Additive Bias Research Articles

Point spread function errors for weak lensing – density cross-correlations

Aims. Calibrating the point spread function (PSF) is a fundamental part of weak gravitational lensing analyses. Even with corrected galaxy images, imperfect calibrations can introduce biases. We propose an analytical framework for quantifying PSF-induced systematics as diagnostics for cross-correlation measurements of weak lensing with density tracers; for example, galaxy-galaxy lensing. We show how those systematics propagate to physical parameters of the density tracers. Those diagnostics only require a shape catalog of PSF stars and foreground galaxy positions. Methods. We considered the PSF-induced multiplicative bias, and introduced three second-order statistics as additive biases. We computed both biases for the weak-lensing derived halo mass of spectroscopic foreground galaxy samples; in particular, their effect on the tangential shear and fit halo mass as a function of stellar mass. In addition, we assessed their impact on the recently published black-hole – halo-mass relation for type I active galactic nuclei (AGNs). Results. Using weak-lensing catalogs from the Ultraviolet Near Infrared Optical Northern Survey (UNIONS) and the Dark Energy Survey (DES), we find the multiplicative biases in the tangential shear to be less than 0.5%. No correlations between additive bias and galaxy properties of the foreground sample are detected. The combined PSF systematics affect low-mass galaxies and small angular scales; halo mass estimates can be biased by up to 18% for a sample of central galaxies in the stellar mass range of 9.0 ≤ log M*/M⊙ < 9.5. Conclusions. The PSF-induced multiplicative bias is a subdominant contribution to current studies of weak-lensing – density cross-correlations, but might become significant for upcoming stage IV surveys. For samples with a low tangential shear, additive PSF systematics can induce a significant bias on derived properties such as the halo mass.

Adaptive Fault-Tolerant Control of Mobile Robots with Fractional-Order Exponential Super-Twisting Sliding Mode

Industrial mobile robots easily experience actuator loss of some effectiveness and additive bias faults due to the working scenarios, resulting in unexpected performance degradation. This article proposes a novel adaptive fault-tolerant control (FTC) strategy for nonholonomic mobile robot systems subject to simultaneous actuator lock-in-place (LIP) and partial loss-of-effectiveness (LOE) faults. First, a nominal fractional-order sliding mode controller based on the designed exponential super-twisting reaching law is investigated to reduce the reaching phase time and eliminate the chattering. To address the time-varying LIP faults and uncertainties, a novel barrier function (BF)-based gain is explored to assist the super-twisting law. An estimator is designed to estimate the lower bound of the time-varying partial LOE fault coefficients, thus without requiring the boundary information of faults that is commonly requested in traditional FTC schemes. Combined with the nominal controller clubbed with BF and estimator-based LOE fault compensation term, the fault-tolerant controller is finally constructed. The proposed FTC scheme achieves fast convergence and the sliding variables can be confined in a predetermined neighborhood of the sliding manifold under actuator faults. The results show that the proposed controller has superior tracking performance under faulty conditions compared with other state-of-the-art adaptive FTC approaches.

Euclid preparation. LIII. LensMC, weak lensing cosmic shear measurement with forward modelling and Markov Chain Monte Carlo sampling

is a weak lensing shear measurement method developed for and Stage-IV surveys. It is based on forward modelling in order to deal with convolution by a point spread function (PSF) with comparable size to many galaxies, sampling the posterior distribution of galaxy parameters via Markov chain Monte Carlo, and marginalisation over nuisance parameters for each of the 1.5 billion galaxies observed by We quantified the scientific performance through high-fidelity images based on the Flagship simulations and emulation of the VIS images, realistic clustering with a mean surface number density of $ arcmin^ for galaxies, and $ arcmin^ for stars, and a diffraction-limited chromatic PSF with a full width at half maximum of $ $ and spatial variation across the field of view. measured objects with a density of $ arcmin^ in $4500 deg^2 $. The total shear bias was broken down into measurement (our main focus here) and selection effects (which will be addressed in future work). We found measurement multiplicative and additive biases of $m_1=(-3.6 $, $m_2=(-4.3 $, $c_1=(-1.78 $, and $c_2=(0.09 $; a large detection bias with a multiplicative component of $1.2 $ and an additive component of $-3 $; and a measurement PSF leakage of $ $ and $ $. When model bias is suppressed, the obtained measurement biases are close to requirement and largely dominated by undetected faint galaxies ($-5 $). Although significant, model bias will be straightforward to calibrate given its weak sensitivity on galaxy morphology parameters. is publicly available at

