania United States Center for AI and Data Science for Integrated Diagnostics University of Pennsylvania United States Center for Biomedical Image Computing and Analytics University of Pennsylvania United States Department of Computer and Information Science University of Pennsylvania United States Department of Radiology Perelman School of Medicine University of Pennsylvania United States Center for Biomedical Image Computing and Analytics University of Pennsylvania 3700 Hamilton Walk 7th Floor PhiladelphiaPA19104 United States

Machine learning (ML) is revolutionizing many areas of engineering and science, including healthcare. However, it is also facing a reproducibility crisis, especially in healthcare. ML models that are carefully constructed from and evaluated on data from one part of the population may not generalize well on data from a different population group, or acquisition instrument settings and acquisition protocols. We tackle this problem in the context of neuroimaging of Alzheimer's disease (AD), schizophrenia (SZ) and brain aging. We develop a weighted empirical risk minimization approach that optimally combines data from a source group, e.g., subjects are stratified by attributes such as sex, age group, race and clinical cohort to make predictions on a target group, e.g., other sex, age group, etc. using a small fraction (10%) of data from the target group. We apply this method to multi-source data of 15,363 individuals from 20 neuroimaging studies to build ML models for diagnosis of AD and SZ, and estimation of brain age. We found that this approach achieves substantially better accuracy than existing domain adaptation techniques: it obtains area under curve greater than 0.95 for AD classification, area under curve greater than 0.7 for SZ classification and mean absolute error less than 5 years for brain age prediction on all target groups, achieving robustness to variations of scanners, protocols, and demographic or clinical characteristics. In some cases, it is even better than training on all data from the target group, because it leverages the diversity and size of a larger training set. We also demonstrate the utility of our models for prognostic tasks such as predicting disease progression in individuals with mild cognitive impairment. Critically, our brain age prediction models lead to new clinical insights regarding correlations with neurophysiological tests. In summary, we present a relatively simple methodology, along with ample experimental evidence, supporting

关键词： Neuroimaging

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Generative AI for Space-Air-Ground Integrated Networks

arXiv

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arXiv 2023年

作者： Zhang, Ruichen Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Zhang, Ping Kim, Dong In The College of Computing and Data Science Nanyang Technological University Singapore The Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The University of Sydney SydneyNSW2006 Australia The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Recently, generative AI technologies have emerged as a significant advancement in artificial intelligence field, renowned for their language and image generation capabilities. Meantime, space-air-ground integrated network (SAGIN) is an integral part of future B5G/6G for achieving ubiquitous connectivity. Inspired by this, this article explores an integration of generative AI in SAGIN, focusing on potential applications and case study. We first provide a comprehensive review of SAGIN and generative AI models, highlighting their capabilities and opportunities of their integration. Benefiting from generative AI’s ability to generate useful data and facilitate advanced decision-making processes, it can be applied to various scenarios of SAGIN. Accordingly, we present a brief survey on their integration, including channel modeling and channel state information (CSI) estimation, joint air-space-ground resource allocation, intelligent network deployment, semantic communications, image extraction and processing, security and privacy enhancement. Next, we propose a framework that utilizes a generative diffusion model (GDM) to construct channel information map to enhance quality of service for SAGIN. Simulation results demonstrate the effectiveness of the proposed framework. Finally, we discuss potential research directions for generative AI-enabled SAGIN. © 2023, CC BY.

关键词： Generative adversarial networks

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On the robustness of EEG tensor completion methods

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Science China(Technological Sciences) 2021年第9期64卷 1828-1842页

作者： DUAN Feng JIA Hao ZHANG ZhiWen FENG Fan TAN Ying DAI YangYang CICHOCKI Andrzej YANG ZhengLu CAIAFA Cesar F. SUN Zhe SOLE-CASALS Jordi College of Artificial Intelligence Nankai UniversityTianjin 300350China Skolkowo Institute of Science and Technology Moscow 121205Russia College of Computer Science Hangzhou Dianzi UniversityHangzhou 310018China Department of Informatics Nicolaus Copernicus UniversityTorun 87-100Poland Systems Research Institute Polish Academy of SciencesWarsaw 01-447Poland College of Computer Science Nankai UniversityTianjin 300350China Instituto Argentino de Radioastronom´ıa IAR-CCT La Plata CONICET/CIC-PBA/UNLPVilla Elisa 1894Argentina Computational Engineering Applications Unit Head Office for Information Systems and CybersecurityRIKENWako-Shi 351-0106Japan Department of Psychiatry University of CambridgeCambridge CB28AHUK Data and Signal Processing Research Group University of Vic-Central University of CataloniaCatalonia 08500Spain

