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Journal articles on the topic 'Representative outliers'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

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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Wang, Dongqi, Mingshuo Nie, and Dongming Chen. "BAE: Anomaly Detection Algorithm Based on Clustering and Autoencoder." Mathematics 11, no. 15 (2023): 3398. http://dx.doi.org/10.3390/math11153398.

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In this paper, we propose an outlier-detection algorithm for detecting network traffic anomalies based on a clustering algorithm and an autoencoder model. The BIRCH clustering algorithm is employed as the pre-algorithm of the autoencoder to pre-classify datasets with complex data distribution characteristics, while the autoencoder model is used to detect outliers based on a threshold. The proposed BIRCH-Autoencoder (BAE) algorithm has been tested on four network security datasets, KDDCUP99, UNSW-NB15, CICIDS2017, and NSL-KDD, and compared with representative algorithms. The BAE algorithm achie

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Templ, Matthias. "Enhancing Precision in Large-Scale Data Analysis: An Innovative Robust Imputation Algorithm for Managing Outliers and Missing Values." Mathematics 11, no. 12 (2023): 2729. http://dx.doi.org/10.3390/math11122729.

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Navigating the intricate world of data analytics, one method has emerged as a key tool in confronting missing data: multiple imputation. Its strength is further fortified by its powerful variant, robust imputation, which enhances the precision and reliability of its results. In the challenging landscape of data analysis, non-robust methods can be swayed by a few extreme outliers, leading to skewed imputations and biased estimates. This can apply to both representative outliers—those true yet unusual values of your population—and non-representative outliers, which are mere measurement errors. D

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Mohammed, Ruqayah, and Miklas Scholz. "Quality Control and Homogeneity Analysis of Precipitation Time Series in the Climatic Region of Iraq." Atmosphere 14, no. 2 (2023): 197. http://dx.doi.org/10.3390/atmos14020197.

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Non-climatic reasons, such as station replacement and changing the measurement device and calculation method, may make climate data unrepresentative of the actual variation of the regional climate. Data quality control and homogenization tests for climate data are critical. Thus, this study aims to evaluate the quality and homogeneity level of precipitation time series in arid and semi-arid climatic zones and specify the breakpoint in the datasets. The proposed methodology has been used to conduct arid and semi-arid representative case studies comprising 40 annual precipitation series for Iraq

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Frouard, Julien. "Robust Estimates of Orientation between Astrometric Catalogs." Astronomical Journal 165, no. 5 (2023): 202. http://dx.doi.org/10.3847/1538-3881/acc6cb.

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Abstract Accurately comparing two celestial reference frames based on the observed position of a number of common objects requires to detect and appropriately process outliers, lest they spuriously influence the results. It is thus of practical importance to use algorithms able to minimize the impact of those outliers when comparing radio and/or optical astrometric catalogs. In this paper, we investigate and compare the performances of some well-established and more recent robust algorithms when fitting a simple rotation vector between two reference frames. We particularly focus on two aspects

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Sanou, Amande-Gaston, Ali Saeidi, Shahriyar Heidarzadeh, Rama Vara Prasad Chavali, Hamza Es Samti, and Alain Rouleau. "Geotechnical Parameters of Landslide-Prone Laflamme Sea Deposits, Canada: Uncertainties and Correlations." Geosciences 12, no. 8 (2022): 297. http://dx.doi.org/10.3390/geosciences12080297.

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Due to inherent variability arising from unpredictable geological depositional and post-depositional processes, the geotechnical parameters of Laflamme sea clay deposits remain highly uncertain. This study aims to develop and apply a methodology to assess the uncertainties of geotechnical parameters using statistical distributions for a landslide-prone Saguenay Lac-Saint-Jean (SLSJ) region. We used the measured physical and mechanical parameters of Laflamme Sea clays of various locations in the SLSJ region to characterize the geotechnical parameters in a representative manner. Goodness-of-fit

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Kamyab, Shima, Zohreh Azimifar, Rasool Sabzi, and Paul Fieguth. "Deep learning methods for inverse problems." PeerJ Computer Science 8 (May 2, 2022): e951. http://dx.doi.org/10.7717/peerj-cs.951.