Iterative Removal of G-PCC Attribute Compression Artifacts Based on a Graph Neural Network

As a compression standard, Geometry-based Point Cloud Compression (G-PCC) can effectively reduce data by compressing both geometric and attribute information. Even so, due to coding errors and data loss, point clouds (PCs) still face distortion challenges, such as the encoding of attribute information may lead to spatial detail loss and visible artifacts, which negatively impact visual quality. To address these challenges, this paper proposes an iterative removal method for attribute compression artifacts based on a graph neural network. First, the geometric coordinates of the PCs are used to construct a graph that accurately reflects the spatial structure, with the PC attributes treated as signals on the graph’s vertices. Adaptive graph convolution is then employed to dynamically focus on the areas most affected by compression, while a bi-branch attention block is used to restore high-frequency details. To maintain overall visual quality, a spatial consistency mechanism is applied to the recovered PCs. Additionally, an iterative strategy is introduced to correct systematic distortions, such as additive bias, introduced during compression. The experimental results demonstrate that the proposed method produces finer and more realistic visual details, compared to state-of-the-art techniques for PC attribute compression artifact removal. Furthermore, the proposed method significantly reduces the network runtime, enhancing processing efficiency.

A Bayesian bias-adjusted random-effects model for synthesizing evidence from randomized controlled trials and nonrandomized studies of interventions.

An important consideration when combining RCTs and NRSIs is how to address their potential biases in the pooled estimates. This study aimed to propose a Bayesian bias-adjusted random effects model for the synthesis of evidence from RCTs and NRSIs. We present a Bayesian bias-adjusted random effects model based on power prior method, which combines the likelihood contribution of the NRSIs, raised to the power parameter of alpha, with the likelihood of the RCT data, modeled with an additive bias. The method was illustrated using a meta-analysis on the association between low-dose methotrexate exposure and melanoma. We also combined RCTs and NRSIs using the naïve data synthesis. The results including only RCTs has a posterior median and 95% credible interval (CrI) of 1.18 (0.31-4.04), the posterior probability of any harm (>1.0) and a meaningful association (>1.15) were 0.61 and 0.52, respectively. The posterior median and 95% CrI based on the naïve data synthesis resulted in 1.17 (0.96-1.47), and the posterior probability of any harm and a meaningful association were 0.96 and 0.60, respectively. For the Bayesian bias-adjusted analysis, the median OR was 1.16 (95% CrI: 0.83-1.71), and the posterior probabilities of any and a meaningful clinical association were 0.88 and 0.53, respectively. The results indicated that integrating NRSIs into meta-analysis could increase the certainty of the body of evidence. However, directly combining RCTs and NRSIs in the same meta-analysis without distinction may lead to misleading conclusions.

A variational Bayesian based robust filter for unknown measurement bias and inaccurate noise statistics

In many practical fields, the unknown time-varying measurement biases (additive and multiplicative bias) and heavy-tailed measurement noise caused by some unpredictable anomalous behaviors may degrade the performance of conventional Kalman filter seriously. To solve the state estimation problem of systems with time-varying measurement biases and heavy-tailed measurement noise, this paper proposes a new variational Bayesian (VB) based robust filter. Firstly, the non-Gaussian measurement likelihood probability density function (ML-PDF) with multiplicative and additive measurement bias is built. Then, the conjugate prior distributions for unknown bias and noise scale parameters are selected, and the VB method is utilized to jointly infer the system state, unknown measurement biases and inaccurate measurement noise covariance matrix. Finally, a VB based robust filter is derived and its effectiveness is verified by the numerical simulations.

