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arXiv 2023年

作者： El Kerim, Ahmed Gosselet, Pierre Magoulès, Frédéric Laboratory of Mathematics in Interaction with Computer Science CentraleSupélec Paris-Saclay University Gif-sur-Yvette France Lamcube University of Lille CNRS Centrale Lille Lille France

A novel approach is being developed to introduce a parallel asynchronous implementation of non-intrusive global-local coupling. This study examines scenarios involving numerous patches, including those covering the entire structure. By leveraging asynchronous, the method aims to minimize reliance on communication, handle failures effectively, and address load imbalances. Detailed insights into the methodology are presented, accompanied by a demonstration of its performance through an academic case study. © 2023, CC BY-NC-ND.

关键词： Domain decomposition methods

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Dynamic Localisation of Spatial-Temporal Graph Neural Network

arXiv

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arXiv 2025年

作者： Duan, Wenying Guo, Shujun Zhou, Zimu Huang, Wei Rao, Hong He, Xiaoxi Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction Nanchang University Nanchang China School of Mathematics and Computer Sciences Nanchang University Nanchang China School of Data Science City University of Hong Kong Hong Kong School of Software Nanchang University Nanchang China Faculty of Science and Technology University of Macau China

Spatial-temporal data, fundamental to many intelligent applications, reveals dependencies indicating causal links between present measurements at specific locations and historical data at the same or other locations. Within this context, adaptive spatial-temporal graph neural networks (ASTGNNs) have emerged as valuable tools for modelling these dependencies, especially through a data-driven approach rather than pre-defined spatial graphs. While this approach offers higher accuracy, it presents increased computational demands. Addressing this challenge, this paper delves into the concept of localisation within ASTGNNs, introducing an innovative perspective that spatial dependencies should be dynamically evolving over time. We introduce DynAGS, a localised ASTGNN framework aimed at maximising efficiency and accuracy in distributed deployment. This framework integrates dynamic localisation, time-evolving spatial graphs, and personalised localisation, all orchestrated around the Dynamic Graph Generator, a light-weighted central module leveraging cross attention. The central module can integrate historical information in a node-independent manner to enhance the feature representation of nodes at the current moment. This improved feature representation is then used to generate a dynamic sparse graph without the need for costly data exchanges, and it supports personalised localisation. Performance assessments across two core ASTGNN architectures and nine real-world datasets from various applications reveal that DynAGS outshines current benchmarks, underscoring that the dynamic modelling of spatial dependencies can drastically improve model expressibility, flexibility, and system efficiency, especially in distributed settings. © 2025, CC BY-NC-ND.

关键词： Spatio-temporal data

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Confidence-oriented Contrastive Graph Clustering

Confidence-oriented Contrastive Graph Clustering

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International Joint Conference on Neural Networks (IJCNN)

作者： Yan-Di Huang Guang-Yu Zhang Dong Huang Chang-Dong Wang Yang Liu Enbo Huang College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-Sen University Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Nanning Normal University Nanning China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Contrastive clustering has recently been an emerging topic in deep unsupervised learning. Nevertheless, the previous works mostly adopt the stochastic data augmentations, which easily leads to the semantic drift problem by limited transformations. Moreover, these approaches ignore the data distribution information when generating the positive and negative pair-wise samples. In light of this, this paper proposes a simple yet effective unsupervised clustering network termed Confidence-oriented Contrastive Graph Clustering (CoCGC). Particularly, we design an end-to-end network paradigm with un-shared weights, among which a hybrid graph filter is utilized to generate two views of reliable augmentations. Guided by the non-dominated sorting theory, we further construct a confidence-oriented sample set from the latent data distribution perspective. By considering the local density and cluster distribution of the embedding representations, the discriminative sample pairs can be derived from the confidence-oriented sets in a two-view contrastive manner. Finally, a cross-view neighbor contrastive loss is devised for better exploiting the self-supervised network signals. Extensive experimental results on five benchmark datasets demonstrate the effectiveness of our method against the existing state-of-the-art deep graph clustering methods.