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In this paper we investigate a variety of deep learning strategies for solving inverse problems. We classify existing deep learning solutions for inverse problems into three categories of Direct Mapping, Data Consistency Optimizer, and Deep Regularizer. We choose a sample of each inverse problem type, so as to compare the robustness of the three categories, and report a statistical analysis of their differences. We perform extensive experiments on the classic problem of linear regression and three well-known inverse problems in computer vision, namely image denoising, 3D human face inverse ren

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Pérez, Francisco Maciá, Jose Vicente Berna Martienz, Alberto Fernández Oliva, and Miguel Abreu Ortega. "Application of the Variable Precision Rough Sets Model to Estimate the Outlier Probability of Each Element." Complexity 2018 (October 8, 2018): 1–14. http://dx.doi.org/10.1155/2018/4867607.

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In a data mining process, outlier detection aims to use the high marginality of these elements to identify them by measuring their degree of deviation from representative patterns, thereby yielding relevant knowledge. Whereas rough sets (RS) theory has been applied to the field of knowledge discovery in databases (KDD) since its formulation in the 1980s; in recent years, outlier detection has been increasingly regarded as a KDD process with its own usefulness. The application of RS theory as a basis to characterise and detect outliers is a novel approach with great theoretical relevance and pr

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Dovečar, Matjaž, Jaša Saražin, and Matevž Triplat. "Cene gozdnih zemljišč v Sloveniji v obdobju 2018-2022." Acta Silvae et Ligni 133 (2024): 13–28. http://dx.doi.org/10.20315/asetl.133.2.

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In this article, we analyze the forest land market in Slovenia over the past five years, using the Slovenian Real Estate Market Register as the primary data source. The proposed methodological approach enables the extraction of transactions involving only forest land. The analysis reveals an increase in forest land prices over time, statistically significant differences across statistical regions and generally high prices for forest land. To ensure the reliability of our results, we excluded outlier values, thus providing a more representative overview of the real estate market for forest land

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19

Gastmeier, Petra, Dorit Sohr, Dietmar Forster, et al. "Identifying Outliers of Antibiotic Usage in Prevalence Studies on Nosocomial Infections." Infection Control & Hospital Epidemiology 21, no. 5 (2000): 324–28. http://dx.doi.org/10.1086/501764.

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AbstractObjective:To investigate whether the correlation between patients' antibiotic treatment (yes/no) and patients' infections (yes/no) in each hospital department, described by Pearson's correlation coefficient (ρ) for binary data as a measure for adequate use of antibiotics, is an appropriate quality indicator.Design:Comparison of the results of repeated prevalence studies in different hospitals with the data of a national prevalence study, comparing the hospital (ρ) and reference (ρNIDHP[Nosokomiale Infektionen in Deutschland: Erfassung und Prävention]) correlation coefficients for “use

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20

Deep, Shaleen, Anja Gruenheid, Paraschos Koutris, Jeffrey Naughton, and Stratis Viglas. "Comprehensive and efficient workload compression." Proceedings of the VLDB Endowment 14, no. 3 (2020): 418–30. http://dx.doi.org/10.14778/3430915.3430931.

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This work studies the problem of constructing a representative workload from a given input analytical query workload where the former serves as an approximation with guarantees of the latter. We discuss our work in the context of workload analysis and monitoring. As an example, evolving system usage patterns in a database system can cause load imbalance and performance regressions which can be controlled by monitoring system usage patterns, i.e., a representative workload, over time. To construct such a workload in a principled manner, we formalize the notions of workload representativity and

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Fernandez-Lozano, Carlos, Adrian Carballal, Penousal Machado, Antonino Santos, and Juan Romero. "Visual complexity modelling based on image features fusion of multiple kernels." PeerJ 7 (July 18, 2019): e7075. http://dx.doi.org/10.7717/peerj.7075.

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Humans’ perception of visual complexity is often regarded as one of the key principles of aesthetic order, and is intimately related to the physiological, neurological and, possibly, psychological characteristics of the human mind. For these reasons, creating accurate computational models of visual complexity is a demanding task. Building upon on previous work in the field (Forsythe et al., 2011; Machado et al., 2015) we explore the use of Machine Learning techniques to create computational models of visual complexity. For that purpose, we use a dataset composed of 800 visual stimuli divided i

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22

Karlberg, Forough. "Small area estimation for skewed data in the presence of zeroes." Statistics in Transition new series 16, no. 4 (2015): 541–62. http://dx.doi.org/10.59170/stattrans-2015-029.

