Rejection Method Research Articles

Multiple-point statistics and non-colocational soft data integration

In geostatistics, available conditional information is typically categorized as either hard (with no uncertainty) or soft (associated with an uncertainty) data. 2-point based Gaussian (kriging) simulation methods have since their inception been able to account for hard and soft data. In contrast, multiple-point statistical (MPS) simulation methods, that allow quantifying more complex phenomena than Gaussian methods, have focused mostly on conditioning to hard data. The most widely used method for accounting for soft data is to consider only co-located soft data. Here we define conditional information data through a probability distribution and show how a general probabilistic solution, in the form of a posterior probability distribution, can be obtained, combining conditional categorical information with information from a categorical training image. We demonstrate how to compute the conditional distribution, as needed by most MPS-based sequential simulation algorithms, that allows accounting for both co- and non-co-located hard and soft data. Examples are provided comparing the direct computation of conditional statistics to using more computational demanding rejection sampling. In order to reduce the significant computational demands, a distance-based measure is proposed to increase the entropy of conditional data, based on the distance to the point being estimated and simulated.

The association between peer rejection and aggression types: A meta-analysis

BackgroundAlthough previous studies have assessed the association between peer rejection and aggression, the results are mixed. ObjectiveThis article presents a meta-analysis of the association between peer rejection and aggression types (overt vs. relational) among children and adolescents. Participants and settingA total of 61 eligible studies with 70 independent effect sizes were included in the analysis (45,966 participants, Mage = 10.34, SD = 3.13). MethodsFirst, random-effects meta-analyses were conducted to explore the association between peer rejection and aggression types (overt vs. relational). Next, moderation analyses were conducted based on the Q statistics for categorical variables (culture, reporting method of peer rejection, reporting method of aggression) and the meta-regression analyses for continuous variable (age). ResultsPeer rejection was positively correlated with overall aggression (r = 0.42, 95 % CI [0.38, 0.47], p < 0.001), overt aggression (r = 0.46, 95 % CI [0.38, 0.54], p < 0.001) and relational aggression (r = 0.43, 95 % CI [0.35, 0.51], p < 0.001). This correlation was positive for each type when controlling for other form of aggression. Moderation analyses suggested that reporting method of aggression (self-report vs. peer-nomination vs. adult-report vs. observation), reporting method of peer rejection (self-report vs. peer-nomination vs. adult-report) and culture (collectivist vs. individualist) were moderators of the association between peer rejection and overall aggression. Culture moderated the association between peer rejection and overt aggression, while age moderated the association between peer rejection and relational aggression. ConclusionsThe findings showed a strong positive association between peer rejection and aggression, although this association varied by aggression type and other moderating variables.

Cooper-Frye sampling with short-range repulsion

This work incorporates the effect of short-range repulsion between particles into the Cooper-Frye hadron sampling procedure. This is achieved by means of a rejection sampling step, which prohibits any pair of particles from overlapping in the coordinate space, effectively modeling the effect of hard-core repulsion. The new procedure---called the fist sampler---is based on the package thermal-fist. It is used here to study the effect of excluded volume on cumulants of the (net-)proton number distribution in central collisions of heavy ions in a broad collision energy range in conjunction with exact global conservation of baryon number, electric charge, and strangeness. The results are compared with earlier calculations based on analytical approximations, quantifying the accuracy of the latter at different collision energies. An additional advantage of the new method over the analytic approaches is that it offers the flexibility provided by event generators, making it straightforwardly extendable to other observables.