During the acquisition of electroencephalographic(EEG) signals, data may be missing or corrupted by noise and artifacts. To reconstruct the incomplete data, EEG signals are firstly converted into a three-order tensor(multi-dimensional data) of shape time × channel × trial. Then, the missing data can be efficiently recovered by applying a tensor completion method(TCM).However, there is not a unique way to organize channels and trials in a tensor, and different numbers of channels are available depending on the EEG setting used, which may affect the quality of the tensor completion results. The main goal of this paper is to evaluate the robustness of EEG completion methods with several designed parameters such as the ordering of channels and trials, the number of channels, and the amount of missing data. In this work, the results of completing missing data by several TCMs were compared. To emulate different scenarios of missing data, three different patterns of missing data were ***, the amount of missing data on completion effects was analyzed, including the time lengths of missing data and the number of channels or trials affected by missing data. Secondly, the numerical stability of the completion methods was analyzed by shuffling the indices along channels or trials in the EEG data tensor. Finally, the way that the number of electrodes of EEG tensors influences completion effects was assessed by changing the number of channels. Among all the applied TCMs, the simultaneous tensor decomposition and completion(STDC) method achieves the best performance in providing stable results when the amount of missing data or the electrode number of EEG tensors is changed. In other words, STDC proves to be an excellent choice of TCM, since permutations of trials or channels have almost no influence on the complete results. The STDC method can efficiently complete the missing EEG signals. The designed simulations can be regarded as a procedure to validate whether or

关键词： electroencephalogram corrupted data missing data tensor completion tensor decomposition

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An HL7-Based Middleware for Exchanging data and Enabling Interoperability in Healthcare Applications 17th

An HL7-Based Middleware for Exchanging Data and Enabling Int...

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17th International Conference on information Technology: New Generations, ITNG 2020

作者： Bezerra, Carlos Andrew Costa de Araújo, André Magno Costa Times, Valéria Cesário Department of Information Systems University Center President Antonio Carlos Araguaína Brazil Advanced Studies in Data Science and Software Engineering Federal University of Alagoas Penedo Brazil Center for Informatics Federal University of Pernambuco Recife Brazil

ISBN: (纸本)9783030430191

data interoperability in the health sector is a current and important topic which has been the object of study in several research. Interoperability provides improved patient care quality, assists professionals in decision-making and enables healthcare organizations to remain competitive on the market. This work presents a cloud middleware based on the HL7 standard capable of encoding, storing, interoperating and integrating Electronic Health Record (EHR) data between different applications. Based on HL7 clinical document architecture, the software architecture of the proposed middleware is specified, a set of rules that maps relational data schemas to HL7 messages is described, and a tool that supports interoperability and integration of EHR data among health institutions is detailed. To validate the proposed solution, we used a real scenario of three health institutions located in the northern region of Brazil and results showed that all messages were encoded and interoperated among the health facilities. © Springer Nature Switzerland AG 2020.

关键词： Middleware

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Discriminative Additive Scale Loss for Deep Imbalanced Classification and Embedding

Discriminative Additive Scale Loss for Deep Imbalanced Class...

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IEEE International Conference on data Mining (ICDM)

作者： Zhao Zhang Weiming Jiang Yang Wang Qiaolin Ye Mingbo Zhao Mingliang Xu Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China AI team Shanghai Shizhuang Information Technology Co. Ltd Shanghai China College of Information Science and Technology Nanjing Forestry University Nanjing China School of Information Science and Technology Donghua University Shanghai China School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9781665423991

Real-world data in emerging applications may suffer from highly-skewed class imbalanced distribution, however how to deal with this kind of problem appropriately through deep learning needs further investigation. In this paper, we mainly propose a novel cross-entropy based loss function, referred to as Additive Scale Loss (ASL), for deep representation learning and imbalanced classification. To deal with the class imbalanced problem, ASL aims at increasing the loss in case of misclassification, which can avoid the superimposed loss values caused by the large amount of easily classified data in the unbalanced database to dominate the loss value of misclassified data. Moreover, in real-world applications, one data source may be used for multiple scenarios, such as classification and embedding learning, however training two separable models to handle these problems is costly, especially in deep learning area. To tackle this issue, we present and integrate a discriminative inter-class separation term into ASL, and propose a discriminative ASL (D-ASL), which can not only improve the classification performance, but also obtain discriminative representations simultaneously. The discriminative inter-class separation term is general, and can be easily integrated to other loss functions, such as CE and FL, as the byproducts. Finally, a new deep convolutional neural network equipped with D-ASL and a fully-connected (FC) layer is proposed, which can classify the imbalanced image data and obtain the discriminative representations at the same time. Extensive experimental results verified the superior performance of our method.