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Skewed distributions with representative outliers pose a problem in many surveys. Various small area prediction approaches for skewed data based on transformation models have been proposed. However, in certain applications of those predictors, the fact that the survey data also contain a non-negligible number of zero-valued observations is sometimes dealt with rather crudely, for instance by arbitrarily adding a constant to each value (to allow zeroes to be considered as “positive observations, only smaller”, instead of acknowledging their qualitatively different nature). On the other hand, wh

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Wang, J., H. B. Zheng, and M. Scaioni. "TOWARDS DESCRIBING FULL-SECTION DEFORMATIONS USING TERRESTRIAL LASER SCANNING IN THE BADALING TUNNEL (CHINA)." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 235–43. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-235-2020.

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Abstract. This paper focuses on the analysis of point clouds from terrestrial laser scanning to interpret possible deformations of the new Badaling Tunnel that was built for the Winter Olympics 2022 in the nearby of Beijing, China. A reference framework is established to compare data corresponding to various days with blocks of uniform columns and rows from an estimated tunnel axis. Filling holes and detecting outliers are performed for quasi-planar estimation, and refinement transformation is used to adjust the data errors between different days. Finally, the full-section deformations are det

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Owerko, Tomasz, Piotr Owerko, and Karolina Tomaszkiewicz. "Robust Open-Source Solution for Bridge Decrement Estimation for Data with Outliers." Civil Engineering Journal 8, no. 4 (2022): 637–53. http://dx.doi.org/10.28991/cej-2022-08-04-02.

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Dynamic tests enable assessment of the structure’s technical condition and provide information necessary for management and maintenance throughout the object’s life cycle. On their basis, the dynamic characteristics of the object are estimated (e.g., the logarithmic decrement). The possible occurrence of atypical features in the obtained signal (e.g. amplitude beat, outliers), as well as the influence of the type of devices and sensors used for measurements, should be considered. If these features are omitted during the analysis, key dynamic characteristics may be evaluated incorrectly. Theref

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Hartwig, Timothy B., Geraldine Naughton, and John Searl. "Defining the Volume and Intensity of Sport Participation in Adolescent Rugby Union Players." International Journal of Sports Physiology and Performance 3, no. 1 (2008): 94–106. http://dx.doi.org/10.1123/ijspp.3.1.94.

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Purpose:Investigating adolescent training loads might help us understand optimal training adaptations. GPS tracking devices and training diaries were used to quantify weekly sport and other physical activity demands placed on adolescent rugby union players and profile typical rugby training sessions.Methods:Participants were 75 males age 14 to 18 y who were recruited from rugby teams representing 3 levels of participation: schoolboy, national representative, and a selective sports school talent squad.Results:Schoolboy players covered a distance of (mean ± SD) 3511 ± 836 m, representative-squad

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Jäntschi, Lorentz. "A Test Detecting the Outliers for Continuous Distributions Based on the Cumulative Distribution Function of the Data Being Tested." Symmetry 11, no. 6 (2019): 835. http://dx.doi.org/10.3390/sym11060835.

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One of the pillars of experimental science is sampling. Based on the analysis of samples, estimations for populations are made. There is an entire science based on sampling. Distribution of the population, of the sample, and the connection among those two (including sampling distribution) provides rich information for any estimation to be made. Distributions are split into two main groups: continuous and discrete. The present study applies to continuous distributions. One of the challenges of sampling is its accuracy, or, in other words, how representative the sample is of the population from

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Wang, Chuyuan. "Optimization strategy of automobile dispatching based on neural network model." E3S Web of Conferences 271 (2021): 01020. http://dx.doi.org/10.1051/e3sconf/202127101020.

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As a representative product of the sharing economy era and a powerful supplement to public transportation shared cars have the characteristics of convenience, efficiency, environmental protection, and green travel, and to a certain extent alleviate the contradiction between supply and demand, and solve the problem of long-term idle vehicles and overloaded operation of roads problems. But the uneven distribution of shared cars, the coexistence of no cars, and empty seats will happen. To solve the above problems, this article first analyzes data outliers, data missing values, and data standardiz

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Golovko, Victor V. "Robust Method for Confidence Interval Estimation in Outlier-Prone Datasets: Application to Molecular and Biophysical Data." Biomolecules 15, no. 5 (2025): 704. https://doi.org/10.3390/biom15050704.