Investigating the effect of depth of interaction on coincidence time resolution

Abstract The timing properties of scintillator-based detectors are a matter of importance in a wide range of fields. As fast scintillators progress and the sensors that preserve the quantized nature of the luminescent signal evolve, the optical transfer time may not always be negligible in ultra-fast time measurement. The present paper discusses the implications of this in a specific configuration for ultra-fast time measurement. To this end, we consider factors influencing the time spread such as the distribution of depth of interaction, non-instantaneous scintillation emission mechanism, and optical transport kinetics. Although such a topic has previously been studied by researchers, the aim of this work is focused on analyzing the detailed factors that govern arrival time spread. From these factors, we are able to obtain the post-interaction time spread, and we then derived the optical transfer time spread (OTTS) by a weighted sum of post-interaction time spread based on the cumulative density function of the depth of interaction. Based on the rejection sampling method, we could obtain the set of arrival times by the OTTS, and then the coincidence time resolution of radioactive sources was calculated. Consequently, we found that the difference in the attenuation coefficient causes the difference in the arrival time distribution, but it does not lend a significant contribution to the coincidence time resolution. In addition, when radiation with different energies is incident, the emission mechanism has a dominant effect on the time resolution, and thus incident radiation having higher energy in the same detector system has a higher light yield, which can exhibit better timing resolution compared to radiation with relatively low energy.

Sampling methods for the concentration parameter and discrete baseline of the Dirichlet Process

Abstract There are many models in the current statistical literature for making inferences based on samples selected from a finite population. Parametric models may be problematic because statistical inference is sensitive to parametric assumptions. The Dirichlet process (DP) prior is very flexible and determines the complexity of the model. It is indexed by two hyper-parameters: the baseline distribution and concentration parameter. We address two distinct problems in the article. Firstly, we review the current sampling methods for the concentration parameter, which use the continuous baseline distribution. We compare three different methods: the adaptive rejection method, the mixture of Gammas method and the grid method. We also propose a new method based on the ratio of uniforms. Secondly, in practice, some survey responses are known to be discrete. If a continuous distribution is adopted as the baseline distribution, the model is misspecified and standard inference may be invalid. We propose a discrete baseline approach to the DP prior and sample the unobserved responses from the finite population both using a Polya urn scheme and a Multinomial distribution. We applied our discrete baseline approach to a Phytophthora data set.

A new Autism Spectrum Disorder Discovery (ASDD) strategy using data mining techniques based on blood tests

Autism Spectrum Disorder (ASD) represents a heterogeneous developmental disability characterized by impairments in social communication and behavioral challenges. The problem of ASD begins with childhood and continues into adolescence as well as adulthood. The number of children suffering from ASD is constantly increasing, so a modern method must be found to make a quick and early diagnosis or discovery of ASD patients with high accuracy. Early discovery of ASD patients aims to quickly take the necessary measures with patients and take care of them until their speedy recovery. During this paper, a new Autism Spectrum Disorder Discovery (ASDD) strategy is provided to give a fast and delicate diagnosis for autism patients. The ASDD composes of two main layers, which are; (i) Pre-processing Layer (PL) and (ii) Autism Discovery Layer (ADL). In the PL, two approaches called feature selection and outlier rejection are used to filter the used data from any non-informative data before starting to train the diagnostic model in the ADL for providing delicate diagnosis. Hybrid Rejection Technique (HRT) as a new outlier rejection method is the main contribution of this paper. HRT includes two main stages called Quick Rejection Stage (QRS) as a fast stage and Precise Rejection Stage (PRS) as a delicate stage. QRS uses a standard deviation to quickly remove outliers and then PRS uses a Hybrid Bio-inspired Optimization Method (HBOM) that combines Binary Genetic Algorithm (BGA) and Binary Gray Wolf Optimizer (BGWO) to delicately eliminate the rest of outliers. According to PL in ASDD strategy, Fisher Score (FS) is applied to select the best subset of features and then HRT is used to reject bad training data. Finally, an Ensemble Technique (ET)that includes three classifiers called Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Deep Learning (DL) is used in ADL to diagnose ASD patients based on the filtered data from PL. The ASDD strategy has been compared to many modern strategies using ASD dataset that includes blood tests from children [1]. Experimental results proven that the ASDD strategy outperforms the other strategies in terms of many metrics called accuracy, error, sensitivity, precision, and execution-time with values 0.92, 0.08, 0.85, 0.80, and 2 s.

