检索结果-内蒙古大学图书馆

GPPT: Graph pyramid pooling transformer for visual scene

学校读者我要写书评

暂无评论

Neurocomputing 2025年 647卷

作者： Li, Zhi-Peng Liu, Wen-Jian Sun, Xin Pan, Yi-Jie Gribova, Valeriya Filaretov, Vladimir Fedorovich Cohn, Anthony G. Huang, De-Shuang Ningbo Institute of Digital Twin Eastern Institute of Technology Tongxin Road No. 568 Zhejiang Ningbo315201 China Faculty of Data Science City University of Macau 999078 China MEGAROBO Technologies Linglong Street No. 88 Jiangsu Suzhou215128 China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing211106 China Institute of Automation and Control Processes Far Eastern Branch of Russian Academy of Sciences Russia School of Computing University of Leeds LeedsLS2 9JT United Kingdom Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology Shanghai East Hospital School of Life Sciences and Technology Tongji University Shanghai200123 China

In the field of computer vision, network architectures are critical to the performance of tasks. Vision Graph Neural Network (ViG) has shown remarkable results in handling various vision tasks with their unique characteristics. However, the lack of multi-scale information in ViG limits its expressive capability. To address this challenge, we propose a Graph Pyramid Pooling Transformer (GPPT), which aims to enhance the performance of the model by introducing multi-scale feature learning. The core advantage of GPPT is its ability to effectively capture and fuse feature information at different scales. Specifically, it first generates multi-level pooled graphs using a graph pyramid pooling structure. Next, it encodes features at each scale using a weight-shared Graph Convolutional Neural Network (GCN). Then, it enhances information exchange across scales through a cross-scale feature fusion mechanism. Finally, it captures long-range node dependencies using a transformer module. The experimental results demonstrate that GPPT achieves exceptional performance across various visual scenes, including image classification, and object detection, highlighting its generality and validity. © 2025 Elsevier B.V.

关键词： Convolutional neural networks

Determination factors of internet advertising affecting internet users’ behavior using canonical correlation analysis

学校读者我要写书评

暂无评论

AIP Conference Proceedings 2024年第1期2895卷

作者： Farah Nabilah Zabidi Nurain Ibrahim Nur Izzah Atiqah Nurul Athirah Norzaidi Salsabilla Kamaludin Adina Najwa Kamarudin School of Mathematical Sciences College of Computing Informatics and Media Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Kompleks Al-Khawarizmi Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia Regional Department Suu Balm Malaysia Livemed Sdn Bhd Aurora Place Plaza No 1 Persiaran Bukit Jalil 57000 Kuala Lumpur Malaysia Federal Government Administrative Centre Block C6 Complex C Lebuh Perdana Selatan Presint 1 62514 Putrajaya Wilayah Persekutuan Putrajaya Malaysia Department of Mathematical Sciences Faculty of Science Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia

One of the most important and fundamental parts of today’s business world is advertising. Advertising is necessary for both business and consumers. We can now observe that the advertising industry has experienced substantial changes and is now employed globally. An advertising company has found this platform to be quite useful for increasing revenue by influencing the online users’ behaviour, which may change in either a positive or bad way. Therefore, the primary topic of this study paper is on how internet advertising affects consumers’ online behaviour in UiTM Shah Alam. Through surveys, we gathered information about two sets of variables: Internet Users’ Behaviour (Purchase Intention, Effective Medium, Frequency of Browsing, and Demographic Variable), and sets of variables Internet Advertising (Webpage Content, Informativeness and Interactivity). To determine the aspects of internet advertising influencing internet users’ behaviour, Canonical Correlation Analysis was applied to the data that was gathered. Internet advertising and internet users’ behaviour are significantly correlated, according to the results analysis. As a result, Internet advertising plays a big role in influencing consumers’ behaviour because of its close connection to their purchasing decisions. Based on findings, it can be concluded that internet advertising plays significant roles in examining internet users’ behaviour, which may be advantageous for many industries globally, particularly in current era of digitalization. With information on consumer behaviour both now and in the future, these findings can assist marketers and advertisers in developing more effective online advertising strategies that better engage young adults’ attention and influence their behaviour, particularly with regard to purchase intention, leading to a misalignment. It will then be able to create more successful online advertising.