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Estimating confidence intervals in small or noisy datasets is a recurring challenge in biomolecular research, particularly when data contain outliers or exhibit high variability. This study introduces a robust statistical method that combines a hybrid bootstrap procedure with Steiner’s most frequent value (MFV) approach to estimate confidence intervals without removing outliers or altering the original dataset. The MFV technique identifies the most representative value while minimizing information loss, making it well suited for datasets with limited sample sizes or non-Gaussian distributions.

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Hurka, Steffen, and Constantin Kaplaner. "Are popular and powerful committees more representative? Evidence from the ninth European Parliament." Research & Politics 7, no. 2 (2020): 205316802091445. http://dx.doi.org/10.1177/2053168020914453.

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The standing committees of the European Parliament perform crucial policy-making functions and, accordingly, the question of how they are composed assumes great relevance. Unlike previous studies, which primarily looked at committee assignments from the perspective of individual MEPs, we assess the representativeness of entire committees by comparing their preference distributions with those we find in the plenary on the left/right and pro/anti EU dimension. For our analysis, we combine new data on committee memberships in the ninth European Parliament with data on policy preferences of nation

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Tang, Keke, Yawen Shi, Jianpeng Wu, et al. "NormalAttack: Curvature-Aware Shape Deformation along Normals for Imperceptible Point Cloud Attack." Security and Communication Networks 2022 (August 12, 2022): 1–11. http://dx.doi.org/10.1155/2022/1186633.

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Many efforts have been made on developing adversarial attack methods on point clouds. However, without fully considering the geometric property of point clouds, existing methods tend to produce clearly visible outliers. In this paper, we propose a novel NormalAttack framework towards imperceptible adversarial attacks on point clouds. First, we enforce the perturbation to be concentrated along normals to deform the underlying surface of 3D point clouds, such that tiny perturbation can make the shape deformed for better attack performance. Second, we guide the perturbation to be located more on

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Zhao, Hongbo, Lin Zhang, Jiaolong Ren, Meng Wang, and Zhiqiang Meng. "AdaBoost-Based Back Analysis for Determining Rock Mass Mechanical Parameters of Claystones in Goupitan Tunnel, China." Buildings 12, no. 8 (2022): 1073. http://dx.doi.org/10.3390/buildings12081073.

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The back analysis is an effective tool to determine the representative values of rock mass mechanical properties in rock engineering. The surrogate model is widely used in back analyses since analytical or numerical models are usually unavailable for practical engineering problems. This study proposes a novel back analysis framework by adopting the AdaBoost algorithm for deriving the surrogate model. Moreover, the simplicial homology global optimization (SHGO) algorithm, which is robust and applicable for a black-box global problem, is also integrated into the framework. To evaluate the perfor

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Vladova, Alla, and Elena Shek. "Data preprocessing for machine analysis of sales representatives’ key performance indicators." Business Informatics 15, no. 3 (2021): 48–59. http://dx.doi.org/10.17323/2587-814x.2021.3.48.59.

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Significant transformation of the operational activity of product and service distributors is driven by changes in data-receiving and processing technology. At present, the work of these companies’ representatives is digitized to a large extent: for example, the road time, the number and places of meetings with customers are automatically recorded. At the same time, the productivity of managers who do not make direct sales is usually evaluated with the help of surveys, experts and costly double visits, although the existence of large data samples makes possible the use of statistical analysis

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Hadzic, Arnela, Martin Urschler, Jan-Niclas Aaron Press, et al. "Evaluating a Periapical Lesion Detection CNN on a Clinically Representative CBCT Dataset—A Validation Study." Journal of Clinical Medicine 13, no. 1 (2023): 197. http://dx.doi.org/10.3390/jcm13010197.

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The aim of this validation study was to comprehensively evaluate the performance and generalization capability of a deep learning-based periapical lesion detection algorithm on a clinically representative cone-beam computed tomography (CBCT) dataset and test for non-inferiority. The evaluation involved 195 CBCT images of adult upper and lower jaws, where sensitivity and specificity metrics were calculated for all teeth, stratified by jaw, and stratified by tooth type. Furthermore, each lesion was assigned a periapical index score based on its size to enable a score-based evaluation. Non-inferi

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Mercier, Norbert, Jean-Michel Galharret, Chantal Tribolo, Sebastian Kreutzer, and Anne Philippe. "Luminescence age calculation through Bayesian convolution of equivalent dose and dose-rate distributions: the <i>D</i><sub>e</sub>_<i>D</i><sub>r</sub> model." Geochronology 4, no. 1 (2022): 297–310. http://dx.doi.org/10.5194/gchron-4-297-2022.

