Relevant bibliographies by topics / Representative outliers

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Representative outliers'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Representative outliers"

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Chen, Wo-Ruo, Yong-Huan Yun, Ming Wen, Hong-Mei Lu, Zhi-Min Zhang, and Yi-Zeng Liang. "Representative subset selection and outlier detection via isolation forest." Analytical Methods 8, no. 39 (2016): 7225–31. http://dx.doi.org/10.1039/c6ay01574c.

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Muhima, Rani Rotul, Muchamad Kurniawan, and Oktavian Tegar Pambudi. "A LOF K-Means Clustering on Hotspot Data." International Journal of Artificial Intelligence & Robotics (IJAIR) 2, no. 1 (2020): 29. http://dx.doi.org/10.25139/ijair.v2i1.2634.

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K-Means is the most popular of clustering method, but its drawback is sensitivity to outliers. This paper discusses the addition of the outlier removal method to the K-Means method to improve the performance of clustering. The outlier removal method was added to the Local Outlier Factor (LOF). LOF is the representative outlier’s detection algorithm based on density. In this research, the method is called LOF K-Means. The first applying clustering by using the K-Means method on hotspot data and then finding outliers using the LOF method. The object detected outliers are then removed. Then new c
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Wang, Bo, Chen Sun, Keming Zhang, and Jubing Chen. "Outlier removal method for the refinement of optically measured displacement field based on critical factor least squares and subdomain division." Measurement Science and Technology 33, no. 5 (2022): 055020. http://dx.doi.org/10.1088/1361-6501/ac476c.

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Abstract As a representative type of outlier, abnormal data in displacement measurement has often inevitably occurred in full-field optical metrology and significantly affected further evaluation, especially when calculating the strain field by differencing the displacement. In this study, an outlier removal method is proposed which can recognize and remove the abnormal data in an optically measured displacement field. An iterative critical factor least squares (CFLS) algorithm is developed which distinguishes the distance between the data points and the least squares plane to identify the out
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Guo, Wei. "Robust adaptive online sequential extreme learning machine for predicting nonstationary data streams with outliers." Journal of Algorithms & Computational Technology 13 (January 2019): 174830261989542. http://dx.doi.org/10.1177/1748302619895421.

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Data streams online modeling and prediction is an important research direction in the field of data mining. In practical applications, data streams are often of nonstationary nature and containing outliers, hence an online learning algorithm with dynamic tracking capability as well as anti-outlier capability is urgently needed. With this in mind, this paper proposes a novel robust adaptive online sequential extreme learning machine (RA-OSELM) algorithm for the online modeling and prediction of nonstationary data streams with outliers. The RA-OSELM is developed from the famous online sequential
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Ruzgienė, Birutė, and Wolfgang Förstner. "RANSAC FOR OUTLIER DETECTION." Geodesy and cartography 31, no. 3 (2012): 83–87. http://dx.doi.org/10.3846/13921541.2005.9636670.

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Up-to-date digital photogrammetry involves operations on huge data sets, and with classical image processing procedures it might be time consuming to find out the best solution. One of the key tasks is to detect outliers in given data, eg for curve fitting or image matching. The problem is hard as the number of outliers is usually large, possibly larger than 50%, thus powerful estimation techniques are needed. We demonstrate one of these techniques, namely Random Sample Consensus (RANSAC), for fitting a model to sample data, especially for fitting a straight line through a set of given points.
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Nouma, Badreddine Ben, Amar Mitiche, Youssef Ouakrim, and Neila Mezghani. "Knee kinematic curve representation and application to knee pathology classification." Journal of Biomedical Engineering and Informatics 4, no. 1 (2018): 32. http://dx.doi.org/10.5430/jbei.v4n1p32.