关键词：

Environment Scan of Generative AI Infrastructure for Clinical and Translational science

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Idnay, Betina Xu, Zihan Adams, William G. Adibuzzaman, Mohammad Anderson, Nicholas R. Bahroos, Neil Bell, Douglas S. Bumgardner, Cody Campion, Thomas Castro, Mario Cimino, James J. Cohen, I. Glenn Dorr, David Elkin, Peter L. Fan, Jungwei W. Ferris, Todd Foran, David J. Hanauer, David Hogarth, Mike Huang, Kun Kalpathy-Cramer, Jayashree Kandpal, Manoj Karnik, Niranjan S. Katoch, Avnish Lai, Albert M. Lambert, Christophe G. Li, Lang Lindsell, Christopher Liu, Jinze Lu, Zhiyong Luo, Yuan McGarvey, Peter Mendonca, Eneida A. Mirhaji, Parsa Murphy, Shawn Osborne, John D. Paschalidis, Ioannis C. Harris, Paul A. Prior, Fred Shaheen, Nicholas J. Shara, Nawar Sim, Ida Tachinardi, Umberto Waitman, Lemuel R. Wright, Rosalind J. Zai, Adrian H. Zheng, Kai Lee, Sandra Soo-Jin Malin, Bradley A. Natarajan, Karthik Price, W. Nicholson Zhang, Rui Zhang, Yiye Xu, Hua Bian, Jiang Weng, Chunhua Peng, Yifan Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY United States Department of Population Health Sciences Weill Cornell Medicine New YorkNY United States Department of Pediatrics Boston Medical Center BostonMA United States Chobanian & Avedisian School of Medicine Boston University BostonMA United States Oregon Clinical and Translational Research Institute Oregon Health and Science University PortlandOR United States Department of Public Health Sciences University of California Davis DavisCA United States Keck School of Medicine University of Southern California Los AngelesCA United States Department of Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Pathology and Laboratory Medicine University of Kentucky College of Medicine LexingtonKY United States Division of Pulmonary Critical Care and Sleep Medicine University of Kansas School of Medicine Kansas CityKS United States Department of Biomedical Informatics and Data Science Heersink School of Medicine University of Alabama BirminghamAL United States Harvard Law School Petrie-Flom Center for Health Law Policy Biotechnology and Bioethics Harvard University CambridgeMA United States Department of Biomedical Informatics University at Buffalo BuffaloNY United States Center for Clinical and Translational Science Mayo Clinic RochesterMN United States Technology and Digital Solutions Stanford Medicine Stanford University StanfordCA United States Center for Biomedical Informatics Rutgers Cancer Institute New BrunswickNJ United States Department of Learning Health Sciences University of Michigan Medical School Ann ArborMI United States University of California San Diego La Jolla CA United States Department of Biostatistics and Health Data Science School of Medicine Indiana University IndianapolisIN United States Division of Artificial Medical Intelligence in Ophthalmology Univ

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the Clinical and Translational science Award (CTSA) Program led by the National Center for Advancing Translational sciences (NCATS) of the National Institutes of Health (NIH) at the United States. With the rapid advancement of GenAI technologies, including large language models (LLMs), healthcare institutions face unprecedented opportunities and challenges. This research explores the current status of GenAI integration, focusing on stakeholder roles, governance structures, and ethical considerations by administering a survey among leaders of health institutions (i.e., representing academic medical centers and health systems) to assess the institutional readiness and approach towards GenAI adoption. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The study highlights significant variations in governance models, with a strong preference for centralized decision-making but notable gaps in workforce training and ethical oversight. Moreover, the results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis also reveals concerns regarding GenAI bias, data security, and stakeholder trust, which must be addressed to ensure the ethical and effective implementation of GenAI technologies. This study offers valuable insights into the challenges and opportunities of GenAI integration in healthcare, providing a roadmap for institutions aiming to leverage GenAI for improved quality of care and operational efficiency. © 2024, CC BY.