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Abstract. In nature, each mineral grain (quartz or feldspar) receives a dose rate (Dr) specific to its environment. The dose-rate distribution therefore reflects the micro-dosimetric context of grains of similar size. If all the grains were well bleached at deposition, this distribution is assumed to correspond, within uncertainties, with the distribution of equivalent doses (De). The combination of the De and Dr distributions in the De_Dr model proposed here would then allow calculation of the true depositional age. If grains whose De values are not representative of this age (hereafter calle

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Feldmann, Rodney M., and Carrie E. Schweitzer. "The enigmatic Cyclida (Pancrustacea): morphological terminology and family-level classification." Journal of Crustacean Biology 39, no. 5 (2019): 617–33. http://dx.doi.org/10.1093/jcbiol/ruz053.

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Abstract The Cyclida comprise six families embracing 55 species in 17 genera. Diagnoses for each family based upon examination of type and referred material as well as photographs of specimens previously known only from drawings has confirmed that four of the families, Cyclidae Packard, 1885; Americlidae Dzik, 2008; Halicynidae Gall & Grauvogel, 1967; and Schraminidae Dzik, 2008, form a unified group morphologically whereas Alsasuacaridae van Bakel, Jagt, Fraaije & Artal, 2011, and Hemitrochiscidae Trauth, 1918 are morphological outliers. Each of the families is documented by illustrat

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Posada-Hernández, Gabriel Jaime, Mauricio López-Bonilla, Diego Alejandro Uribe-Suarez, Viviana Gómez-Ceballos, and Luis Fernando Cardona-Palacio. "Estimating the added value of critical reading competence in college students using statistical modeling." Revista de Investigación, Desarrollo e Innovación 15, no. 1 (2025): 67–86. https://doi.org/10.19053/uptc.20278306.v15.n1.2025.18823.

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This study develops a model to predict students' scores and added value in the critical reading competency at Luis Amigó Catholic University, Medellín campus (Colombia). The institutional added value is assessed by comparing results from the Saber 11 test with an institutional exam conducted in the fifth semester. A linear correlation matrix was used to identify statistically significant variables at a 95% confidence level, with the accumulated average emerging as the most representative variable. Assumptions of linearity, normality, independence, collinearity, and homoscedasticity were verifi

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Fotakis, Dimitris, Panagiotis Patsilinakos, Eleni Psaroudaki, and Michalis Xefteris. "Efficient Time-Series Clustering through Sparse Gaussian Modeling." Algorithms 17, no. 2 (2024): 61. http://dx.doi.org/10.3390/a17020061.

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In this work, we consider the problem of shape-based time-series clustering with the widely used Dynamic Time Warping (DTW) distance. We present a novel two-stage framework based on Sparse Gaussian Modeling. In the first stage, we apply Sparse Gaussian Process Regression and obtain a sparse representation of each time series in the dataset with a logarithmic (in the original length T) number of inducing data points. In the second stage, we apply k-means with DTW Barycentric Averaging (DBA) to the sparsified dataset using a generalization of DTW, which accounts for the fact that each inducing p

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Muyulema-Masaquiza, Darío, and Manuel Ayala-Chauvin. "Segmentation of Energy Consumption Using K-Means: Applications in Tariffing, Outlier Detection, and Demand Prediction in Non-Smart Metering Systems." Energies 18, no. 12 (2025): 3083. https://doi.org/10.3390/en18123083.

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The management of energy demand in systems lacking smart metering presents a significant challenge for electric distributors, primarily due to the absence of real-time data. This research assesses the efficacy of the K-Means algorithm when applied to the monthly billing records of 221,401 residential customers from Empresa Eléctrica Ambato Regional Centro Norte S.A. (EEASA) (Ecuador) over the period 2023–2024. The methodology encompassed data cleaning, Z-score normalization, and validation employing the Silhouette (0.55) and Davies–Bouldin (0.51) indices. Additionally, linear regression (LR) a

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Xiao, Xiang, and Yuhong Sheng. "Hampel Estimation for Uncertain Autoregressive Model." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 33, no. 04 (2025): 507–24. https://doi.org/10.1142/s0218488525500217.