Perceived partial social integration, levels of distress and resilience, and COVID-19 vaccine rejection of Jewish and Arab citizens of Israel.

The present study examines the role of perceived partial social integration (PPSI) in determining the rejection of the COVID-19 vaccine of Jewish and Arab citizens of Israel. The research hypotheses are examined using a relatively large sample of the Israeli public, including 208 Arab and 600 Jewish adults, who have responded to an anonymous questionnaire pertaining, among other issues, to partial social integration and the individual level of vaccine uptake. Higher levels of PPSI were found to be associated with higher levels of vaccine rejection, in both Jewish and Arab samples. The Arab minority group regards themselves as less socially integrated into the Israeli society and therefore rejects the COVID-19 vaccine to a greater extent than the majority group. The Arab respondents expressed a higher level of psychological distress and a lower level of resilience compared with the Jewish participants. The perceived partial social integration score significantly predicted the levels of distress and resilience of the Jewish but not the Arab sample. The study indicates that increasing the vaccination rates depends more substantially on trust in the authorities than on leveraging greater pressure on individuals that reject the vaccine. Increased trust in the authorities and regarding oneself as an integral component of society are two vital conditions for vaccine acquiescence. Insufficient social integration is a major reason for vaccine rejection.

On error reduction by the symmetric rejection method in multi-stage biometric verification systems

On error reduction by the symmetric rejection method in multi-stage biometric verification systems

Results of using Luer-Lok access device for clinics, intensive care units, and emergency services with high pre-analytical errors: analysis of 491.850 samples

Abstract Objectives The effectiveness of the Luer-Lok™ Access Device (LL) with the intravenous catheter (IVC) on sample rejections, which is used to prevent primarily hemolysis in the emergency department (ED), clinics, and intensive care units (ICU), was examined. Methods A total of 491.850 samples of eight months from Amasya University Sabuncuoğlu Şerefeddin Training and Research Hospital were investigated retrospectively. Inpatient, intensive care unit and emergency department samples were included in the study. Pre- (BLL) and post-Luer Lok (ALL) rejection of the samples analyzed. In the BLL period, 3,463 rejection samples out of 253,818 (1.36%) in the September-December period of 2020; in the ALL period, 1819 rejected samples from 238,032 (0.76%) in January-April 2021 were analyzed for all reasons. Results It was determined that the use of LL significantly reduced all-cause sample rejections. In addition, a significant decrease was observed in the rates of hemolysis and clot-related rejection thanks to LL. Conclusions According to our study data, in patients with IVC with the LL device, the pressure brought by the syringe is mainly avoided, and reliable blood collection is achieved, similar to the routine vacutainer blood collection apparatus, and hemolysis- and clot-related sample rejections are largely prevented.

CSCE-Net: Channel-Spatial Contextual Enhancement Network for Robust Point Cloud Registration

Seeking reliable correspondences between two scenes is crucial for solving feature-based point cloud registration tasks. In this paper, we propose a novel outlier rejection network, called Channel-Spatial Contextual Enhancement Network (CSCE-Net), to obtain rich contextual information on correspondences, which can effectively remove outliers and improve the accuracy of point cloud registration. To be specific, we design a novel channel-spatial contextual (CSC) block, which is mainly composed of the Channel-Spatial Attention (CSA) layer and the Nonlocal Channel-Spatial Attention (Nonlocal CSA) layer. The CSC block is able to obtain more reliable contextual information, in which the CSA layer can selectively aggregate the mutual information between the channel and spatial dimensions. The Nonlocal CSA layer can compute feature similarity and spatial consistency for each correspondence, and the CSA layer and Nonlocal CSA layer can support each other. In addition, to improve the distinguishing ability between inliers and outliers, we present an advanced seed selection mechanism to select more dependable initial correspondences. Extensive experiments demonstrate that CSCE-Net outperforms state-of-the-art methods for outlier rejection and pose estimation tasks on public datasets with varying 3D local descriptors. In addition, the network parameters of CSCE-Net are reduced from 1.05M to 0.56M compared to the recently learning-based outlier rejection method PointDSC.

