Correction Framework Research Articles

Feasibility of bone marrow edema detection using dual-energy cone-beam computed tomography.

Dual-energy (DE) detection of bone marrow edema (BME) would be a valuable new diagnostic capability for the emerging orthopedic cone-beam computed tomography (CBCT) systems. However, this imaging task is inherently challenging because of the narrow energy separation between water (edematous fluid) and fat (health yellow marrow), requiring precise artifact correction and dedicated material decomposition approaches. We investigate the feasibility of BME assessment using kV-switching DE CBCT with a comprehensive CBCT artifact correction framework and a two-stage projection- and image-domain three-material decomposition algorithm. DE CBCT projections of quantitative BME phantoms (water containers 100-165mm in size with inserts presenting various degrees of edema) and an animal cadaver model of BME were acquired on a CBCT test bench emulating the standard wrist imaging configuration of a Multitom Rax twin robotic x-ray system. The slow kV-switching scan protocol involved a 60kV low energy (LE) beam and a 120kV high energy (HE) beam switched every 0.5° over a 200° angular span. The DE CBCT data preprocessing and artifact correction framework consisted of (i) projection interpolation onto matched LE and HE projections views, (ii) lag and glare deconvolutions, and (iii) efficient Monte Carlo (MC)-based scatter correction. Virtual non-calcium (VNCa) images for BME detection were then generated by projection-domain decomposition into an Aluminium (Al) and polyethylene basis set (to remove beam hardening) followed by three-material image-domain decomposition into water, Ca, and fat. Feasibility of BME detection was quantified in terms of VNCa image contrast and receiver operating characteristic (ROC) curves. Robustness to object size, position in the field of view (FOV) and beam collimation (varied 20-160mm) was investigated. The MC-based scatter correction delivered>69% reduction of cupping artifacts for moderate to wide collimations (>80mm beam width), which was essential to achieve accurate DE material decomposition. In a forearm-sized object, a 20% increase in water concentration (edema) of a trabecular bone-mimicking mixture presented as ∼15 HU VNCa contrast using 80-160mm beam collimations. The variability with respect to object position in the FOV was modest (<15% coefficient of variation). The areas under the ROC curve were>0.9. A femur-sized object presented a somewhat more challenging task, resulting in increased sensitivity to object positioning at 160mm collimation. In animal cadaver specimens, areas of VNCa enhancement consistent with BME were observed in DE CBCT images in regions of MRI-confirmed edema. Our results indicate that the proposed artifact correction and material decomposition pipeline can overcome the challenges of scatter and limited spectral separation to achieve relatively accurate and sensitive BME detection in DE CBCT. This study provides an important baseline for clinical translation of musculoskeletal DE CBCT to quantitative, point-of-care bone health assessment.

Deep-BCSI: A deep learning-based framework for bias correction and spatial imputation of PM2.5 concentrations in South Korea

In this study, we introduce a deep learning-based framework, Deep-BCSI, which leverages Convolutional Neural Networks (CNN) for bias correction and Partial Convolutional Neural Networks (PConv) for spatial imputation. It is designed to enhance the accuracy of PM2.5 concentration forecasts over South Korea, at both station and grid levels, up to three days in advance. The framework utilizes 72-h simulations of 10 variables from the Community Multiscale Air Quality (CMAQ) model, 31 variables from the Weather Research and Forecasting (WRF) model, and 6 variables from the previous day's ground-based in-situ observations. The CNN and PConv models' training time spans from 2016 to 2019. The Deep-BCSI framework was evaluated in 2021. From Day 1 to Day 3, the CNN model demonstrated significant efficiency in bias correction, yielding higher Index of Agreement (IOA) values (0.71–0.80) compared to the CMAQ's (0.65–0.68) across 402 stations in South Korea. In metropolitan areas such as Seoul, Busan, Incheon, and Daegu, the Root Mean Squared Error (RMSE) was reduced by 25% - 41%. Post-bias correction, spatial imputation using the PConv model provided accurate grid-based forecasts of PM2.5 concentrations, particularly in the northwestern and southeastern regions of South Korea. From Day 1 to Day 3, a 10-fold spatial cross-validation reveals that the PConv model consistently yielded higher IOA values (0.71–0.79) compared to the CMAQ's (0.66–0.69). An analysis using Shapley Additive Explanations (SHAP) offered insights into the CNN model's prediction-making process, confirming it as scientifically valid and closely aligned with essential atmospheric chemistry and meteorological phenomena related to PM2.5 pollution. The Deep-BCSI presents an efficient framework for generating accurate PM2.5 concentration forecasts, particularly for urban regions. It has potential applications in operational facilities, planning, and policymaking to mitigate the hazards posed by PM2.5 pollution.