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Parameter estimation is widely used as an essential branch of uncertain time series, among which the least squares (LSE) estimation is the most representative. Since LSE estimation is ineffective in the presence of outliers, Hampel estimation that its stability solves this problem well. Therefore, in this paper, we use Hampel estimation to calculate the parameters of the uncertain autoregressive (UAR) model. The sum of sample errors (SSE) function is used to determine the parameters in Hampel estimation before fitting the UAR model. In addition, the residuals are analysed, and future trends in

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Ganesan, A. L., M. Rigby, A. Zammit-Mangion, et al. "Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods." Atmospheric Chemistry and Physics Discussions 13, no. 12 (2013): 33403–31. http://dx.doi.org/10.5194/acpd-13-33403-2013.

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Abstract. We present a hierarchical Bayesian method for atmospheric trace gas inversions. This method is used to estimate emissions of trace gases as well as "hyper-parameters" that characterize the probability density functions (PDF) of the a priori emissions and model-measurement covariances. By exploring the space of "uncertainties in uncertainties", we show that the hierarchical method results in a more complete estimation of emissions and their uncertainties than traditional Bayesian inversions, which rely heavily on expert judgement. We present an analysis that shows the effect of includ

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Ganesan, A. L., M. Rigby, A. Zammit-Mangion, et al. "Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods." Atmospheric Chemistry and Physics 14, no. 8 (2014): 3855–64. http://dx.doi.org/10.5194/acp-14-3855-2014.

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Abstract. We present a hierarchical Bayesian method for atmospheric trace gas inversions. This method is used to estimate emissions of trace gases as well as "hyper-parameters" that characterize the probability density functions (PDFs) of the a priori emissions and model-measurement covariances. By exploring the space of "uncertainties in uncertainties", we show that the hierarchical method results in a more complete estimation of emissions and their uncertainties than traditional Bayesian inversions, which rely heavily on expert judgment. We present an analysis that shows the effect of includ

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Hurwitz, Mark S., Roger J. Moiles, and David W. Rohde. "Distributive and Partisan Issues in Agriculture Policy in the 104th House." American Political Science Review 95, no. 4 (2001): 911–22. http://dx.doi.org/10.1017/s0003055400400092.

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Distributive, informational, and partisan rationales provide contrasting accounts of legislative politics. Which of these theories best explains the decisions and policies of the House Agriculture Committee? We contend that all are appropriate, at various times, depending upon the particular issues and circumstances. By analyzing issues within the jurisdiction of the Agriculture Committee and the Agriculture and Rural Development Subcommittee of the Appropriations Committee in the 104th House, we demonstrate that the issue environments of both are multidimensional (even within individual bills

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Hurwitz, Mark S., Roger J. Moiles, and David W. Rohde. "Distributive and Partisan Issues in Agriculture Policy in the 104th House." American Political Science Review 95, no. 4 (2001): 923–37. http://dx.doi.org/10.1017/s0003055400400109.

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Distributive, informational, and partisan rationales provide contrasting accounts of legislative politics. Which of these theories best explains the decisions and policies of the House Agriculture Committee? We contend that all are appropriate, at various times, depending upon the particular issues and circumstances. By analyzing issues within the jurisdiction of the Agriculture Committee and the Agriculture and Rural Development Subcommittee of the Appropriations Committee in the 104th House, we demonstrate that the issue environments of both are multidimensional (even within individual bills

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Droutsa, Kalliopi G., Constantinos A. Balaras, Spyridon Lykoudis, Simon Kontoyiannidis, Elena G. Dascalaki, and Athanassios A. Argiriou. "Baselines for Energy Use and Carbon Emission Intensities in Hellenic Nonresidential Buildings." Energies 13, no. 8 (2020): 2100. http://dx.doi.org/10.3390/en13082100.

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This work exploits data from 30,000 energy performance certificates of whole nonresidential (NR) buildings in Greece. The available information is analyzed for 30 different NR building uses (e.g., hotels, schools, sports facilities, hospitals, retails, offices) and four main services (space heating, space cooling, domestic hot water and lighting). Data are screened in order to exclude outliers and checked for consistency with the Hellenic NR building stock. The average energy use and CO2 emission intensities for all building uses are calculated, as well as the respective energy ratings in orde

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Johnson, Branden B., and Nathan F. Dieckmann. "Americans’ views of scientists’ motivations for scientific work." Public Understanding of Science 29, no. 1 (2019): 2–20. http://dx.doi.org/10.1177/0963662519880319.