Unveiling the Associative Mechanisms Underlying the Additive Bias: Using an Implicit Association Test to Gain Insight into People's Preference for Additive Actions

ABSTRACTWhen faced with the need to transform an object, idea, or situation, people have a tendency to favor adding new components rather than removing existing ones. This is called the additive bias. Previous research, along with historical and anecdotal examples, shows that this bias may significantly reduce problem‐solving abilities and have a detrimental impact on the innovation process. In this study, our objective was to develop a novel tool, the additive bias implicit association test (ad‐IAT), to investigate the reasons underlying people's preference for additive actions. By using this tool, we empirically demonstrated that people displayed an inherent tendency to assign a positive valence to additive concepts and to perceive additive actions as safer and more functional than subtractive concepts. Importantly, we also found that implicit preference for addition resulted in participants favoring additive actions while neglecting subtractive alternatives when engaged in a problem‐solving task. Collectively, our series of experiments substantiated the effectiveness of our ad‐IAT in uncovering and quantifying the additive bias. This, in turn, provided a deeper comprehension of the underlying factors contributing to the bias and its impact on people's behavior.

Minimum-variance-based outlier detection method using forward-search model error in geodetic networks

Abstract. Geodetic observations are crucial for monitoring landslides, crustal movements, and volcanic activity. They are often integrated with data from interdisciplinary studies, including paleo-seismological, geological, and interferometric synthetic aperture radar observations, to analyze earthquake hazards. However, outliers in geodetic observations can significantly impact the accuracy of estimation results if not reliably identified. Therefore, assessing the outlier detection model's reliability is imperative to ensure accurate interpretations. Conventional and robust methods are based on the additive bias model, which may cause type-I and type-II errors. However, outliers can be regarded as additional unknown parameters in the Gauss–Markov model. It is based on modeling the outliers as unknown parameters, considering as many combinations as possible of outliers selected from the observation set. In addition, this method is expected to be more effective than conventional methods as it is based on the principle of minimal variance and eliminates the interdependence of decisions made in iterations. The primary purpose of this study is to seek an efficient outlier detection model in the geodetic networks. The efficiency of the proposed model was measured and compared with the robust and conventional methods by the mean success rate (MSR) indicator of different types and magnitudes of outliers. Thereby, this model enhances the MSR by almost 40 %–45 % compared to the Baarda and Danish (with the variance unknown case) method for multiple outliers. Besides, the proposed model is 20 %–30 % more successful than the others in the low-controllability observations of the leveling network.

Agreement Between a Colorimetric Assay and Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry for Quantifying Paracetamol Plasma Concentrations.

Special populations, like geriatric patients, experience altered paracetamol pharmacokinetics (PK), complicating pain management. More PK research is essential to optimize paracetamol (acetaminophen) dosing. Yet, the reference method ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) is not readily available. Therefore, we aimed to evaluate the agreement between UPLC-MS/MS and the more accessible colorimetric Roche acetaminophen (ACETA) assay in quantifying paracetamol plasma concentrations, to facilitate PK studies and therapeutic drug monitoring for pain management. Patient data and plasma samples were obtained from a prospective study including geriatric patients admitted to the geriatric wards. ACETA and UPLC-MS/MS assays were performed in two separate laboratories. Bland-Altman plot and Passing-Bablok regression were used to assess agreement. Accuracy was evaluated using the McNemar test for a threshold value of 10mg/L. Population PK modeling was employed to bridge PK data obtained from both methods (NONMEM 7.5). A total of 242 plasma sample pairs were available from 40 geriatric patients (age range, 80-95years). Paracetamol plasma concentrations from ACETA (median 9.8 [interquartile range 6.1-14.4] mg/L) and UPLC-MS/MS (9.5 [6.2-14.8] mg/L) did not differ significantly (P > 0.05). No significant proportional nor additive bias was observed between both assay methods. The classification accuracy (at threshold 10mg/L) was 85% (P = 0.414). The conversion factor between ACETA and UPLC-MS/MS was estimated at 1.06 (relative standard error 5%), yet with a 13.4% (relative standard error 23%) interindividual variability. ACETA assay showed no systematic bias in comparison with the UPLC-MS/MS assay in determining paracetamol exposure in geriatric blood samples despite the imprecision.