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This study investigates a variational method to determine the most representative shape of a set of knee kinematic curves with application to knee pathology classification. Although they provide essential information for pathology classification, knee kinematic curves are characterized by high intra-class variability and outliers are often present. As a result, a set of several measurement curves are acquired of any single individual which are then averaged before their use for pathology classification. Rather than using the average of an individual’s recorded measurement curves, this method d
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Sangeux, Morgan, and Julia Polak. "A simple method to choose the most representative stride and detect outliers." Gait & Posture 41, no. 2 (2015): 726–30. http://dx.doi.org/10.1016/j.gaitpost.2014.12.004.

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Guo, Wei, Jianjiang Yu, Caigen Zhou, Xiaofeng Yuan, and Zhanxiu Wang. "RCBLS: An Outlier-Robust Broad Learning Framework with Compact Structure." Electronics 12, no. 14 (2023): 3118. http://dx.doi.org/10.3390/electronics12143118.

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Recently, the broad learning system (BLS) has been widely developed in virtue of its excellent performance and high-computational efficiency. However, two deficiencies still exist in BLS and preclude its deployment in real applications. First, the standard BLS performs poorly in outlier environments because the least squares loss function it uses to train the network is sensitive to outliers. Second, the model structure of BLS is likely to be redundant since the hidden nodes in it are randomly generated. To address the above two issues, a new robust and compact BLS (RCBLS), based on M-estimato
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Siraj, Amir, and Abraham Loeb. "Interstellar Meteors Are Outliers in Material Strength." Astrophysical Journal Letters 941, no. 2 (2022): L28. http://dx.doi.org/10.3847/2041-8213/aca8a0.

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Abstract The first interstellar meteor larger than dust was detected by US government sensors in 2014, identified as an interstellar object candidate in 2019, and confirmed by the Department of Defense in 2022. Here, we describe an additional interstellar object candidate in the CNEOS fireball catalog and compare the implied material strength of the two objects, referred to here as IM1 and IM2, respectively. IM1 and IM2 are ranked first and third in terms of material strength out of all 273 fireballs in the CNEOS catalog. Fitting a log-normal distribution to material strengths of objects in th
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Cui, Hai-Feng, Zi-Hong Ye, Lu Xu, Xian-Shu Fu, Cui-Wen Fan, and Xiao-Ping Yu. "Automatic and Rapid Discrimination of Cotton Genotypes by Near Infrared Spectroscopy and Chemometrics." Journal of Analytical Methods in Chemistry 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/793468.

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This paper reports the application of near infrared (NIR) spectroscopy and pattern recognition methods to rapid and automatic discrimination of the genotypes (parent, transgenic, and parent-transgenic hybrid) of cotton plants. Diffuse reflectance NIR spectra of representative cotton seeds (n=120) and leaves (n=123) were measured in the range of 4000–12000 cm−1. A practical problem when developing classification models is the degradation and even breakdown of models caused by outliers. Considering the high-dimensional nature and uncertainty of potential spectral outliers, robust principal compo
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Dissertations / Theses on the topic "Representative outliers"

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Galvenius, Hugo. "Identifikation av icke-representativa svar i frågeundersökningar genom detektion av multivariata avvikare." Thesis, Uppsala universitet, Matematiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-219546.

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To United Minds, large-scale surveys are an important offering to clients, not least the public opinion poll Väljarbarometern. A risk associated with surveys is satisficing – sub-optimal response behaviour impairing the possibility of correctly describing the sampled population through its results. The purpose of this study is to – through the use of multivariate outlier detection methods - identify those observations assumed to be non-representative of the population. The possibility of categorizing responses generated through satisficing as outliers is investigated. With regards to the char
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Shinde, Krushna. "Interval uncertainty method to treat inconsistent measurements in inverse problems." Thesis, Compiègne, 2021. http://www.theses.fr/2021COMP2594.

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Le problème inverse consiste à trouver les valeurs des paramètres d’un modèle physique à partir d’un ensemble de mesures. Dans les problèmes de génie mécanique, la caractérisation du comportement des matériaux nécessite une méthode inverse pour identifier les paramètres des matériaux. Le problème d’identification déterministe est généralement sensible aux données, et une façon de résoudre ce problème est de prendre en compte les incertitudes dans les données. Bien que plusieurs de ces méthodes existent dans la littérature, la plupart d’entre elles utilisent la minimisation des moindres carrés
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Bandyopadhyay, Sambaran. "Representing Networks: Centrality, Node Embeddings, Community Outliers and Graph Representation." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4646.