关键词： Electronic health record

An AI-based approach to detect cognitive impairment from digital voice recordings of neuropsychological assessments

学校读者我要写书评

暂无评论

Alzheimer's & Dementia 2022年第S7期18卷

作者： Samad Amini Boran Hao Lifu Zhang Mengting Song Aman Gupta Cody Karjadi Vijaya B. Kolachalama Rhoda Au Ioannis Paschalidis Division of Systems Engineering Boston University Boston MA USA Department of Electrical & Computer Engineering Boston University Boston MA USA The Framingham Heart Study Boston University School of Medicine Boston MA USA Hariri Institute for Computing and Computational Science & Engineering Boston University Boston MA USA Boston University School of Medicine Boston MA USA Faculty of Computing & Data Sciences Boston University Boston MA USA The Framingham Heart Study Boston University School of Medicine Boston University School of Public Health Boston MA USA Department of Biomedical Engineering Boston University Boston MA USA

Background Reliable cognitive impairment screening tools that are easy to administer and minimally time consuming are greatly needed. Given the high sensitivity of neuropsychological (NP) exams in detection of cognitive decline, we seek to develop an automated screening tool to detect dementia and mild cognitive impairment (MCI) based on digital voice recordings of NP assessments. This could enable wide-spread screening for dementia and accelerate preventative efforts. Method We used natural language processing methods to create a screening tool that identifies different stages of dementia based on automated transcription of digital voice recordings. The transcribed sentences were classified into 8 main sub-tests including memory assessment, naming and language skill, verbal fluency, general questions, etc. Using the idea of transfer learning, we encoded the participants' sentences into quantitative data. This data and the participants’ demographic variables such as age, sex, Apoe gene, and education were employed to train and test three binary classification tasks, (I) Normal cognition versus Dementia, (II) Normal/MCI versus Dementia, and (III) Normal versus MCI. Result We evaluated the performance of the classification tasks using the digital voice recordings of NP assessments, collected from the Framingham Heart Study, containing 410 cognitively intact subjects, 387 MCI, and 287 subjects with dementia. The average Area Under the Curve (AUC) on the held-out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal from Dementia, Normal or MCI from Dementia, and Normal from MCI, respectively. Looking at the importance of the sub-tests in differentiating MCI from Normal, we note that general questions can be more useful for assessment of MCI, whereas verbal fluency would not be as useful in this task. Conclusion The proposed approach offers a fully automated identification of MCI and dementia based on a recorded NP test, providing an opportunity to develo

关键词：

Vessel-CAPTCHA: An efficient learning framework for vessel annotation and segmentation

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Dang, Vien Ngoc Galati, Francesco Cortese, Rosa Di Giacomo, Giuseppe Marconetto, Viola Mathur, Prateek Lekadir, Karim Lorenzi, Marco Prados, Ferran Zuluaga, Maria A. Data Science Department EURECOM Sophia Antipolis France Artificial Intelligence in Medicine Lab Facultat de Matemàtiques I Informàtica Universitat de Barcelona Spain Queen Square MS Centre Dept of Neuroinflammation UCL Queen Square Institute of Neurology Faculty of Brain Sciences University College London United Kingdom Politecnico di Torino Turin Italy Université Côte d'Azur Inria Sophia Antipolis Epione Research Group Valbonne France Centre for Medical Image Computing Dept of Medical Physics and Bioengineering University College London United Kingdom National Institute for Health Research University College London Hospitals Biomedical Research Centre London United Kingdom e-Health Center Universitat Oberta de Catalunya Barcelona Spain

Deep learning techniques for 3D brain vessel image segmentation have not been as successful as in the segmentation of other organs and tissues. This can be explained by two factors. First, deep learning techniques tend to show poor performances at the segmentation of relatively small objects compared to the size of the full image. Second, due to the complexity of vascular trees and the small size of vessels, it is challenging to obtain the amount of annotated training data typically needed by deep learning methods. To address these problems, we propose a novel annotation-efficient deep learning vessel segmentation framework. The framework avoids pixel-wise annotations, only requiring weak patch-level labels to discriminate between vessel and non-vessel 2D patches in the training set, in a setup similar to the CAPTCHAs used to differentiate humans from bots in web applications. The user-provided weak annotations are used for two tasks: 1) to synthesize pixel-wise pseudo-labels for vessels and background in each patch, which are used to train a segmentation network, and 2) to train a classifier network. The classifier network allows to generate additional weak patch labels, further reducing the annotation burden, and it acts as a noise filter for poor quality images. We use this framework for the segmentation of the cerebrovascular tree in Time-of-Flight angiography (TOF) and Susceptibility-Weighted Images (SWI). The results show that the framework achieves state-of-the-art accuracy, while reducing the annotation time by ∼77% w.r.t. learning-based segmentation methods using pixel-wise labels for training. Copyright © 2021, The Authors. All rights reserved.