Impact of PSF misestimation and galaxy population bias on precision shear measurement using a CNN

ABSTRACT Weak gravitational lensing of distant galaxies provides a powerful probe of dark energy. The aim of this study is to investigate the application of convolutional neural networks (CNNs) to precision shear estimation. In particular, using a shallow CNN, we explore the impact of point spread function (PSF) misestimation and ‘galaxy population bias’ (including ‘distribution bias’ and ‘morphology bias’), focusing on the accuracy requirements of next generation surveys. We simulate a population of noisy disc and elliptical galaxies and adopt a PSF that is representative of a Euclid-like survey. We quantify the accuracy achieved by the CNN, assuming a linear relationship between the estimated and true shears and measure the multiplicative (m) and additive (c) biases. We make use of an unconventional loss function to mitigate the effects of noise bias and measure m and c when we use either: (i) an incorrect galaxy ellipticity distribution or size–magnitude relation, or the wrong ratio of morphological types, to describe the population of galaxies (distribution bias); (ii) an incorrect galaxy light profile (morphology bias); or (iii) a PSF with size or ellipticity offset from its true value (PSF misestimation). We compare our results to the Euclid requirements on the knowledge of the PSF model shape and size. Finally, we outline further work to build on the promising potential of CNNs in precision shear estimation.

Teacher Stereotypes and Teacher Expectations at the Intersection of Student Gender and Socioeconomic Status

Abstract: Gender and family socioeconomic status (SES) are central dimensions of educational inequality and may interact in shaping inequality. This study addresses teacher expectations and stereotypes that possibly contribute to intersectional inequality. The study relies on two samples of teachers and students in German primary schools (sample 1: NTeachers=69, 94 % female, NStudents= 1,049 (German language)/1,027 (mathematics), 48 % female; sample 2: NTeachers= 698 (German language)/614 (mathematics), 94 % female; NStudents= 4,732 (German language)/4,117 (mathematics), 51 % female). Two-level regression analyses revealed additive gender and socioeconomic bias in teacher expectations in German language and mathematics but no intersectional bias (i. e., constant gender bias along family SES and similar socioeconomic bias for girls and boys). Further, teachers with more traditional gender stereotypes showed stronger gender bias, while SES-related stereotypes were unrelated to teacher expectations. We discuss how additive teacher expectation biases relate to the broader concept of intersectionality, potentially shaping unique educational experiences at the intersection of gender and SES.

An attention-mechanism-based deep fusion model for improving quantitative precipitation estimation in a sparsely-gauged basin

Improving quantitative precipitation estimation (QPE) in sparsely-gauged basins via merging remote sensing precipitation data and rain gauge data still remains a challenge since most existing merging models degrade when the rain gauge data become limited. To address the challenge, we propose an attention-mechanism-based deep learning model, Multi-Level Transformer Fusion (MLTF) model, which allows to capture the inner interactions among the multi-source input data (TRMM 3B42 V7 data, GridSat-B1 data, and DEM) in the sparsely-gauged basin. Taking the source region of the Yellow River basin (SRYRB) as a representative case study, we demonstrate the performance of the proposed model and compare it with conventional methods (e.g., Multiplicative Bias Correction, Additive Bias Removal, linear regression, and Kriging) and deep-learning-based models (CNN, CNN-LSTM). Results indicate that the merged precipitation in SRYRB produced by the MLTF model exhibits a RMSE reduction of 27.1 %, MAE decrease of 11.2 %, and CC increase of 19.2 % in comparison to the original TRMM data, outperforming all the selected comparative methods. Finally, an improved daily precipitation dataset during 1999–2019 with a spatial resolution of 0.05° is produced for the study area. This study proposes a new method for QPE improvement in a sparsely-gauged basin, which would provide valuable data support for regional hydrological study and water resources management.

Traceability ensuring by organizing the Metrological Measurements Network

A new scheme for the traceability ensuring, called the Metrological Measurements Network, was proposed. It is not an alternative to the existing scheme but is only another form of implementation of the traceability scheme. It is about the main principle of construction of the Metrological Measurements Network and its advantages over the existing scheme. The main reason is that each laboratory calibrates its own object for measurement using its measurement standard and sends it for calibration to a laboratory-participant and, almost simultaneously, receives a similar object from another laboratory-participant that calibrated it. If each participant, at the same time as the others, makes at least four such calibrations and transfers between laboratories-participants, it will form a common and very precise Metrological Measurement Network in a very short time. It can cover hundreds and even thousands of laboratories in a short period of time. The joint processing of a large number of such measurements will help to define the additive and/or multiplicative biases of each measurement standards. Moreover, the reference conditions are imposed that the sum of additive and, separately, the sum of multiplicative biases for all measurement standards is equal to zero regardless of the number of network participants.

