检索结果-内蒙古大学图书馆

Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free Approach

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Karimijafarbigloo, Sanaz Azad, Reza Merhof, Dorit Institute of Image Analysis and Computer Vision Faculty of Informatics and Data Science University of Regensburg Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Few-shot semantic segmentation (FSS) offers immense potential in the field of medical image analysis, enabling accurate object segmentation with limited training data. However, existing FSS techniques heavily rely on annotated semantic classes, rendering them unsuitable for medical images due to the scarcity of annotations. To address this challenge, multiple contributions are proposed: First, inspired by spectral decomposition methods, the problem of image decomposition is reframed as a graph partitioning task. The eigenvectors of the Laplacian matrix, derived from the feature affinity matrix of self-supervised networks, are analyzed to estimate the distribution of the objects of interest from the support images. Secondly, we propose a novel self-supervised FSS framework that does not rely on any annotation. Instead, it adaptively estimates the query mask by leveraging the eigenvectors obtained from the support images. This approach eliminates the need for manual annotation, making it particularly suitable for medical images with limited annotated data. Thirdly, to further enhance the decoding of the query image based on the information provided by the support image, we introduce a multi-scale large kernel attention module. By selectively emphasizing relevant features and details, this module improves the segmentation process and contributes to better object delineation. Evaluations on both natural and medical image datasets demonstrate the efficiency and effectiveness of our method. Moreover, the proposed approach is characterized by its generality and model-agnostic nature, allowing for seamless integration with various deep architectures. The code is publicly available at GitHub. © 2023, CC BY.

关键词： Eigenvalues and eigenfunctions

Simulation-based Bayesian inference under model misspecification

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Kelly, Ryan P. Warne, David J. Frazier, David T. Nott, David J. Gutmann, Michael U. Drovandi, Christopher School of Mathematical Sciences Queensland University of Technology Australia Centre for Data Science Queensland University of Technology Australia Department of Econometrics and Business Statistics Monash University Australia Department of Statistics and Data Science National University of Singapore Singapore School of Informatics Institute for Adaptive & Neural Computation University of Edinburgh United Kingdom Centre of Excellence for the Mathematical Analysis of Cellular Systems Queensland University of Technology Australia

Simulation-based Bayesian inference (SBI) methods are widely used for parameter estimation in complex models where evaluating the likelihood is challenging but generating simulations is relatively straightforward. However, these methods commonly assume that the simulation model accurately reflects the true data-generating process, an assumption that is frequently violated in realistic scenarios. In this paper, we focus on the challenges faced by SBI methods under model misspecification. We consolidate recent research aimed at mitigating the effects of misspecification, highlighting three key strategies: i) robust summary statistics, ii) generalised Bayesian inference, and iii) error modelling and adjustment parameters. To illustrate both the vulnerabilities of popular SBI methods and the effectiveness of misspecification-robust alternatives, we present empirical results on an illustrative example. © 2025, CC BY.

关键词： Neural network models

European standardization efforts from FAIR toward explainable-AI-ready data documentation in materials modelling

学校读者我要写书评

暂无评论

European standardization efforts from FAIR toward explainabl...

Applied Artificial Intelligence (ICAPAI), International Conference on

作者： Martin Thomas Horsch Björn Schembera Heinz A. Preisig Department of Data Science Material Theory and Informatics Norwegian University of Life Sciences Ås Norway Institute of Applied Analysis and Numerical Simulation University of Stuttgart Stuttgart Germany Department of Chemical Engineering Norwegian University of Science and Technology Trondheim Norway

Security critical AI applications require a standardized and interoperable data and metadata documentation that makes the source data explainable-AI ready (XAIR). Within the domain of materials modelling and characterization, European initiatives have proposed a series of metadata standards and procedural recommendations that were accepted as CEN workshop agreements (CWAs): CWA 17284 MODA, CWA 17815 CHADA, and CWA 17960 ModGra. It is discussed how these standards have been ontologized, and gaps are identified as regards the epistemic grounding metadata, i.e., an annotation of data and claims by something that substantiates whether, why, and to what extent they are indeed knowledge and can be relied upon.

关键词：

A Comprehensive Guideline to Design Interpretable Hierarchical Fuzzy Systems

学校读者我要写书评

暂无评论

A Comprehensive Guideline to Design Interpretable Hierarchic...

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Tajul Rosli Razak Jonathan M. Garibaldi Christian Wagner School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Malaysia Laboratory for Uncertainty in Data and Decision Making (LUCID) School of Computer Science University of Nottingham United Kingdom Intelligent Modelling and Analysis Research Group School of Computer Science University of Nottingham Ningbo China

ISBN: (数字)9798350319545

ISBN: (纸本)9798350319552

Hierarchical fuzzy systems (HFSs) are claimed to be an excellent approach to reducing the number of rules in Fuzzy logic systems (FLSs). Further, HFSs have also been shown to have the potential to reduce complexity and improve interpretability for FLSs. However, designing an interpretable HFS is a challenging task. This is due to the HFSs' structures, which have multiple subsystems, layers and topologies. This paper put forward an approach to present a design guidelines framework to build interpretable HFSs. The framework consists of five key guidelines for interpretable HFSs. It is demonstrated using a real-world example to design interpretable HFS for the Iris classification problem. This study contributes to providing insight into a pathway for designing interpretable HFSs, as can be used in practice.

