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Key Opinion Leaders: An Invisible Force in Digital Home Media 8

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Key Opinion Leaders: An Invisible Force in Digital Home Medi...

8th International Conference on Digital Home, ICDH 2020

作者： Wang, Yepeng Bai, Huifeng Li, Ji Wang, Ruomei Liverpool John Moores University Liverpool Business School Liverpool United Kingdom Guilin University of Electronic Technology School of Computer Science and Information Security Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin China Sun Yat-Sen University National Engineering Research Center of Digital Life School of Data and Computer Science Guangdong China

ISBN: (纸本)9781728192345

With the process of home digitalization, short video media begins to infiltrate our daily living area. The key opinion leader, which indicates the person who is known as an influencer, is a modern concept in such short video media. In this paper, we analyze how KOLs appearing in the digital home media (Smart Tvs, phones and pads) affect the consumer goods marketing. Through the questionnaire, we show results of consumers' attitudes towards watching KOLs' short videos and their views of the future trends of KOL marketing. Also, this paper reveals that KOL marketing should be designed an appropriate management mechanism in order to control the actions of the KOLs. © 2020 IEEE.

关键词： Marketing

Robust enhancement of high-harmonic generation from all-dielectric metasurfaces enabled by polarization-insensitive bound states in the continuum

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arXiv 2022年

作者： Xiao, Shuyuan Qin, Meibao Duan, Junyi Liu, Tingting Institute for Advanced Study Nanchang University Nanchang330031 China Jiangxi Key Laboratory for Microscale Interdisciplinary Study Nanchang University Nanchang330031 China School of Physics and Materials Science Nanchang University Nanchang330031 China Institute of Photonic Chips University of Shanghai for Science and Technology Shanghai200093 China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

The emerging all-dielectric platform exhibits high-quality (Q) resonances governed by the physics of bound states in the continuum (BIC) that drives highly efficient nonlinear optical processes. Here we demonstrate the robust enhancement of third-(THG) and fifth-harmonic generation (FHG) from all-dielectric metasurfaces composed of four silicon nanodisks. Through the symmetry breaking, the genuine BIC transforms into the high-Q quasi-BIC resonance with tight field confinement for record high THG efficiency of 3.9 × 10−4 W−2 and FHG efficiency of 4.8 × 10−10 W−4 using a moderate pump intensity of 1 GW/cm2. Moreover, the quasi-BIC and the resonantly enhanced harmonics exhibit polarization-insensitive characteristics due to the special C4 arrangement of meta-atoms. Our results suggest the way for smart design of efficient and robust nonlinear nanophotonic devices. Copyright © 2022, The Authors. All rights reserved.

关键词： Harmonic generation

OPP-Miner: Order-preserving sequential pattern mining

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arXiv 2022年

作者： Wu, Youxi Hu, Qian Li, Yan Guo, Lei Zhu, Xingquan Wu, Xindong School of Artificial Intelligence Hebei University of Technology Tianjin300401 China Hebei Key Laboratory of Big Data Computing Tianjin300401 China School of Artificial Intelligence Hebei University of Technology Tianjin300401 China School of Economics and Management Hebei University of Technology Tianjin300401 China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300401 China Department of Computer Electrical Engineering and Computer Science Florida Atlantic University FL33431 United States Mininglamp Academy of Sciences Mininglamp Technology Beijing100084 China Ministry of Education Hefei230009 China

A time series is a collection of measurements in chronological order. Discovering patterns from time series is useful in many domains, such as stock analysis, disease detection, and weather forecast. To discover patterns, existing methods often convert time series data into another form, such as nominal/symbolic format, to reduce dimensionality, which inevitably deviates the data values. Moreover, existing methods mainly neglect the order relationships between time series values. To tackle these issues, inspired by order-preserving matching, this paper proposes an Order-Preserving sequential Pattern (OPP) mining method, which represents patterns based on the order relationships of the time series data. An inherent advantage of such representation is that the trend of a time series can be represented by the relative order of the values underneath the time series data. To obtain frequent trends in time series, we propose the OPP-Miner algorithm to mine patterns with the same trend (sub-sequences with the same relative order). OPP-Miner employs the filtration and verification strategies to calculate the support and uses pattern fusion strategy to generate candidate patterns. To compress the result set, we also study finding the maximal OPPs. Experiments validate that OPP-Miner is not only efficient and scalable but can also discover similar sub-sequences in time series. In addition, case studies show that our algorithms have high utility in analyzing the COVID-19 epidemic by identifying critical trends and improve the clustering performance. The algorithms and data can be downloaded from https://***/wuc567/Pattern-Mining/tree/master/OPP-Miner. Copyright © 2022, The Authors. All rights reserved.