On the impact of the galaxy window function on cosmological parameter estimation

ABSTRACT One important source of systematics in galaxy redshift surveys comes from the estimation of the galaxy window function. Up until now, the impact of the uncertainty in estimating the galaxy window function on parameter inference has not been properly studied. In this paper, we show that the uncertainty and the bias in estimating the galaxy window function will be salient for ongoing and next-generation galaxy surveys using a simulation-based approach. With a specific case study of cross-correlating emission-line galaxies from the DESI Legacy Imaging Surveys and the Planck cosmic microwave background lensing map, we show that neural network-based regression approaches to modelling the window function are superior in comparison to linear regression-based models. We additionally show that the definition of the galaxy overdensity estimator can impact the overall signal-to-noise of observed power spectra. Finally, we show that the additive biases coming from the window functions can significantly bias the modes of the inferred parameters and also degrade their precision. Thus, a careful understanding of the window functions will be essential to conduct cosmological experiments.

Revealing the influence of bias in a letter acuity identification task: A noisy template model

In clinical testing of visual acuity, it is often assumed that performance reflects sensory abilities and observers do not exhibit strong biases for or against specific letters, but this assumption has not been extensively tested. We re-analyzed single-letter identification data as a function of letter size, spanning the resolution threshold, for 10 Sloan letters at central and paracentral visual field locations. Individual observers showed consistent letter biases across letter sizes. Preferred letters were named much more often and others less often than expected (group averages ranged from 4% to 20% across letters, where the unbiased rate was 10%). In the framework of signal detection theory, we devised a noisy template model to distinguish biases from differences in sensitivity. When bias varied across letter templates the model fitted very well - much better than when sensitivity varied without bias. The best model combined both, having substantial biases and small variations in sensitivity across letters. The over- and under-calling decreased at larger letter sizes, but this was well-predicted by template responses that had the same additive bias for all letter sizes: with stronger inputs (larger letters) there was less opportunity for bias to influence which template gave the biggest response. The neural basis for such letter bias is not known, but a plausible candidate is the letter-recognition machinery of the left temporal lobe. Future work could assess whether such biases affect clinical measures of visual performance. Our analyses so far suggest very small effects in most settings.

Image Segmentation Based on the Hybrid Bias Field Correction

Image segmentation is the foundation for analyzing and understanding high-level images. How to effectively segment an intensity inhomogeneous image into several meaningful regions in terms of human visual perception and ensure that the segmented regions are consistent at different resolutions is still a very challenging task. In order to describe the structure information of the intensity inhomogeneous efficiently, this paper proposes a novel hybrid bias field correction model by decoupling the multiplicative bias field and the additive bias field. These kinds of bias fields are assumed to be smooth, so can employ the Sobolev space W1,2 to feature them and use a constraint to the multiplicative bias field. Since the proposed model is a constrained optimization problem, we use the Lagrangian multiplier method to transform it into an unconstrained optimization problem, and then the alternating direction method can be used to solve it. In addition, we also discuss some mathematical properties of our proposed model and algorithm. Numerical experiments on the natural images and the medical images demonstrate performance improvement over several state-of-the-art models.

Microaneurysms detection in retinal fundus images based on shape constraint with region-context features

Microaneurysms (MAs) are the earliest lesions diabetic retinopathy (DR), and the accurate MA detection can assist in early diagnosis of diabetes. Many effective detection techniques have been proposed recently, the interference of blood vessels and blurry boundaries of MAs reduce the performance of these methods. To address these problems, this paper proposes an accurate MA detection method. In the candidate detection stage, a shape suppression filter is proposed to remove strip-shaped blood vessels and retain the dark blobs. In order to enhance blurry boundaries of MAs, we apply the additive bias correction level set to locate circular regions. In the next stage of feature extraction, a new set of features based on gray level co-occurrence matrix (GLCM) is extracted for each candidate to discriminate the MAs from non-MAs candidates using the random undersampling boosting (RUSBoost) classifier. We test our method on four public datasets, resulting in the optimal performance to the existing methods. It achieves average sensitivity values of 0.672, 0.721, 0.602 and 0.735, respectively.