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Networks are ubiquitous. We start our technical work in this thesis by exploring the classical concept of node centrality (also known as influence measure) in information networks. Like clustering, node centrality is also an ill-posed problem. There exist several heuristics and algorithms to compute the centrality of a node in a graph, but there is no formal definition of centrality available in the network science literature. Lately, researchers have proposed axiomatic frameworks for the centrality of a node in a network. However, these existing formal frameworks are not generic in nature in
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Books on the topic "Representative outliers"

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Lukʹi︠a︡nov, Anatoliĭ Ivanovich. Parlamentarizm v Rossii: Voprosy istorii, teorii i praktiki, kurs lekt︠s︡iĭ. Norma, INFRA-M, 2010.

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Office, General Accounting. Resource management, information on the coastal zone management program: Fact sheet for the chairman, Committee on Merchant Marine and Fisheries, House of Representatives. GAO, 1986.

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Office, General Accounting. Health insurance regulation: Wide variation in states' authority, oversight, and resources : report to the Chairman, Subcommittee on Health, Committee on Ways and Means, House of Representatives. The Office, 1993.

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Rapohin, Nikolay. Applied psychology. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1070336.

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The textbook outlines the main provisions of modern applied scientific psychology. The socio-psychological and ethnopsychological features of behavior are analyzed. The author's interpretation of the psychological structure of personality is given, applied methods of its study are described. The issues of communication and psychological impact on the personality are considered. Using practical examples, the characteristics of styles and specific types of business communication are revealed. The features of establishing and maintaining business contacts with representatives of various national
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Office, General Accounting. Health insurance regulation: Variation in recent state small employer health insurance reforms : fact sheet for the chairman, Subcommittee on Employer-Employee Relations, Committee on Economic and Educational Opportunities, House of Representatives. The Office, 1995.

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Shakespeare, William. Hamlet...: Arranged for representation at the Royal Princess's Theatre. Pergamon, 1985.

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Serrano, M. R., and Edlira Xhafa. Industrial relations in micro and small enterprises. ILO, 2024. http://dx.doi.org/10.54394/xjhf2363.

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This working paper offers a global review of industrial relations within micro and small enterprises (MSEs), focusing on the collective representation of workers and employers, collective bargaining, and legal frameworks. MSEs and the self-employed account for 70 per cent of global employment, largely in the informal economy, and face challenges such as the limited reach of labour law and weak industrial relations mechanisms. The paper highlights the scarcity of research on industrial relations in MSEs and makes recommendations for further research in this area. It outlines policy recommendati
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Anikin, Valeriy, and Boris Poyzner. To the dissertation dissertation: the semantic aspect. INFRA-M Academic Publishing LLC., 2024. http://dx.doi.org/10.12737/1909143.

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The textbook outlines the methodology for preparing and submitting a dissertation for defense. The specifics of this qualification text, the norms of the formulation of the protected provisions are described, the meaning of expert assessments by the dissertation council, the leading organization, and opponents is indicated. The peculiarities of professional perception of the results of the dissertation work, the need for their level to comply with regulatory documents are indicated. The mission of the dissertation supervisor is revealed. The historical outline of the development of the dissert
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Shrayberg, Yakov. Current trends in the development of digitalization of society: scientific, educational, library and information environment. INFRA-M Academic Publishing LLC., 2025. https://doi.org/10.12737/2155873.

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The monograph outlines the main components and trends in the development of digitalization of society, primarily such as blockchain, artificial intelligence, cloud computing, robotics, the Internet of Things, digital twins, mobile communications, virtual and augmented reality, and a number of others. The relevant changes and transformations in the library and information environment are considered, in particular, the transformation of the information market, mainly in terms of changing legislation on copyright and intellectual property protection, as well as the state of print and electronic b
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Shakespeare, William. Shakespeare's tragedy of Macbeth: With Locke's music, arranged for representation at the Princess's Theatre. Pergamon Press, 1985.