关键词： Pixels

eROSITA clusters dynamical state and their impact on the BCG luminosity

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Zenteno, A. Kluge, M. Kharkrang, R. Hernandez-Lang, D. Damke, G. Saro, A. Monteiro-Oliveira, R. Carrasco, E.R. Salvato, M. Comparat, J. Fabricius, M. Snigula, J. Arevalo, P. Cuevas, H. Castellon, J.L. Nilo Ramirez, A. Astudillo, S. Véliz Landriau, M. Myers, A.D. Schlafly, E. Valdes, F. Weaver, B. Mohr, J.J. Grandis, S. Klein, M. Liu, A. Bulbul, E. Zhang, X. Sanders, J.S. Bahar, Y.E. Ghirardini, V. Ramos, M. Balzer, F. Cerro Tololo Inter-American Observatory NSF’s National Optical-Infrared Astronomy Research Laboratory Casilla 603 La Serena Chile Max Planck Institute for Extraterrestrial Physics Giessenbachstrasse 1 Garching85748 Germany Universitè Paris-Saclay CNRS Institut dÁstrophysique Spatiale Orsay91405 France Faculty of Physics Ludwig-Maximilians-Universität München Scheinerstr. 1 Munich81679 Germany INAF - Osservatorio Astronomico di Trieste via G. B. Tiepolo 11 Trieste34143 Italy IFPU - Institute for Fundamental Physics of the Universe Via Beirut 2 Trieste34014 Italy Astronomy Unit Department of Physics University of Trieste via Tiepolo 11 Trieste34131 Italy INFN - National Institute for Nuclear Physics Via Valerio 2 TriesteI-34127 Italy ICSC - Italian Research Center on High Performance Computing Big Data and Quantum Computing Italy Institute of Astronomy and Astrophysics Academia Sinica Taipei10617 Taiwan International Gemini Observatory NSF NOIRLab Casilla 603 La Serena Chile Instituto de Física y Astronomía Universidad de Valparaíso Gran Bretaña 1111 Valparaíso Chile Chile Departamento de Astronomía Universidad de La Serena Av. Raúl Bitrán 1305 La Serena Chile Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Department of Physics & Astronomy University of Wyoming 1000 E. University Dept. 3905 LaramieWY82071 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States 950 N. Cherry Ave TucsonAZ85732 United States Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstr. 25/8 Innsbruck6020 Austria

Context. The SRG (Spectrum Roentgen Gamma)/eROSITA first public release contains 12,247 clusters and groups from the first 6 months of operation. We use the offset between the Brightest Cluster Galaxy (BCG) and the X–ray peak (DBCG−X) to classify the cluster dynamical state of 3,946 galaxy clusters and groups. Aims. We aim to investigate the evolution of the merger and relaxed cluster distributions with redshift and mass, and their impact on the BCG. Methods. We use the X–ray peak from the eROSITA survey and the BCG position from the DECaLS DR10 optical data, which includes the DECam eROSITA Survey optical data, to measure the DBCG−X offset. We model the distribution of DBCG−X, in units of r500, as the sum of two Rayleigh distributions representing the cluster’s relaxed and disturbed populations, and explore their evolution with redshift and mass. To explore the impact of the cluster’s dynamical state on the BCG luminosity, we separate the main sample according to the dynamical state. We define clusters as relaxed if DBCG−X500, disturbed if DBCG−X>0.5r500, and as ‘diverse’ if 0.25BCG−X Copyright © 2025, The Authors. All rights reserved.

关键词： Photons

Progression subtypes in Parkinson's disease: Results of an AI-based multi-cohort analysis

学校读者我要写书评

暂无评论

Clinical Neurophysiology 2024年 159卷 e39-e39页

作者： Hähnel, T. Raschka, T. Sapienza, S. Klucken, J. Glaab, E. Corvol, J. Falkenburger, B. Fröhlich, H. Department of Neurology University Hospital and Faculty of Medicine Carl Gustav Carus TUD Dresden University of Technology Dresden Germany Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Sankt Augustin Germany Bonn-Aachen International Center for IT University of Bonn Bonn Germany Biomedical Data Science Luxembourg Centre for Systems Biomedicine (LCSB) University of Luxembourg Esch-sur-Alzette Luxembourg Luxembourg Institute of Health (LIH) Strassen Luxembourg Centre Hospitalier de Luxembourg (CHL) Luxembourg Sorbonne Université Paris Brain Institute – ICM Inserm CNRS Assistance Publique Hôpitaux de Paris Pitié-Salpêtrière Hospital Department of Neurology Paris France German Center for Neurodegenerative Diseases (DZNE) Tatzberg 41 01307 Dresden Germany