Identification of Nonlinear State-Space Systems With Skewed Measurement Noises

In this paper, we consider the identification problem for nonlinear state-space models with skewed measurement noises. The generalized hyperbolic skew Student’s t (GHSkewt) distribution is employed to describe the skewed noises and formulate the hierarchical model of the considered system. A unified framework for estimating unknown states and model parameters is presented based on expectation-maximization (EM) algorithm, in which the forward filtering backward simulation with rejection sampling (RS-FFBSi) is employed to efficiently estimate the smoothing densities of the hidden states, and optimization method is adopted to update model parameters. One numerical study and the electro-mechanical positioning system (EMPS) are employed to verify the effectiveness of the developed approach.

Loading a relativistic Kappa distribution in particle simulations

A procedure for loading particle velocities from a relativistic kappa distribution in particle-in-cell and Monte Carlo simulations is presented. It is based on the rejection method and the beta prime distribution. The rejection part extends earlier method for the Maxwell–Jüttner distribution, and then the acceptance rate reaches ≳95%. Utilizing the generalized beta prime distributions, we successfully reproduce the relativistic kappa distribution, including the power-law tail. The derivation of the procedure, mathematical preparations, comparison with other procedures, and numerical tests are presented.

Sensitivity-based Parameter Calibration of Single- and Dual-continuum Coreflooding Simulation Models

Our study is keyed to the development of a viable framework for the stochastic characterization of coreflooding simulation models under two- and three-phase flow conditions taking place within a core sample in the presence of preferential flow of the kind that can be associated with the presence of a system of fractures. We do so considering various modeling strategies based on (spatially homogeneous or heterogeneous) single- and dual-continuum formulations of black-oil computational models and relying on a global sensitivity-driven stochastic parameter calibration. The latter is constrained through a set of data collected under a water alternating gas scenario implemented in laboratory-scale coreflooding experiments. We set up a collection of Monte Carlo (MC) numerical simulations while considering uncertainty encompassing (a) rock attributes (i.e., porosity and absolute permeability), as well as (b) fluid–fluid/ fluid–solid interactions, as reflected through characteristic parameters of relative permeability and capillary pressure formulations. Modern moment-based global sensitivity indices are evaluated on the basis of the MC model responses, with the aim of (i) quantifying sensitivity of the coreflooding simulation results to variations of the input uncertain model parameters and (ii) assessing the possibility of reducing the dimensionality of model parameter spaces. We then rest on a stochastic inverse modeling approach grounded on the acceptance–rejection sampling (ARS) algorithm to obtain probability distributions of the key model parameters (as identified through our global sensitivity analyses) conditional to the available experimental observations. The relative skill of the various candidate models to represent the system behavior is quantified upon relying on the deviance information criterion. Our findings reveal that amongst all tested models, a dual-continuum formulation provides the best performance considering the experimental observations available. Only a few of the parameters embedded in the dual-continuum formulation are identified as major elements significantly affecting the prediction (and associated uncertainty) of model outputs, petrophysical attributes and relative permeability model parameters having a stronger effect than parameters related to capillary pressure.

High Throughput Implementation of Post-Quantum Key Encapsulation and Decapsulation on GPU for Internet of Things Applications

Internet of Things (IoT) sensor nodes are placed ubiquitously to collect information, which is then vulnerable to malicious attacks. For instance, adversaries can perform side channel attack on the sensor nodes to recover the symmetric key for encrypting IoT data. Refreshing the symmetric key frequently can reduce the risk of compromised keys. However, the number of sensor nodes connected to the gateway and cloud server is massive. Refreshed symmetric keys need to be sent to gateway devices and cloud server frequently with a secure key encapsulation mechanism (KEM), which is time-consuming. In this article, novel and efficient implementation techniques are proposed to accelerate Kyber, a post-quantum KEM, on a Graphics Processing Unit (GPU). Fully parallel implementation of number theoretic transform (NTT) with combined levels is presented, which is 2.65× faster than state-of-the-art result on a GPU. Other proposed techniques include parallel rejection sampling, central binomial distribution with coalesced memory access and parallel fine-grain AES-256. These techniques enable high throughput performance with 162760 encapsulations/second and 107631 decapsulations/second on an RTX2060 GPU. This is also the first fine grain implementation of post-quantum KEM (Kyber) on a GPU, which can be used to offer key encapsulation/decapsulation as a service to reduce the burden on IoT systems.