关键词： Network topology Semantics Neural networks Reliability theory Reliability engineering Information filters Filtering theory

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Dynamic Localisation of Spatial-Temporal Graph Neural Network 25

Dynamic Localisation of Spatial-Temporal Graph Neural Networ...

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Wenying Duan Shujun Guo Zimu Zhou Wei Huang Hong Rao Xiaoxi He Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction Nanchang University Nanchang China School of Mathematics and Computer Sciences Nanchang University Nanchang China School of Data Science City University of Hong Kong Hong Kong Hong Kong School of Software Nanchang University Nanchang China Faculty of Science and Technology University of Macau Macao Macao Macao

ISBN: (纸本)9798400712456

关键词： graph sparsification

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Molecular Profiling of Plasma-Derived Extracellular Vesicle Cargo in Duchenne Muscular Dystrophy Using Integration of Ftir Spectroscopy and Machine Learning: Revealing Disease Signatures

SSRN

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SSRN 2024年

作者： Natesan Sella, Raja Rajavel, Archana Essakipillai, Narayanan Anbazhagan, Ramajayam Ramakrishnan, Jayashree Venkataraman, Viswanathan Membrane Protein Interaction Laboratory Department of Genetic Engineering School of Bioengineering SRM Institute of Science and Technology Chengalpattu Tamilnadu Kattankulathur603 203 India Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology Chengalpattu Tamilnadu Kattankulathur603 203 India Department of Mathematics Faculty of Engineering and Technology SRM Institute of Science and Technology Chengalpattu Tamil Nadu Kattankulathur603 203 India Department of Paediatrics Neurology Apollo Children’s Hospital Thousands Lights Tamilnadu Chennai600 006 India

Purpose:To identify and monitor the FTIR spectral signatures of plasma extracellular vesicles (EVs) from Duchenne Muscular Dystrophy (DMD) patients at different stages with Healthy controls using machine learning *** and Methods:Whole blood samples were collected from the DMD (n=30) and Healthy controls (n=12). EVs were extracted by the Total Exosome Isolation (TEI) Method and resuspended in 1XPBS. We characterize the morphology, size, particle count, and surface markers (CD9, Alix, and Flotillin) by HR-TEM, NTA, and Western Blot analysis. The mid-IR spectra were recorded from (400-4000 cm-1) by Bruker ALPHA II FTIR spectrometer model, which was equipped with an attenuated total reflection (ATR) module. Machine learning algorithms like Principal Component Analysis (PCA) and random Forest (RF) for dimensionality reduction and classifying the two study groups based on the FTIR spectra. The model performance was evaluated by a confusion matrix and the sensitivity, specificity, and Receiving Operating Characteristic Curve (ROC) was calculated ***:Alterations in Amide I & II (1470-1700 cm-1) and lipid (2800-3000 cm-1) regions in FTIR spectra of DMD compared with healthy controls. The PCA-RF model classified correctly the two study groups in the range of 400-4000cm-1 with a sensitivity of 20 %, specificity of 87.50%, accuracy of 71.43%, precision of 33.33%, and 5-fold cross-validation accuracy of 82%. We analyzed the ten different spectral regions which showed statistically significant at P © 2024, The Authors. All rights reserved.

关键词： Fourier transform infrared spectroscopy

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Image-guided Breast Biopsy of MRI-visible Lesions with a Hand-mounted Motorised Needle Steering Tool

Image-guided Breast Biopsy of MRI-visible Lesions with a Han...

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International Symposium on Medical Robotics (ISMR)

作者： Marta Lagomarsino Vincent Groenhuis Maura Casadio Marcel K. Welleweerd Françoise J. Siepel Stefano Stramigioli Robotics and Mechatronics (RAM) Laboratory Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede The Netherlands University of Genoa Genoa Italy Human-Robot Interfaces and Physical Interaction (HRII) Laboratory Istituto Italiano di Tecnologia Genoa Italy Politecnico di Milano Milan Italy