Impact of point spread function higher moments error on weak gravitational lensing – II. A comprehensive study

ABSTRACT Weak lensing is one of the most powerful probes for dark matter and dark energy science, although it faces increasing challenges in controlling systematic uncertainties as the statistical errors become smaller. The point spread function (PSF) needs to be precisely modeled to avoid systematic error on the weak lensing measurements. The weak lensing biases induced by errors in the PSF model second moments, i.e. its size and shape, are well-studied. However, Zhang et al. showed that errors in the higher moments of the PSF may also be a significant source of systematics for upcoming weak lensing surveys. Therefore, this work comprehensively investigate the modelling quality of PSF moments from the 3rd to 6th order, and propagate the PSFEx higher moments modelling error in the HSC survey data set to the weak lensing shear–shear correlation functions and their cosmological analyses. The overall multiplicative shear bias associated with errors in PSF higher moments can cause an ∼0.1σ shift on the cosmological parameters for LSST Y10, while the associated additive biases can induce 1σ uncertainties in cosmology parameter inference for LSST Y10, if not accounted. We compare the PSFEx model with PSF in Full FOV, and find similar performance in modelling the PSF higher moments. We conclude that PSF higher moment errors of the future PSF models should be reduced from those in current methods, otherwise needed to be explicitly modeled in the weak lensing analysis.

ShapePipe: A new shape measurement pipeline and weak-lensing application to UNIONS/CFIS data

Context. The Ultraviolet Near-Infrared Optical Northern Survey (UNIONS) is an ongoing collaboration that will provide the largest deep photometric survey of the northern sky in four optical bands to date. As part of this collaboration, the Canada-France Imaging Survey (CFIS) is observing r-band data with an average seeing of 0.65 arcsec, which is complete to magnitude 24.5 and thus ideal for weak-lensing studies. Aims. We perform the first weak-lensing analysis of CFIS r-band data over an area spanning 1700 deg2 of the sky. We create a catalogue with measured shapes for 40 million galaxies, corresponding to an effective density of 6.8 galaxies per square arcminute, and demonstrate a low level of systematic biases. This work serves as the basis for further cosmological studies that will use the full UNIONS survey of 4800 deg2 when completed. Methods. Here we present SHAPEPIPE, a newly developed weak-lensing pipeline. This pipeline makes use of state-of-the-art methods such as NGMIX for accurate galaxy shape measurement. Shear calibration is performed with metacalibration. We carry out extensive validation tests on the point spread function (PSF) and on the galaxy shapes. In addition, we create realistic image simulations to validate the estimated shear. Results. We quantify the PSF model accuracy and show that the level of systematics is low as measured by the PSF residuals. Their effect on the shear two-point correlation function is sub-dominant compared to the cosmological contribution on angular scales < 100′. The additive shear bias is below 5 × 10−4, and the residual multiplicative shear bias is at most 10−3 as measured on image simulations. Using complete orthogonal sets of E-/B-mode integrals (COSEBIs), we show that there are no significant B-modes present in second-order shear statistics. We present convergence maps and see clear correlations of the E-mode with known cluster positions. We measure the stacked tangential shear profile around Planck clusters at a significance higher than 4σ.

Correlations of Dark Matter, Gas, and Stellar Profiles in Dark Matter Halos

Halos of similar mass and redshift exhibit a large degree of variability in their differential properties, such as dark matter, hot gas, and stellar mass density profiles. This variability is an indicator of diversity in the formation history of these dark matter halos that is reflected in the coupling of scatters about the mean relations. In this work, we show that the strength of this coupling depends on the scale at which halo profiles are measured. By analyzing the outputs of the IllustrisTNG hydrodynamical cosmological simulations, we report the radial- and mass-dependent couplings between the dark matter, hot gas, and stellar mass radial density profiles utilizing the population diversity in dark matter halos. We find that for the same mass halos, the scatters in the density of baryons and dark matter are strongly coupled at large scales (r > R 200), but the coupling between gas and dark matter density profiles fades near the core of halos (r < 0.3R 200). We then show that the correlation between halo profile and integrated quantities induces a radius-dependent additive bias in the profile observables of halos when halos are selected on properties other than their mass. We discuss the impact of this effect on cluster abundance and cross-correlation cosmology with multiwavelength cosmological surveys.