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Book chapters on the topic "Representative outliers"

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Ding, Zhengming, Handong Zhao, and Yun Fu. "Multi-view Outlier Detection." In Learning Representation for Multi-View Data Analysis. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00734-8_4.

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Trimeche, Mejdi, and Jukka Yrjänäinen. "A Method for Simultaneous Outlier Rejection in Image Super-Resolution." In Visual Content Processing and Representation. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39798-4_25.

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Tola, Engin, and A. Aydın Alatan. "Fast Outlier Rejection by Using Parallax-Based Rigidity Constraint for Epipolar Geometry Estimation." In Multimedia Content Representation, Classification and Security. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11848035_76.

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Forsyth, David, Andrew Zisserman, and Jitendra Malik. "Distinctive representations for the recognition of curved surfaces using outlines and markings." In Object Representation in Computer Vision. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60477-4_6.

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Matsypaev, Dmitry A., and Andrey G. Bronevich. "The Fuzzy Representation of Prior Information for Separating Outliers in Statistical Experiments." In Strengthening Links Between Data Analysis and Soft Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-10765-3_20.

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Alqaryouti, Omar, Nur Siyam, and Khaled Shaalan. "Outlier Detection for Customs Post Clearance Audit Using Convex Space Representation." In Recent Innovations in Artificial Intelligence and Smart Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14748-7_19.

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Kermarrec, Gaël, Vibeke Skytt, and Tor Dokken. "LR B-Splines for Representation of Terrain and Seabed: Data Fusion, Outliers, and Voids." In Optimal Surface Fitting of Point Clouds Using Local Refinement. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16954-0_5.

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AbstractPerforming surface approximation of geospatial point clouds with locally refined (LR) B-splines comes with several challenges: (i) Point clouds have varying data density, (ii) outliers should be eliminated without deleting features, (iii) voids, also called holes, or data gaps should be treated specifically to avoid the drop of the approximated surface in domains without points. These factors tend to be even more challenging when point clouds acquired from different sensors having different noise characteristics are fused together. The data set becomes non-uniform and the fusing proces
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Shimauchi, Hirokazu. "Quasiconformal Extension-Based Unsupervised Representation Learning and Application to Semi-supervised Outlier Detection." In Studies in Computational Intelligence. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85252-7_11.

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Duraj, Agnieszka, and Piotr S. Szczepaniak. "Linguistic Summaries Using Interval-Valued Fuzzy Representation of Imprecise Information - An Innovative Tool for Detecting Outliers." In Computational Science – ICCS 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77980-1_38.

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Huang, Xiaocong, Guoheng Huang, Guo Zhong, et al. "ROSAL: Semi-supervised Active Learning with Representation Aggregation and Outlier for Endoscopy Image Classification." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-6606-5_24.

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Conference papers on the topic "Representative outliers"

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Freitas, Geovani R., Sara M. Badenes, Rui Oliveira, and Fernando G. Martins. "Machine Learning Models for Predicting the Amount of Nutrients Required in a Microalgae Cultivation System." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.105325.

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Effective prediction of nutrient demands is crucial for optimising microalgae growth, maximising productivity and minimising the waste of resources. With the increasing amount of data related to microalgae cultivation systems, data mining and machine learning models to extract additional knowledge have gained popularity. In the development of such models, a data preprocessing stage is necessary due to the poor data quality. At this stage, cleaning and outlier removal techniques are employed to eliminate missing data and outliers, respectively. Afterwards, data splitting and cross-validation st
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Zapata-Boada, Santiago, Francisco Heredia, and Klever Mera. "Predicting Corrosion Rate in Oil Wells Using First Principles and Machine Learning." In LatinCORR 2023. AMPP, 2023. https://doi.org/10.5006/lac23-20530.