ISBN: (纸本)9781665406239

A biopsy is the only diagnostic procedure for accurate histological confirmation of breast cancer. When sonographic placement is not feasible, a Magnetic Resonance Imaging(MRI)-guided biopsy is often preferred. The lack of real-time imaging information and the deformations of the breast make it challenging to bring the needle precisely towards the tumour detected in pre-interventional Magnetic Resonance (MR) images. The current manual MRI-guided biopsy workflow is inaccurate and would benefit from a technique that allows real-time tracking and localisation of the tumour lesion during needle insertion. This paper proposes a robotic setup and software architecture to assist the radiologist in targeting MR-detected suspicious tumours. The approach benefits from image fusion of preoperative images with intraoperative optical tracking of markers attached to the patient’s skin. A hand-mounted biopsy device has been constructed with an actuated needle base to drive the tip toward the desired direction. The steering commands may be provided both by user input and by computer guidance. The workflow is validated through phantom experiments. On average, the suspicious breast lesion is targeted with a radius down to 2.3 mm. The results suggest that robotic systems taking into account breast deformations have the potentials to tackle this clinical challenge.

关键词： Target tracking Biopsy Phantoms Breast Needles Real-time systems Skin

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Clustering with dynamic bipartite graph learning

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Neurocomputing 2025年 648卷

作者： Yun Liang Wang Gao Qimin Liang Cankun Zhong Feiping Nie South China Agricultural University College of Mathematics and Informatics Guangzhou 510642 Guangdong China Northwestern Polytechnical University School of Artificial Intelligence Optics and Electronics (iOPEN) and the Key Laboratory of Intelligent Interaction and Applications (Ministry of Industry and Information Technology) Xi’an 710072 Shaanxi China Northwestern Polytechnical University School of Computer Science Xi’an 710072 Shaanxi China

Anchor-based bipartite graph clustering algorithms greatly enhance data analysis by accelerating computations without sacrificing performance. However, these methods face two limitations: reliance on fixed bipartite graphs and lack of interaction between the bipartite graph and anchor labels, which reduces adaptability to dynamic data and clustering performance. To overcome these, a novel method, called Clustering with Dynamic Bipartite Graph Learning (DBGL) is proposed. The DBGL objective function is composed of two parts: construct the bipartite graph and learn the anchor labels. The bipartite graph and anchor labels are updated iteratively, resulting in a dynamic bipartite graph that evolves throughout the clustering process. Our dynamic technique is more flexible and performs better on large, complicated datasets than fixed bipartite graph methods. Additionally, we introduce label transmission into DBGL, enabling autonomous interaction between bipartite graph and anchor labels, which mutually guide each other toward optimal clustering results. We design an alternating optimization algorithm where the bipartite graph is updated using an Iteratively Re-Weighted (IRW) algorithm, while the anchor labels are optimized through an improved Coordinate Descent (CD) algorithm. Comprehensive experiments on benchmark datasets demonstrate that DBGL outperforms leading methods in clustering accuracy, efficiency, and robustness, with theoretical analysis confirming its convergence and stability.

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Global guidance network for breast lesion segmentation in ultrasound images

arXiv

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arXiv 2021年

作者： Xue, Cheng Zhu, Lei Fu, Huazhu Hu, Xiaowei Li, Xiaomeng Zhang, Hai Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Department of Applied Mathematics and Theoretical Physics University of Cambridge Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Shenzhen People's Hospital Second Clinical College of Jinan University First Affiliated Hospital of Southern University of Science and Technology Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Automatic breast lesion segmentation in ultrasound helps to diagnose breast cancer, which is one of the dreadful diseases that affect women globally. Segmenting breast regions accurately from ultrasound image is a challenging task due to the inherent speckle artifacts, blurry breast lesion boundaries, and inhomogeneous intensity distributions inside the breast lesion regions. Recently, convolutional neural networks (CNNs) have demonstrated remarkable results in medical image segmentation tasks. However, the convolutional operations in a CNN often focus on local regions, which suffer from limited capabilities in capturing long-range dependencies of the input ultrasound image, resulting in degraded breast lesion segmentation accuracy. In this paper, we develop a deep convolutional neural network equipped with a global guidance block (GGB) and breast lesion boundary detection (BD) modules for boosting the breast ultrasound lesion segmentation. The GGB utilizes the multi-layer integrated feature map as a guidance information to learn the long-range non-local dependencies from both spatial and channel domains. The BD modules learn additional breast lesion boundary map to enhance the boundary quality of a segmentation result refinement. Experimental results on a public dataset and a collected dataset show that our network outperforms other medical image segmentation methods and the recent semantic segmentation methods on breast ultrasound lesion segmentation. Moreover, we also show the application of our network on the ultrasound prostate segmentation, in which our method better identifies prostate regions than state-of-the-art networks. © 2021, CC BY.