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Scholars have not examined public views of scientific motivations directly, despite scientific authority implications. A US representative sample rated 11 motivations both descriptively (they do motivate scientists’ work) and normatively (they should motivate scientists) for scientists employed by federal government agency, large business corporation, advocacy group (nonprofit seeking to influence policy), or university. Descriptive and normative ratings fell into extrinsic (money, fame, power, being liked, helping employer) and intrinsic (do good science, enjoy challenge, helping society and

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Horesco, Joseph, and Bill Yoder. "Considerations for extending sound studies beyond typical regulatory compliance requirements for data centers in suburban and rural environments." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 269, no. 1 (2024): 1303–12. http://dx.doi.org/10.3397/nc_2024_0171.

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The escalating presence of data centers in suburban and rural environments has emerged as a potential new source of noise in areas often lacking comprehensive noise regulations. Where noise regulations do exist, many do not provide spectral limitations or other methods of assessing and regulating the frequently cited community concerns such as low-frequency noise (LFN) and tonality of the sounds. This may lead to public dissatisfaction of the data center sounds despite compliance with the applicable noise limits. This paper provides an overview of representative codes and standards to help ill

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Yan, Si-Min, Jun-Ping Liu, Lu Xu, et al. "Rapid Discrimination of the Geographical Origins of an Oolong Tea (Anxi-Tieguanyin) by Near-Infrared Spectroscopy and Partial Least Squares Discriminant Analysis." Journal of Analytical Methods in Chemistry 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/704971.

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This paper focuses on a rapid and nondestructive way to discriminate the geographical origin of Anxi-Tieguanyin tea by near-infrared (NIR) spectroscopy and chemometrics. 450 representative samples were collected from Anxi County, the original producing area of Tieguanyin tea, and another 120 Tieguanyin samples with similar appearance were collected from unprotected producing areas in China. All these samples were measured by NIR. The Stahel-Donoho estimates (SDE) outlyingness diagnosis was used to remove the outliers. Partial least squares discriminant analysis (PLSDA) was performed to develop

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Vandewege, Michael W., Cibele G. Sotero-Caio, and Caleb D. Phillips. "Positive Selection and Gene Expression Analyses from Salivary Glands Reveal Discrete Adaptations within the Ecologically Diverse Bat Family Phyllostomidae." Genome Biology and Evolution 12, no. 8 (2020): 1419–28. http://dx.doi.org/10.1093/gbe/evaa151.

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Abstract The leaf-nosed bats (Phyllostomidae) are outliers among chiropterans with respect to the unusually high diversity of dietary strategies within the family. Salivary glands, owing to their functions and high ultrastructural variability among lineages, are proposed to have played an important role during the phyllostomid radiation. To identify genes underlying salivary gland functional diversification, we sequenced submandibular gland transcriptomes from phyllostomid species representative of divergent dietary strategies. From the assembled transcriptomes, we performed an array of select

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Zhong, Wantong. "Portfolio Construction in Terms of Bitcoin, S&P 500, Gold Futures and American 10-year National Debt." BCP Business & Management 35 (December 31, 2022): 675–82. http://dx.doi.org/10.54691/bcpbm.v35i.3369.

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Contemporarily, the financial sector occupies a vital part of the world, and asset allocation in it is the top priority. Portfolio construction is the main procedure of asset allocation. In order to gain better application of asset allocation, this paper selects two representative models from the most classic asset allocation models (i.e., Index model and Markowitz model), as well as processes and analyzes them with actual data and targets to obtain real model results. Specifically, this article compares the curves under five different constraints from two models. According to the analysis, th

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Agustia Hananto, Tukino Tukino, and Elfina Novalia. "Klasterisasi Kesiapan Digital Daerah." Jurnal Ilmiah Teknik Informatika dan Komunikasi 5, no. 1 (2024): 28–41. https://doi.org/10.55606/juitik.v5i1.979.

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Public sector digital transformation requires a deep understanding of the digital readiness of each administrative region. The Electronic Government System (EGIS) Index is used by the Government of Indonesia as a measuring tool to assess the digital maturity of government agencies. This study aims to cluster districts/cities in West Java Province based on their 2023 EGIS scores to identify hidden patterns of digital readiness. Three unsupervised learning algorithms—K-Means, DBSCAN, and Agglomerative Clustering—are used to explore data-driven regional segmentation. The analyzed dataset includes

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