关键词： Deep learning Training Representation learning Additives databases Soft sensors Conferences

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Addressing uncertainty in machine learning-integrated life cycle assessment (ML+LCA)

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Journal of Environmental Management 2025年 389卷

作者： Mehran Akrami Michael D. Porter Lisa M. Colosi Department of Civil and Environmental Engineering University of Virginia 151 Engineer's Way Charlottesville VA 22904 United States School of Data Science University of Virginia Charlottesville VA United States Department of Systems and Information Engineering University of Virginia Charlottesville VA United States

There has been a recent surge in studies applying artificial intelligence, most notably machine learning (ML), to questions about environmental sustainability and climate change. Particularly, there has been growing interest in combining ML and life cycle assessment (LCA) as a means to expand the breadth and depth of LCA studies. However, much of the ML-integrated LCA (ML + LCA) work published to date has not considered the uncertainty of ML modeling. This study explores the application of ML techniques for use in LCA with careful focus on propagating and managing uncertainty. An existing open-access ML + LCA model of hydrothermal biomass treatment was selected as a case study. The benchmark model was rebuilt, and four different uncertainty treatments (cases) were evaluated: (I) no uncertainty analysis, (II) uncertainty analysis for ML only, (III) uncertainty analysis for LCA only (via Monte Carlo), and (IV) uncertainty analysis for both ML and LCA. Three techniques were used to generate ML-based parameter predictions with corresponding prediction intervals (PIs), including Random Forest with Quantile Regression, Artificial Neural Network with Monte Carlo dropout, and Natural Gradient Boosting (NGBoost). Relevant metrics were then used to evaluate the resulting PIs, including Prediction Interval Coverage Probability (PICP), Mean Prediction Interval Width (MPIW), and overall interval score. Results indicate that NGBoost outperforms other ML techniques for the case study of interest, achieving acceptable ML validity measures and much narrower PIs than the other evaluated techniques. For Case I (no uncertainty), the single value Global Warming Potential (GWP) estimate was approximately 1 kg CO 2 eq/Mt feedstock dry weight (DW). For Case II (ML uncertainty only), the interpercentile interval for GWP ranged from −170 to 152 kg CO 2 eq/Mt DW. Case III (LCA uncertainty only) exhibited an even wider interpercentile interval (−225, 288). Case IV (ML and LCA uncertainty) exhi

关键词： Decision-making LCA ML Monte Carlo Prediction interval Uncertainty

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Research of the influence of reliability on qualitative parameters of the transport subnet of the infocommunication system

Research of the influence of reliability on qualitative para...

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2020 International Conference on information Science and Communications Technologies, ICISCT 2020

作者： Nishanbayev, Tuygun Yakubova, Muborak Muradova, Alevtina Amreev, Maksad Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Data Communication Networks and Systems Tashkent city100084 Uzbekistan Academician of Kazakhstan Almaty University of Power Engineering and Telecommunications Department of Radio Engineering Alma-Ata050013 Kazakhstan Department of Telecommunication Engineering Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Tashkent city100084 Uzbekistan Almaty University of Power Engineering and Telecommunications Department of Radio Engineering Alma-Ata050013 Kazakhstan

ISBN: (纸本)9781728199696

The article proposes a model of research the influence of reliability parameters of the transport subnet of an infocommunication system on qualitative network parameters, based on queuing theory, shows the degree of influence of the availability coefficient on the values of the probability of uptime and the probability of loss of message packets of external traffic. © 2020 IEEE.

关键词： Reliability theory

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Graph convolution neural network for weakly supervised abnormality localization in long capsule endoscopy videos

arXiv

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arXiv 2021年

作者： Adewole, Sodiq Fernandes, Philip Jablonski, James Copland, Andrew Porter, Michael Syed, Sana Brown, Donald Department of Systems and Information Engineering University of Virginia CharlottesvilleVA United States Department of Pediatrics School of Medicine University of Virginia CharlottesvilleVA United States School of Data Science University of Virginia CharlottesvilleVA United States