Investigation of Discrepant ABO Blood Grouping Results from an Autoanalyzer.

A paucity of studies evaluating failed cases of ABO grouping using autoanalyzers exists. We investigated autoanalyzer rejected cases, including serologically suspicious ABO subgroups and discrepant ABO blood grouping results from Erytra Eflexis (Grifols, Spain), to demonstrate efficient use of autoanalyzers for ABO grouping. Samples requested for ABO grouping throughout 2020 were tested using two Eflexis instruments and standard ABO RhD and reverse grouping cards. Neonatal cards were not used. When necessary, a conventional tube technique (TUBE) was used to resolve rejected/discrepant Eflexis ABO grouping results. The overall sample rejection rate (RR) was 3.2% (628/19,466), 1.3% of which were due to various error flags and 1.9% for discrepant results. Cases from neonates ≤1 year old accounted for 35.3% of the rejected cases based on Eflexis results. The ABO groups with the highest and lowest RR (excluding neonates) were A and O, respectively. The 628 samples resulted in 682 rejections, which were frequently associated with reverse grouping, including 28.4% against A1 and 54.5% for B red cells. Among 14 serologically weakened A and/or B blood groups, six A2BW and two ABw, which had been missed by Eflexis, were detected using TUBE and our follow-up laboratory criteria. The ABO group and a proportion of neonatal samples influenced the RR due to weak reverse grouping reactivity, especially toward B red cells. Confirmatory ABO grouping by TUBE in a new patient and/or extra rejection criteria for forward grouping are needed to detect cis-AB, which is relatively common in Korea.

Efficient subsampling of realistic images from GANs conditional on a class or a continuous variable

To improve image quality, subsampling or refining images generated from (unconditional) generative adversarial networks (GANs) has received much attention recently. These methods are less effective or inefficient, however, for conditional GANs (cGANs) that either condition on a class (i.e., class-conditional GANs) or a continuous variable (i.e., continuous cGANs or CcGANs). Thus, we introduce a novel subsampling scheme for cGANs: conditional density ratio rejection sampling (cDR-RS). Specifically, cDR-RS comprises an improved feature extraction mechanism and a conditional Softplus loss (cSP). We also derive an error bound for the density ratio model trained with the cSP loss. Fake images are accepted or rejected based on the improved estimated conditional density ratio. An additional filtering scheme further increases fake images’ label consistency without losing diversity when sampling from CcGANs. We extensively test the effectiveness and efficiency of cDR-RS in sampling from both class-conditional GANs and CcGANs on six benchmark datasets. Experimental results demonstrate that the proposed method can achieve state-of-the-art performances in subsampling both types of conditional GANs.

Endowing data-driven models with rejection ability: Out-of-distribution detection and confidence estimation for black-box models of building energy systems

As one of the most popular modeling methods, data-driven methods would fail easily when the online data are outside the scope of training data. To tackle this problem, this paper proposed a novel concept named as the rejection ability of data-driven models. Its main idea is rejecting the unreliable predictions based on the threshold which can be either calculated from out-of-distribution detection model or the confidence score. To compare with traditional data-driven models, three evaluation metrics are derived from the rejection table to quantify model performance. The usefulness of rejection method is validated through the fault detection and diagnosis problem of chillers. The ASHARE RP-1043 dataset is adopted to conduct the data experiments where the in-distribution performance and out-of-distribution performance of three task models, including RF, ET and ANN, and their combinations with four rejection methods, including AE-MSE, AE-MAE, Entropy and Max probability, are comprehensively investigated. The experimental results indicate that the model performance improves for a large margin with the introduction of rejection method. The optimal combination is ET-Entropy model, where its threshold selection is discussed for different application scenarios. The proposed rejection methods might provide a solid foundation for the real-world application of data-driven models.