基于MeerKAT和FAST探索球状星团:脉冲星偏振研究（英文）

学校读者我要写书评

暂无评论

science Bulletin 2025年第10期 1568-1571页

作者：张蕾 Federico Abbate 李菂 Andrea Possenti Matthew Bailes Alessandro Ridolfi Paulo C.C.Freire Scott M.Ransom 张永坤郭猛尼盟萌胡佳乐冯毅王培张洁支启军 National Astronomical Observatories Chinese Academy of Sciences Centre for Astrophysics and Supercomputing Swinburne University of Technology INAF-Osservatorio Astronomico di Cagliari Max-Planck-Institut für Radioastronomie New Cornerstone Science Laboratory Department of AstronomyTsinghua University ARC Center of Excellence for Gravitational Wave Discovery (OzGrav) Swinburne University of Technology Faculty of Physics University of Bielefeld National Radio Astronomy Observatory National Supercomputing Center in Jinan Qilu University of Technology Jinan Institute of Supercomputing Technology Research Center for Intelligent Computing Platforms Zhejiang Laboratory Institute for Astronomy School of PhysicsZhejiang University College of Physics and Electronic Engineering Qilu Normal University Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University

Magnetic fields are pervasive throughout the Universe. They are integral to a wide array of astrophysical processes that span various physical scales and field strengths. The Galactic magnetic field,in particular, holds significant importance in shaping the evolution of our Galaxy. However, our understanding of its behavior on small scales remains poor, especially when considering its penetration into the Galactic halo [1].

关键词：

The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Ma, Jun Xie, Ronald Ayyadhury, Shamini Ge, Cheng Gupta, Anubha Gupta, Ritu Gu, Song Zhang, Yao Lee, Gihun Kim, Joonkee Lou, Wei Li, Haofeng Upschulte, Eric Dickscheid, Timo de Almeida, José Guilherme Wang, Yixin Han, Lin Yang, Xin Labagnara, Marco Gligorovski, Vojislav Scheder, Maxime Rahi, Sahand Jamal Kempster, Carly Pollitt, Alice Espinosa, Leon Mignot, Tâm Middeke, Jan Moritz Eckardt, Jan-Niklas Li, Wangkai Li, Zhaoyang Cai, Xiaochen Bai, Bizhe Greenwald, Noah F. Van Valen, David Weisbart, Erin Cimini, Beth A. Cheung, Trevor Brück, Oscar Bader, Gary D. Wang, Bo Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Laboratory Medicine and Pathobiology University of Toronto TorontoON Canada Vector Institute TorontoON Canada Department of Molecular Genetics University of Toronto TorontoON Canada Donnelly Centre University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada School of Medicine and Pharmacy Ocean University of China Qingdao China New Delhi India Laboratory Oncology Dr. BRA-IRCH All India Institute of Medical Sciences New Delhi India Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Shanghai Artificial Intelligence Laboratory Shanghai China Graduate School of AI KAIST Seoul Korea Republic of Shenzhen Research Institute of Big Data Shenzhen China Shenzhen China Helmholtz AI Research Center Jülich Jülich Germany Faculty of Mathematics and Natural Sciences Institute of Computer Science Heinrich Heine University Düsseldorf Düsseldorf Germany Hinxton United Kingdom Champalimaud Foundation - Centre for the Unknown Lisbon Portugal Department of Bioengineering Stanford University Palo AltoCA United States Tandon School of Engineering New York University New YorkNY United States School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Lausanne Switzerland School of Biological Sciences University of Reading Reading United Kingdom Laboratoire de Chimie Bactérienne CNRS Université Aix Marseille UMR Institut de Microbiologie de la Méditerranée Marseille France Department of Internal Medicine I University Hospital Dresden Technical University Dresden Dresden Germany Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany Department of Automation University of Science and Technology of China Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei Chi

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. © 2023, CC BY-NC-ND.

关键词： Deep learning