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Corrosion in oil wells results from the complex interaction between various factors such as chemical composition, temperature, pressure, and flow rates of the fluids involved, and poses a significant challenge to the oil and gas industry. Accurate prediction of corrosion rates is essential for operational efficiency, proactive maintenance, and risk mitigation. Traditional methods for corrosion rate prediction rely on empirical models, which are often reactive and inaccurate. Machine learning models can provide a more accurate prediction of corrosion rate1, 2. The objective of this study is to
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Ginting, Dewi Sartika Br, Syahril Efendi, Amalia, and Poltak Sihombing. "Cure-Sne Model Detects Outliers in Data to Optimize the Process of Clustering Using Representatives." In 2024 International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA). IEEE, 2024. https://doi.org/10.1109/icicyta64807.2024.10913289.

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Zhang, Xingxing, Zhenfeng Zhu, Yao Zhao, and Deqiang Kong. "Self-Supervised Deep Low-Rank Assignment Model for Prototype Selection." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/436.

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Prototype selection is a promising technique for removing redundancy and irrelevance from large-scale data. Here, we consider it as a task assignment problem, which refers to assigning each element of a source set to one representative, i.e., prototype. However, due to the outliers and uncertain distribution on source, the selected prototypes are generally less representative and interesting. To alleviate this issue, we develop in this paper a Self-supervised Deep Low-rank Assignment model (SDLA). By dynamically integrating a low-rank assignment model with deep representation learning, our mod
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Genovese, Federica, Giuseppe Muscolino, and Alba Sofi. "Bounds of Reliability Function for Structural Systems Subjected to Imprecise Seismic Actions." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-73231.

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Abstract The present paper focuses on reliability analysis of structural systems under ground motion acceleration taking into account the inherent random nature of the excitation as well as epistemic uncertainties affecting the definition of its spectrum. Specifically, seismic excitation is modelled as a zero-mean stationary Gaussian random process fully characterized by an imprecise power spectral density (PSD) function i.e. with interval parameters. The ranges of such interval parameters are determined by analysing a large set of accelerograms recorded on rigid soil deposits. To discard outl
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Hou, Mingliang, Jing Ren, Falih Febrinanto, Ahsan Shehzad, and Feng Xia. "Cross Network Representation Matching with Outliers." In 2021 International Conference on Data Mining Workshops (ICDMW). IEEE, 2021. http://dx.doi.org/10.1109/icdmw53433.2021.00124.

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Gerges, Nader, Luisa Ana Barillas Cortez, Midhun Madhavan, et al. "Novel Approach for Machine Learning Assisted Carbonates Reservoirs Saturation Height Modeling and Automated Pore Network Characterization." In 2023 SPWLA 64th Annual Symposium. Society of Petrophysicists and Well Log Analysts, 2023. http://dx.doi.org/10.30632/spwla-2023-0086.

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Key data sets for permeability modeling, rock typing, reservoir characterization, and saturation height modeling (SHM) include capillary pressure (Pc) measurements and conventional core analysis (CCA). The Pc data QA/QC and modeling are time consuming and sensitive to the intricate pore network in Carbonate rocks. In this study, we automate the Pc-based pore network characterization and use machine learning (ML) for capillary pressure modeling in an effort to improve workflow efficiency and lessen interpretation bias. Additionally, a framework for advanced analytics was developed to enable int
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Amadioha, Alexander U., Adam C. Bannister, Simon Slater, and Martin Connelly. "HAZ Toughness: Realistic Testing for Pipeline Integrity." In ASME 2011 30th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2011. http://dx.doi.org/10.1115/omae2011-49179.