Temporal activity localization in long videos is an important problem. The cost of obtaining frame level label for long Wireless Capsule Endoscopy (WCE) videos is prohibitive. In this paper, we propose an end-to-end temporal abnormality localization for long WCE videos using only weak video level labels. Physicians use Capsule Endoscopy (CE) as a non-surgical and non-invasive method to examine the entire digestive tract in order to diagnose diseases or abnormalities. While CE has revolutionized traditional endoscopy procedures, a single CE examination could last up to 8 hours generating as much as 100,000 frames. Physicians must review the entire video, frameby-frame, in order to identify the frames capturing relevant lesion or abnormality. This, sometimes could be as few as just a single frame. Given this very high level of redundancy, analysing long CE videos can be very tedious, time consuming and also error prone. This paper presents a novel multi-step method for an end-to-end localization of target frames capturing abnormalities of interest in the long video using only weak video labels. First we developed an automatic temporal segmentation using change point detection technique to temporally segment the video into uniform, homogeneous and identifiable segments. Then we employed Graph Convolutional Neural Network (GCNN) to learn a representation of each video segment. Using weak video segment labels, we trained our GCNN model to recognize each video segment as abnormal if it contains at least a single abnormal frame. Finally, leveraging the parameters of the trained GCNN model, we replaced the final layer of the network with a temporal pool layer to localize the relevant abnormal frames within each abnormal video segment. We experimented with multiple real patients' endoscopy videos and achieved an accuracy of 89.9% on the graph classification task and a specificity of 97.5% on the abnormal frames localization task. © 2021, CC BY.

关键词： Endoscopy

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Deep learning for embodied visual navigation research: A survey

arXiv

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arXiv 2021年

作者： Zhu, Fengda Zhu, Yi Lee, Vincent C.S. Liang, Xiaodan Chang, Xiaojun Department of Data Science and AI Faculty of Information Technology Monash University University of Chinese Academy of Sciences School of Intelligent Systems Engineering Sun Yat-sen University School of Computing Technologies RMIT University

"Embodied visual navigation" problem requires an agent to navigate in a 3D environment mainly rely on its first-person observation. This problem has attracted rising attention in recent years due to its wide application in vacuum cleaner, and rescue robot, etc. A navigation agent is supposed to have various intelligent skills, such as visual perceiving, mapping, planning, exploring and reasoning, etc. Building such an agent that observes, thinks, and acts is a key to real intelligence. The remarkable learning ability of deep learning methods empowered the agents to accomplish embodied visual navigation tasks. Despite this, embodied visual navigation is still in its infancy since a lot of advanced skills are required, including perceiving partially observed visual input, exploring unseen areas, memorizing and modeling seen scenarios, understanding cross-modal instructions, and adapting to a new environment, etc. Recently, embodied visual navigation has attracted rising attention of the community, and numerous works has been proposed to learn these skills. This paper attempts to establish an outline of the current works in the field of embodied visual navigation by providing a comprehensive literature survey. We summarize the benchmarks and metrics, review different methods, analysis the challenges, and highlight the state-of-the-art methods. Finally, we discuss unresolved challenges in the field of embodied visual navigation and give promising directions in pursuing future research. © 2021, CC BY.

关键词： Navigation

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Unsupervised shot boundary detection for temporal segmentation of long capsule endoscopy videos

arXiv

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arXiv 2021年

Physicians use Capsule Endoscopy (CE) as a noninvasive and non-surgical procedure to examine the entire gastrointestinal (GI) tract for diseases and abnormalities. A single CE examination could last between 8 to 11 hours generating up to 80,000 frames which is compiled as a video. Physicians have to review and analyze the entire video to identify abnormalities or diseases before making diagnosis. This review task can be very tedious, time consuming and prone to error. While only as little as a single frame may capture useful content that is relevant to the physicians' final diagnosis, frames covering the small bowel region alone could be as much as 50,000. To minimize physicians' review time and effort, this paper proposes a novel unsupervised and computationally efficient temporal segmentation method to automatically partition long CE videos into a homogeneous and identifiable video segments. However, the search for temporal boundaries in a long video using high dimensional framefeature matrix is computationally prohibitive and impracticable for real clinical application. Therefore, leveraging both spatial and temporal information in the video, we first extracted high level frame features using a pretrained CNN model and then projected the high-dimensional frame-feature matrix to lower 1-dimensional embedding. Using this 1-dimensional sequence embedding, we applied the Pruned Exact Linear Time (PELT) algorithm to searched for temporal boundaries that indicates the transition points from normal to abnormal frames and viceversa. The key novelty of this work is in three (3) folds - first, the automated detection of temporal boundaries in long CE video has not been previously considered. Secondly, the reduction in the computational cost of the temporal boundary detection search by using a lower dimensional frame feature embedding;and lastly, the entire temporal segmentation of the CE videos requiring no supervision from medical expert is a new concept. The output of ou

关键词： Image segmentation

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