关键词： Time series

Cloud-Magnetic Resonance Imaging System: In the Era of 6G and artificial intelligence

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arXiv 2023年

作者： Zhou, Yirong Wu, Yanhuang Su, Yuhan Li, Jing Cai, Jianyun You, Yongfu Guo, Di Qu, Xiaobo The Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361104 China The Department of Electronic Science Key Laboratory of Digital Fujian on IoT Communication Architecture and Safety Technology Xiamen University Xiamen361104 China Shanghai Electric Group CO. LTD Shanghai200001 China China Telecom Group Quanzhou362000 China China Mobile Group Xiamen361005 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Magnetic Resonance Imaging (MRI) plays an important role in medical diagnosis, generating petabytes of image data annually in large hospitals. This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure. Additionally, local data processing demands substantial manpower and hardware investments. data isolation across different healthcare institutions hinders cross-institutional collaboration in clinics and research. In this work, we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing, 6G bandwidth, edge computing, federated learning, and blockchain technology. This system is called Cloud-MRI, aiming at solving the problems of MRI data storage security, transmission speed, AI algorithm maintenance, hardware upgrading, and collaborative work. The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw data (ISMRMRD) format. Then, the data are uploaded to the cloud or edge nodes for fast image reconstruction, neural network training, and automatic analysis. Then, the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services. The Cloud-MRI system will save the raw imaging data, reduce the risk of data loss, facilitate inter-institutional medical collaboration, and finally improve diagnostic accuracy and work efficiency. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

EnsembleNTLDetect: An Intelligent Framework for Electricity Theft Detection in Smart Grid

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EnsembleNTLDetect: An Intelligent Framework for Electricity ...

IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Yogesh Kulkarni Sayf Hussain Z Krithi Ramamritham Nivethitha Somu Pune Institute of Computer Technology Pune India College of Engineering Guindy Anna University Chennai India Robert Bosch Centre for Data Science & Artificial Intelligence IIT Madras Chennai India IIT Bombay Mumbai India

ISBN: (纸本)9781665424288

artificial intelligence-based techniques applied to the electricity consumption data generated from the smart grid prove to be an effective solution in reducing Non Technical Loses (NTLs), thereby ensures safety, reliability, and security of the smart energy systems. However, imbalanced data, consecutive missing values, large training times, and complex architectures hinder the real time application of electricity theft detection models. In this paper, we present EnsembleNTLDetect, a robust and scalable electricity theft detection framework that employs a set of efficient data pre-processing techniques and machine learning models to accurately detect electricity theft by analysing consumers’ electricity consumption patterns. This framework utilises an enhanced Dynamic Time Warping Based Imputation (eDTWBI) algorithm to impute missing values in the time series data and leverages the Near-miss undersampling technique to generate balanced ***, stacked autoencoder is introduced for dimensionality reduction and to improve training efficiency. A Conditional Generative Adversarial Network (CTGAN) is used to augment the dataset to ensure robust training and a soft voting ensemble classifier is designed to detect the consumers with aberrant consumption patterns. Furthermore, experiments were conducted on the real-time electricity consumption data provided by the State Grid Corporation of China (SGCC) to validate the reliability and efficiency of EnsembleNTLDetect over the state-of-the-art electricity theft detection models in terms of various quality metrics.

关键词： Training Measurement Heuristic algorithms Time series analysis data models Real-time systems Smart grids

A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation

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arXiv 2024年

作者： Chen, Zhihong Varma, Maya Xu, Justin Paschali, Magdalini Van Veen, Dave Johnston, Andrew Youssef, Alaa Blankemeier, Louis Bluethgen, Christian Altmayer, Stephan Valanarasu, Jeya Maria Jose Muneer, Mohamed Siddig Eltayeb Reis, Eduardo Pontes Cohen, Joseph Paul Olsen, Cameron Abraham, Tanishq Mathew Tsai, Emily B. Beaulieu, Christopher F. Jitsev, Jenia Gatidis, Sergios Delbrouck, Jean-Benoit Chaudhari, Akshay S. Langlotz, Curtis P. Stanford Center for Artificial Intelligence in Medicine and Imaging Stanford University Palo AltoCA United States Department of Radiology Stanford University StanfordCA United States Department of Computer Science Stanford University StanfordCA United States Big Data Institute University of Oxford Oxford United Kingdom Department of Electrical Engineering Stanford University StanfordCA United States Department of Radiology University Hospital Zurich Zürich Switzerland Stability AI London United Kingdom Jülich Supercomputing Centre Jülich Germany LAION Germany Department of Biomedical Data Science Stanford University StanfordCA United States Department of Medicine Stanford University StanfordCA United States