关键词： Fuzzy logic Iris Design methodology Semantics Hafnium Complexity theory Topology

Individual tree detection and counting based on high-resolution imagery and the canopy height model data

学校读者我要写书评

暂无评论

地球空间信息科学学报（英文版） 2024年第6期27卷中插23,2162-2178页

作者： Ye Zhang Moyang Wang Joseph Mango Liang Xin Chen Meng Xiang Li Key Laboratory of Geographic Information Science(Ministry of Education)and School of Geographic Sciences East China Normal UniversityShanghaiChina Department of Transportation and Geotechnical Engineering University of Dar es SalaamDar es salaamTanzania College of Surveying and Geographic Informatics Tongji UniversityShanghaiChina Cadre School of Shanghai Municipal Bureau of Planning and Natural Resources ShanghaiChina School of Ecology and Environmental Sciences East China Normal UniversityShanghaiChina Key Laboratory of Geographic Information Science(Ministry of Education)and School of Geographic Sciences East China Normal UniversityShanghaiChina Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration East China Normal UniversityShanghaiChina Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities(Ministry of Natural Resources) East China Normal UniversityShanghaiChina

Individual Tree Detection-and-Counting(ITDC)is among the important tasks in town areas,and numerous methods are proposed in this *** their many advantages,still,the proposed methods are inadequate to provide robust results because they mostly rely on the direct field *** paper presents a novel approach involving high-resolution imagery and the Canopy-Height-Model(CHM)data to solve the ITDC *** new approach is studied in six urban scenes:farmland,woodland,park,industrial land,road and residential ***,it identifies tree canopy regions using a deep learning network from high-resolution *** then deploys the CHM-data to detect treetops of the canopy regions using a local maximum algorithm and individual tree canopies using the region ***,it calculates and describes the number of individual trees and tree *** proposed approach is experimented with the data from Shanghai,*** results show that the individual tree detection method had an average overall accuracy of 0.953,with a precision of 0.987 for woodland ***,the R2 value for canopy segmentation in different urban scenes is greater than 0.780 and 0.779 for canopy area and diameter size,*** results confirm that the proposed method is robust enough for urban tree planning and management.

关键词： Individual tree detection-and-counting(ITDC) deep learning high-resolution imagery Canopy Height Model data(CHM)

A Variational Hierarchical Model for Neural Cross-Lingual Summarization

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Liang, Yunlong Meng, Fandong Zhou, Chulun Xu, Jinan Chen, Yufeng Su, Jinsong Zhou, Jie Beijing Key Lab Of Traffic Data Analysis And Mining Beijing Jiaotong University China Pattern Recognition Center WeChatAI Tencent Inc China School Of Informatics Xiamen University China

The goal of the cross-lingual summarization (CLS) is to convert a document in one language (e.g., English) to a summary in another one (e.g., Chinese). Essentially, the CLS task is the combination of machine translation (MT) and monolingual summarization (MS), and thus there exists the hierarchical relationship between MT&MS and CLS. Existing studies on CLS mainly focus on utilizing pipeline methods or jointly training an end-to-end model through an auxiliary MT or MS objective. However, it is very challenging for the model to directly conduct CLS as it requires both the abilities to translate and summarize. To address this issue, we propose a hierarchical model for the CLS task, based on the conditional variational auto-encoder. The hierarchical model contains two kinds of latent variables at the local and global levels, respectively. At the local level, there are two latent variables, one for translation and the other for summarization. As for the global level, there is another latent variable for cross-lingual summarization conditioned on the two local-level variables. Experiments on two language directions (English⇔Chinese) verify the effectiveness and superiority of the proposed approach. In addition, we show that our model is able to generate better cross-lingual summaries than comparison models in the few-shot setting. Copyright © 2022, The Authors. All rights reserved.