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Fracture toughness testing of the heat-affected zone (HAZ) of linepipe seam welds is a requirement for most pipeline projects. Occasionally, low individual values can be measured in the HAZ and these have been attributed to, among other factors, the statistical nature of the HAZ and the associated probability of encountering local brittle zones. The structural significance of these outliers has remained a subject of debate between linepipe users and manufacturers [1], especially as their low significance can be demonstrated via large-scale structurally-representative tests [2–3]. To circumvent
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Amini, Sajjad, Mostafa Sadeghi, Mohsen Joneidi, Massoud Babaie-Zadeh, and Christian Jutten. "Outlier-aware dictionary learning for sparse representation." In 2014 IEEE 24th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2014. http://dx.doi.org/10.1109/mlsp.2014.6958854.

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Stocker, I., P. Evans, M. Jupp, R. Bose, and B. Smith. "Reconciliation of Source-Level Methane Emissions Measurement with Site-Level Measurement Using a Drone-Mounted Sensor: New Insights for Standardisation and Global Deployment." In SPE International Health, Safety, Environment and Sustainability Conference and Exhibition. SPE, 2024. http://dx.doi.org/10.2118/220313-ms.

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Abstract Accurate and transparent reporting of methane emissions is vital for targeted and defensible methane reductions in the energy sector. This is recognised in the United Nations’ new reporting framework (OGMP2.0) where measurement-based reporting of methane emissions that is accompanied with a statement of reconciliation constitutes the highest level of conformance (‘level 5’). A fully traceable top-down measurement system suitable for onshore and offshore deployment was developed to conform with OGMP2.0 level 5 expectations. Site-level methane emissions are measured by means of a drone-
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Reports on the topic "Representative outliers"

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Kemoklidze, Nino. Proportional Representation: Implications for Georgia. Institute of Development Studies, 2022. http://dx.doi.org/10.19088/k4d.2022.111.

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In a process that spanned over three years, Georgia adopted some important changes to its constitution that is meant to complete the country’s transition from a “semi-presidential to a parliamentary system of governance” (Civil.ge, 17 Dec. 2018). In the last 2020 parliamentary elections, Georgia’s current (revised) electoral mixed system was already the most proportional the country had seen in three decades (Bogishvili, 2020). 120 Members of Parliament (MPs) were elected proportionally by a party list (previously the number was 77) and the remaining 30 were single mandate (majoritarian) MPs e
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POLLACK, Katharina, Florian PAUSCH, and Piotr MAJDAK. Parametric pinna model for a realistic representation of listener-specific pinna geometry. Verlag der Österreichischen Akademie der Wissenschaften, 2025. https://doi.org/10.1553/ica_2022_parametric-model.

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Personalised binaural audio requires individual head-related transfer functions (HRTFs). Nowadays, it is feasible to compute HRTFs numerically from meshes of a listener’s anatomy. These meshes require geometric details of the pinna which are difficult to capture and often corrupted by noise and outliers. The alignment of a highresolution template geometry to the captured listener geometry seems to be a promising approach to deal with these corruptions. However, such an alignment represents a multi-dimensional optimisation problem. To tackle this, we describe a parametric pinna model which redu
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Roantree, Barra, Karina Doorley, Theano Kakoulidou, and Seamus O'Malley. Budget 2022. ESRI, 2021. http://dx.doi.org/10.26504/qec2021win_sa_roantree.

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This Article outlines and assesses changes to the tax and welfare system announced as part of Budget 2022. It first looks at the main taxation measures announced before turning to employment, education and social welfare supports. It then considers the effect of the package of measures as a whole on the incomes of households using representative survey data from the Survey of Incomes and Living Conditions run on SWITCH – the ESRI’s tax and benefit microsimulation model – and ITSim – an indirect tax microsimulation model developed jointly by the ESRI and the Department of Finance. The Article c
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Hingre, Garance, and Helen Russell. Ireland's Women in Finance Charter: Annual Report 2024. ESRI, 2024. http://dx.doi.org/10.26504/sustat125.

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Ireland’s Women in Finance Charter is an initiative to address gender imbalances in the sector, by supporting firms’ initiatives to improve their female representation. Created in 2022, the Charter now counts 81 signatories. Together these firms employ over 63,428 workers, representing just over half of employees in the sector. Therefore, the changes undertaken by the signatory firms have the potential to significantly influence the opportunities available to women working in the Irish financial sector. This is the second annual Ireland’s Women in Finance Charter report. It outlines the progre
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Zhang, Jiayi. Mepsy Methodology: Model and Approach for Climate Impact Analysis. CLASP, 2025. https://doi.org/10.70098/ouan6240.