关键词： Deep neural networks

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Wisdom of the institutional crowd

arXiv

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arXiv 2017年

作者： Primicerio, Kevin Challet, Damien Gualdi, Stanislao Laboratory of Mathematics in Interaction with Computer Science CentraleSupélec Grande Voie des Vignes Châtenay-Malabry92290 France Capital Fund Management 23 rue de l’Université Paris75007 France

The average portfolio structure of institutional investors is shown to have properties which account for transaction costs in an optimal way. This implies that financial institutions unknowingly display collective rationality, or Wisdom of the Crowd. Individual deviations from the rational benchmark are ample, which illustrates that system-wide rationality does not need nearly rational individuals. Finally we discuss the importance of accounting for constraints when assessing the presence of Wisdom of the Crowd. Copyright © 2017, The Authors. All rights reserved.

关键词： Investments

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Reports of the AAAI 2009 spring symposia

Reports of the AAAI 2009 spring symposia

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作者： Bao, Jie Bojars, Uldis Choudhury, Tanzeem Ding, Li Greaves, Mark Kapoor, Ashish Louchart, Sandy Mehta, Manish Nebel, Bernhard Nirenburg, Sergei Oates, Tim Roberts, David L. Sanfilippo, Antonio Stojanovic, Nenad Stubbs, Kristen Thomaz, Andrea L. Tsui, Katherine Woelfl, Stefan Tetherless World Constellation Rensselaer Polytechnic Institute United States Social Software Unit of DERI Galway Ireland Computer Science Department Dartmouth College United States Knowledge Systems at Vulcan Inc United States Adaptive Systems and Interaction Group Microsoft Research Redmond United States School for Mathematics and Computer Sciences Heriot-Watt University United Kingdom School ot Interactive Computing Georgia Institute of Technology United States Department of Computer Science University of Freiburg Germany Computer Science and Electrical Engineering Department University of Maryland Baltimore County United States Pacific Northwest National Laboratory Richland WA United States University of Karlsruhe Germany IRobot Corporation United States University of Massachusetts Lowell United States

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of computer science, was pleased to present the 2009 Spring Symposium Series, held Monday through Wednesday, March 23-25, 2009, at Stanford University. The titles of the nine symposia were Agents That Learn from Human Teachers, Benchmarking of Qualitative Spatial and Temporal Reasoning Systems, Experimental Design for Real- World Systems, Human Behavior Modeling, Intelligent Event Processing, Intelligent Narrative Technologies II, Learning by Reading and Learning to Read, Social Semantic Web: Where Web 2.0 Meets Web 3.0, and Technosocial Predictive Analytics. The goal of the Agents That Learn from Human Teachers symposium was to investigate how we can enable software and robotics agents to leam from real-time interaction with an everyday human partner. The aim of the Benchmarking of Qualitative Spatial and Temporal Reasoning Systems symposium was to initiate the development of a problem repository in the field of qualitative spatial and temporal reasoning and identify a graded set of challenges for future midterm and long-term research. The Experimental Design symposium discussed the challenges of evaluating AI systems. The Human Behavior Modeling symposium explored reasoning methods for understanding various aspects of human behavior, especially in the context of designing intelligent systems that interact with humans. The Intelligent Event Processing symposium discussed the need for more Al-based approaches in event pro-cessing and defined a kind of research agenda for the field, coined as intelligent complex event processing (iCEP). The Intelligent Narrative Technologies IIAAAI symposium discussed innovations, progress, and novel techniques in the research domain. The Learning by Reading and Learning to Read symposium explored two aspects of making natural language texts semantically accessible to, and processable by, machines. The Social Semantic

关键词： Benchmarking

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