An adaptive rejection sampler for sampling from the Wiener diffusion model.

The Wiener diffusion model with two absorbing boundaries is one of the most frequently applied models for jointly modeling responses and response latencies in psychological research. We consider four methods for sampling from the model with and without variability in drift rate, starting point, and non-decision time: Inverse transform sampling, rejection sampling, and two new methods based on adaptive rejection sampling (ARS). We implement these four methods in an R package, validate the methods, and compare their sampling speed in different settings. All four implemented methods provide samples that follow the intended distributions. The ARS-based methods, however, outperform the other methods in sampling speed as the requested sample size increases. We provide guidelines for when using ARS is more efficient than using traditional methods and vice versa.

A modified walk‐on‐sphere method for high dimensional fractional Poisson equation

AbstractWe develop walk‐on‐sphere method for fractional Poisson equations with Dirichilet boundary conditions in high dimensions. The walk‐on‐sphere method is based on probabilistic representation of the fractional Poisson equation. We propose efficient quadrature rules to evaluate integral representation in the ball and apply rejection sampling method to drawing from the computed probabilities in general domains. Moreover, we provide an estimate of the number of walks in the mean value for the method when the domain is a ball. We show that the number of walks is increasing in the fractional order and the distance of the starting point to the origin. We also give the relationship between the Green function of fractional Laplace equation and that of the classical Laplace equation. Numerical results for problems in 2–10 dimensions verify our theory and the efficiency of the modified walk‐on‐sphere method.

An Improved RANSAC Outlier Rejection Method for UAV-Derived Point Cloud

A common problem with matching algorithms, in photogrammetry and computer vision, is the imperfection of finding all correct corresponding points, so-called inliers, and, thus, resulting in incorrect or mismatched points, so-called outliers. Many algorithms, including the well-known randomized random sample consensus (RANSAC)-based matching, have been developed focusing on the reduction of outliers. RANSAC-based methods, however, have limitations such as increased false positive rates of outliers, and, consequently resulting in fewer inliers, an unnecessary high number of iterations, and high computational time. Such deficiencies possibly result from the random sampling process, the presence of noise, and incorrect assumptions of the initial values. This paper proposes a modified version of RANSAC-based methods, called Empowered Locally Iterative SAmple Consensus (ELISAC). ELISAC improves RANSAC by utilizing three basic modifications individually or in combination. These three modifications are (a) to increase the stability and number of inliers using two Locally Iterative Least Squares (LILS) loops (Basic LILS and Aggregated-LILS), based on the new inliers in each loop, (b) to improve the convergence rate and consequently reduce the number of iterations using a similarity termination criterion, and (c) to remove any possible outliers at the end of the processing loop and increase the reliability of results using a post-processing procedure. In order to validate our proposed method, a comprehensive experimental analysis has been done on two datasets. The first dataset contains the commonly-used computer vision image pairs on which the state-of-the-art RANSAC-based methods have been evaluated. The second dataset image pairs were captured by a drone over a forested area with various rotations, scales, and baselines (from short to wide). The results show that ELISAC finds more inliers with a faster speed (lower computational time) and lower error (outlier) rates compared to M-estimator SAmple Consensus (MSAC). This makes ELISAC an effective approach for image matching and, consequently, for 3D information extraction of very high and super high-resolution imagery acquired by space-borne, airborne, or UAV sensors. In particular, for applications such as forest 3D modeling and tree height estimations where standard matching algorithms are problematic due to spectral and textural similarity of objects (e.g., trees) on image pairs, ELISAC can significantly outperform the standard matching algorithms.