Over 1.4 billion chest X-rays (CXRs) are performed annually due to their cost-effectiveness as an initial diagnostic test. This scale of radiological studies provides a significant opportunity to streamline CXR interpretation and documentation. While foundation models are a promising solution, the lack of publicly available large-scale datasets and benchmarks inhibits their iterative development and real-world evaluation. To overcome these challenges, we constructed a large-scale dataset (CheXinstruct), which we utilized to train a vision-language foundation model (CheXagent). We systematically demonstrated competitive performance across eight distinct task types on our novel evaluation benchmark (CheXbench). Beyond technical validation, we assessed the real-world utility of CheXagent in directly drafting radiology reports. Our clinical assessment with eight radiologists revealed a 36% time saving for residents using CheXagent-drafted reports, while attending radiologists showed no significant time difference editing resident-drafted or CheXagent-drafted reports. The CheXagent-drafted reports improved the writing efficiency of both radiology residents and attending radiologists in 81% and 61% of cases, respectively, without loss of quality. Overall, we demonstrate that CheXagent can effectively perform a variety of CXR interpretation tasks and holds potential to assist radiologists in routine clinical workflows. © 2024, CC BY.

关键词： Visual languages

Machine learning for modelling unstructured grid data in computational physics: a review

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arXiv 2025年

作者： Cheng, Sibo Bocquet, Marc Ding, Weiping Finn, Tobias Sebastian Fu, Rui Fu, Jinlong Guo, Yike Johnson, Eleda Li, Siyi Liu, Che Moro, Eric Newton Pan, Jie Piggott, Matthew Quilodran, Cesar Sharma, Prakhar Wang, Kun Xiao, Dunhui Xue, Xiao Zeng, Yong Zhang, Mingrui Zhou, Hao Zhu, Kewei Arcucci, Rossella CEREA ENPC EDF R&D Institut Polytechnique de Paris Île-de-France France School of Artificial Intelligence and Computer Science Nantong University Jiangsu Nantong226019 China School of Mathematical Sciences Key Laboratory of Intelligent Computing and Applications Tongji University Shanghai200092 China School of Engineering and Materials Science Faculty of Science and Engineering Queen Mary University of London LondonE1 4NS United Kingdom Zienkiewicz Centre for Modelling Data and AI Faculty of Science and Engineering Swansea University SwanseaSA1 8EN United Kingdom Department of Computer Science and Engineering Hong Kong university of science and technology Hong Kong Department of Earth Science & Engineering Imperial College London LondonSW7 2AZ United Kingdom Tianjin Key Laboratory of Imaging and Sensing Microelectronics Technology School of Microelectronics Tianjin University Tianjin300072 China Centre for Health Informatics Cumming School of Medicine University of Calgary CalgaryABT2N 1N4 Canada Department of Community Health Sciences Cumming School of Medicine University of Calgary CalgaryABT2N 1N4 Canada Undaunted Grantham Institute for Climate Change and the Environment Imperial College London LondonSW7 2AZ United Kingdom Culham Campus AbingdonOX14 3DB United Kingdom Centre for Computational Science Department of Chemistry University College London LondonWC1E 6BT United Kingdom Concordia Institute for Information Systems Engineering Concordia University MontrealQCH3G 1M8 Canada School of Mechanical Medical and Process Engineering Faculty of Engineering Queensland University of Technology BrisbaneQLD Australia Department of Chemical Engineering University College London LondonWC1E 6BT United Kingdom

Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for conventional machine learning (ML) techniques. This paper provides a comprehensive review of advanced ML methodologies designed to handle unstructured grid data in high-dimensional dynamical systems. Key approaches discussed include graph neural networks, transformer models with spatial attention mechanisms, interpolation-integrated ML methods, and meshless techniques such as physics-informed neural networks. These methodologies have proven effective across diverse fields, including fluid dynamics and environmental simulations. This review is intended as a guidebook for computational scientists seeking to apply ML approaches to unstructured grid data in their domains, as well as for ML researchers looking to address challenges in computational physics. It places special focus on how ML methods can overcome the inherent limitations of traditional numerical techniques and, conversely, how insights from computational physics can inform ML development. To support benchmarking, this review also provides a summary of open-access datasets of unstructured grid data in computational physics. Finally, emerging directions such as generative models with unstructured data, reinforcement learning for mesh generation, and hybrid physics-data-driven paradigms are discussed to inspire future advancements in this evolving field. © 2025, CC BY-NC-SA.