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Mepsy is CLASP’s appliance and equipment climate impact calculator. It’s designed for policymakers, researchers, and other industry stakeholders to model different policy scenarios and measure their impact. Mepsy uses a “bottom-up” accounting approach, considering the number of appliances in use in a country, the energy performance of representative products, the climate-intensity of the local power grid, and other variables to analyze the electricity use, carbon dioxide emissions, and consumer energy costs associated with a given policy scenario. Wherever possible, the calculator references p
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Davies, Rick. Representing Theories Of Change: Technical Challenges With Evaluation Consequences. Centre for Excellence and Development Impact and Learning (CEDIL), 2018. http://dx.doi.org/10.51744/cip15.

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This paper looks at the technical issues associated with the representation of Theories of Change and the implications of design choices for the evaluability of those theories. The focus is on the description of connections between events, rather the events themselves, because this is seen as a widespread design weakness. Using examples and evidence from Internet sources six structural problems are described, along with their consequences for evaluation. The paper then outlines a range of different ways of addressing these problems, which could be used by programme designers, implementers and
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Kinser, Ryan, Mark Barkey, Timothy Rushing, et al. Computationally efficient modeling of lightweight expeditionary airfield surfacing systems at large length scales. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48266.

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Expeditionary airfield matting systems are lightweight, portable surfaces that enable the rapid deployment of infrastructure to support aircraft operations. Individual matting components are assembled via interlocking joints to construct arrays that serve as temporary aircraft operating surfaces. The paper outlines the homogenization of the AM2 portable airfield matting system and its interlocking mechanisms to permit computationally efficient analyses toward understanding mechanisms that influence the global behavior of these arrays and underlying subgrade during aircraft maneuvers. An equiva
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Libra, Jesse Madden. Behind the Numbers: A comparative analysis of international datasets on water and sanitation in Latin America and the Caribbean. Edited by Maria Eugenia De la Peña and María Pérez Urdiales. Inter-American Development Bank, 2024. https://doi.org/10.18235/0013412.

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The development of robust public water and sanitation policies requires dependable, transparent, and representative data; however, the available datasets for the region are complex with indicators that are often misinterpreted by policy professionals. It is essential that users understand the strengths and limitations of these datasets to facilitate source choice and ensure that their use results in accurate insights. This document discusses and contrasts water and sanitation access data produced by three sources: the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygie
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Glewwe, Paul, and Kenn Chua. Learning Environments under COVID-Induced School Closures: Evidence from Vietnam. Research on Improving Systems of Education (RISE), 2023. http://dx.doi.org/10.35489/bsg-rise-ri_2023/056.

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The COVID-19 pandemic has disrupted the education of over 1.5 billion students globally. A majority of students live in countries where schools were either fully closed or were operational only through remote access. As school disruptions are likely to have lasting impacts on children’s human capital accumulation, data documenting how schools and households have adapted to this new learning environment have the potential to provide information on how to curb the adverse effects of school closures on children’s educational progress. Using a telephone survey, the RISE Vietnam country research te
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Oakden, Libby, Rebecca Gillespie, and Abbie Collins. Citizen Science for Food Standards Challenges: Programme Review. Food Standards Agency, 2023. http://dx.doi.org/10.46756/sci.fsa.vhh726.

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Citizen science is an umbrella term that describes a variety of ways in which members of the public can participate in science. The main characteristics of the approach are that: citizens are actively involved in research, in partnership or collaboration with scientists or professionals; and there is a genuine outcome, such as new scientific knowledge, conservation action or policy change. Citizen science involves communities participating in data collection or analysis, or other kinds of collaboration, like co-creating research questions and interpreting data. The approach, endorsed by the Eu
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