关键词： Hierarchical systems

Nuclear power plant probabilistic safety assessment by fuzzy approach: current states, future research directions and challenges

学校读者我要写书评

暂无评论

International Journal of Nuclear Energy Science and Technology 2023年第4期16卷 297-315页

作者： Purba, Julwan Hendry Hendayun, Mokhamad Tjahyani, Damianus Toersiwi Sony Susila, I. Putu Wahanani, Nursinta Adi Waskita, Arya Adhyaksa Ekariansyah, Andi Sofrany Research Center for Nuclear Reactor Technology Research Organization for Nuclear Energy National Research and Innovation Agency Kawasan Sains dan Teknologi BJ Habibie South Tangerang Banten15310 Indonesia Postgraduate Department Langlangbuana University Jl. Karapitan No. 116 Bandung West Java40261 Indonesia Research Center for Radiation Detection and Nuclear Analysis Technology Research Organization for Nuclear Energy National Research and Innovation Agency Kawasan Sains dan Teknologi BJ Habibie South Tangerang Banten15310 Indonesia Center for Data and Information National Research and Innovation Agency Kawasan Sains dan Teknologi BJ Habibie South Tangerang Banten15310 Indonesia Research Center for Data and Information Sciences Research Organization for Electronics and Informatics National Research and Innovation Agency Kawasan Sains dan Teknologi Samaun Samadikun West Java Bandung40135 Indonesia

Probabilistic safety assessment (PSA) has been widely implemented in nuclear power plant (NPP) safety analysis. In PSA, all events are always assumed to have their own historical failure data. This becomes the technical challenge in PSA. To deal with this limitation, fuzzy PSA has been proposed. Fuzzy concept is introduced to model system reliability when quantitative historical failure data is limited or unavailable. This study reviews the implementation of fuzzy fault tree analysis and fuzzy event tree analysis in NPP safety assessment. To stimulate further research directions, the limitations of existing fuzzy PSA and challenges for future studies are also discussed. The study confirms that fuzzy PSA offers some features to overcome the constraints of conventional PSA. Nevertheless, fuzzy PSA still has some challenges to be dealt with. To improve the capabilities of fuzzy PSA in the future, those limitations and challenges still need to be practically and scientifically overcome. © 2023 Inderscience Enterprises Ltd.

关键词： Nuclear power plants

ISMI-VAE: A deep learning model for classifying disease cells using gene expression and SNV data

学校读者我要写书评

暂无评论

Computers in Biology and Medicine 2024年 175卷 108485-108485页

作者： Li, Han Zhou, Yitao Zhao, Ningyuan Wang, Ying Lai, Yongxuan Zeng, Feng Yang, Fan Department of Automation Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen361005 China Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Making Xiamen university Xiamen361000 China School of Informatics Xiamen University Xiamen China State Key Laboratory of Cellular Stress Biology School of Life Sciences Xiamen University China Research Unit of Cellular Stress of CAMS Cancer Research Center School of Medicine Xiamen University China

Various studies have linked several diseases, including cancer and COVID-19, to single nucleotide variations (SNV). Although single-cell RNA sequencing (scRNA-seq) technology can provide SNV and gene expression data, few studies have integrated and analyzed these multimodal data. To address this issue, we introduce Interpretable Single-cell Multimodal data Integration Based on Variational Autoencoder (ISMI-VAE). ISMI-VAE leverages latent variable models that utilize the characteristics of SNV and gene expression data to overcome high noise levels and uses deep learning techniques to integrate multimodal information, map them to a low-dimensional space, and classify disease cells. Moreover, ISMI-VAE introduces an attention mechanism to reflect feature importance and analyze genetic features that could potentially cause disease. Experimental results on three cancer data sets and one COVID-19 data set demonstrate that ISMI-VAE surpasses the baseline method in terms of both effectiveness and interpretability and can effectively identify disease-causing gene features. © 2024 Elsevier Ltd

关键词： Classification (of information)

Cellular automata can really solve the parity problem

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Wolnik, Barbara Nenca, Anna Balbi, Pedro Paulo De Baets, Bernard Institute of Mathematics Faculty of Mathematics Physics and Informatics University of Gdańsk Gdańsk80-308 Poland Institute of Informatics Faculty of Mathematics Physics and Informatics University of Gdańsk Gdańsk80-308 Poland Faculdade de Computação e Informática Universidade Presbiteriana Mackenzie Rua da Consolação 896 Consolação SP São Paulo01302-907 Brazil KERMIT Department of Data Analysis and Mathematical Modelling Faculty of Bioscience Engineering Ghent University Coupure links 653 GentB-9000 Belgium

Determining properties of an arbitrary binary sequence is a challenging task if only local processing is allowed. Among these properties, the determination of the parity of 1s by distributed consensus has been a recurring endeavour in the context of automata networks. In its most standard formulation, a one-dimensional cellular automaton rule should process any odd-sized cyclic configuration and lead the lattice to converge to the homogeneous fixed point of 0s if the parity of 1s is even and to the homogeneous fixed point of 1s, otherwise. The only known solution to this problem with a single rule was given by Betel, de Oliveira and Flocchini (coined BFO rule after the authors’ initials). However, three years later the authors of the BFO rule realised that the rule would fail for some specific configuration and proposed a computationally sound fix, but a proof could not be worked out. Here we provide another fix to the BFO rule along with a full proof, therefore reassuring that a single-rule solution to the problem really does exist. © 2025, CC BY.

关键词： Binary sequences