关键词： Mesh generation

DINOv2-powered Few-Shot Semantic Segmentation: A Unified Framework via Cross-Model Distillation and 4D Correlation Mining

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arXiv 2025年

作者： Zhuo, Wei Tang, Zhiyue Xue, Wufeng Ding, Hao Shen, Linlin School of Artificial Intelligence Shenzhen University Shenzhen518060 China National Engineering Laboratory of Big Data System Computing Technology Shenzhen University China School of Biomedical Engineering Shenzhen University Medical School Shenzhen University China School of Computer Science and Software Engineering Shenzhen University China

Few-shot semantic segmentation has gained increasing interest due to its generalization capability, i.e., segmenting pixels of novel classes requiring only a few annotated images. Prior work has focused on meta-learning for support-query matching, with extensive development in both prototype-based and aggregation-based methods. To address data scarcity, recent approaches have turned to foundation models to enhance representation transferability for novel class segmentation. Among them, a hybrid dual-modal framework including both DINOv2 and SAM has garnered attention due to their complementary capabilities. We wonder "can we build a unified model with knowledge from both foundation models?" To this end, we propose FS-DINO, with only DINOv2’s encoder and a lightweight segmenter. The segmenter features a bottleneck adapter, a meta-visual prompt generator based on dense similarities and semantic embeddings, and a decoder. Through coarse-to-fine cross-model distillation, we effectively integrate SAM’s knowledge into our lightweight segmenter, which can be further enhanced by 4D correlation mining on support-query pairs. Extensive experiments on COCO-20i, PASCAL-5i, and FSS-1000 demonstrate the effectiveness and superiority of our method. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19

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Informatics in Medicine Unlocked 2022年 28卷 100840页

作者： Hasan, Md. Imran Rahman, Md Habibur Islam, M. Babul Islam, Md Zahidul Hossain, Md Arju Moni, Mohammad Ali Department of Computer Science and Engineering Islamic University Kushtia 7003 Bangladesh Department of Electrical and Electronic Engineering University of Rajshahi 6205 Bangladesh Department of Information and Communication Technology Islamic University Kushtia 7003 Bangladesh Department of Biotechnology and Genetic Engineering Mawlana Bhashani Science and Technology University Tangail 1092 Bangladesh Department of Computer Science and Engineering Pabna University of Science and Technology Pabna 6600 Bangladesh Artificial Intelligence & Digital Health Data Science School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St Lucia QLD 4072 Australia

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection results in the development of a highly contagious respiratory ailment known as new coronavirus disease (COVID-19). Despite the fact that the prevalence of COVID-19 continues to rise, it is still unclear how people become infected with SARS-CoV-2 and how patients with COVID-19 become so unwell. Detecting biomarkers for COVID-19 using peripheral blood mononuclear cells (PBMCs) may aid in drug development and treatment. This research aimed to find blood cell transcripts that represent levels of gene expression associated with COVID-19 progression. Through the development of a bioinformatics pipeline, two RNA-Seq transcriptomic datasets and one microarray dataset were studied and discovered 102 significant differentially expressed genes (DEGs) that were shared by three datasets derived from PBMCs. To identify the roles of these DEGs, we discovered disease-gene association networks and signaling pathways, as well as we performed gene ontology (GO) studies and identified hub protein. Identified significant gene ontology and molecular pathways improved our understanding of the pathophysiology of COVID-19, and our identified blood-based hub proteins TPX2, DLGAP5, NCAPG, CCNB1, KIF11, HJURP, AURKB, BUB1B, TTK, and TOP2A could be used for the development of therapeutic intervention. In COVID-19 subjects, we discovered effective putative connections between pathological processes in the transcripts blood cells, suggesting that blood cells could be used to diagnose and monitor the disease's initiation and progression as well as developing drug therapeutics. © 2022 The Authors

关键词： Biomarker signatures Blood common gene COVID-19 Differentially expressed genes Gene ontology PBMCs cell