Semi-supervised integration of single-cell transcriptomics data

Batch effects in single-cell RNA-seq data pose a significant challenge for comparative analyses across samples, individuals, and conditions. Although batch effect correction methods are routinely applied, data integration often leads to overcorrection and can result in the loss of biological variability. In this work we present STACAS, a batch correction method for scRNA-seq that leverages prior knowledge on cell types to preserve biological variability upon integration. Through an open-source benchmark, we show that semi-supervised STACAS outperforms state-of-the-art unsupervised methods, as well as supervised methods such as scANVI and scGen. STACAS scales well to large datasets and is robust to incomplete and imprecise input cell type labels, which are commonly encountered in real-life integration tasks. We argue that the incorporation of prior cell type information should be a common practice in single-cell data integration, and we provide a flexible framework for semi-supervised batch effect correction.

Exploring δ13 C data compatibility in a broad δ13 C range: Reassembling δ13 C values obtained for NIST CO2 RMs 8562-8564 at different laboratories.

Stable isotope datasets obtained in different laboratories over years must be compared and combined. The strict compatibility in δ13 C at ±0.01‰ and ±0.02‰, k= 1, respectively, is required for observations of greenhouse gases CO2 and methane. That implies the long-term compatibility of calibrations realized at different laboratories. Thus far, this level of compatibility has not been demonstrated for pure CO2 in a broad δ13 C range and over years. We reassemble the δ13 C obtained in different laboratories for NIST CO2 RMs 8562-8564, which are pure CO2 gases. If the measurements are based on independent calibrations and reported on the same 17 O correction framework, δ13 C of all three RMs can be used to estimate the long-term δ13 C data compatibility. δ13 C values obtained in different laboratories for NIST CO2 RMs 8562-8564 (a range of δ13 C from ~ -3.7‰ to ~ -41.6‰) from 1998 to 2020 agree within their uncertainties. Two NIST studies (1999 and 2004) have combined data from several laboratories using different Isotope ratio mass spectrometry (IRMS) methods and somewhat different approaches. Three single-laboratory studies (2003, 2008, and 2020) have used the same type of IRMS and similar approaches. The δ13 C values from these laboratories agree within ±0.01‰, k= 1, over the entire δ13 C range and have uncertainties about two times lower than those obtained at NIST by combining data from several laboratories. The δ13 C values for NIST CO2 RMs 8562-8564 obtained in three laboratories using the same IRMS (2003, 2008, and 2020) agree within ±0.01‰, k= 1. First, this confirms the long-term stability in δ13 C of RMs 8562-8564. Second, this example of compatibility in δ13 C calibrations on pure CO2 , from -3.7‰ to -41.6‰, is relevant to stable isotope measurements of greenhouse gases CO2 and methane. Third, this confirms the high accuracy of the δ13 C(air-CO2 ) calibrations performed in 2003 and 2008 for the project Civil Aircraft for the Regular Investigation of the atmosphere Based on an Instrumented Container (CARIBIC).

MitH: A framework for Mitigating Hygroscopicity in low-cost PM sensors

Air quality estimation using low-cost sensors is a pressing issue, with meteorological factors often causing measurement discrepancies. Hygroscopicity, arising from humidity’s interaction with particulates, leads to inaccurate PM concentration readings in laser-scattering low-cost PM sensors. Common remedies involve data removal during high relative humidity or reference station calibration, but these solutions are not always practical or accurate due to the localized nature of hygroscopicity. In this paper, the authors present an adaptive correction framework that dynamically models hygroscopicity effectively mitigating humidity’s impact on particle measurements. The framework exploits historical sensors’ data, providing real-time adaptability in any context without relying on reference data, thus improving air quality estimations from low-cost sensors.

Efficient Framework for Real-Time Color Cast Correction and Dehazing Using Online Algorithms to Approximate Image Statistics

Efficient Framework for Real-Time Color Cast Correction and Dehazing Using Online Algorithms to Approximate Image Statistics

Construction of English Numerical Intelligence Text Translation Data Corpus in Colleges and Universities

Abstract Given the specialized nature of English text translation in academic settings and the frequent absence of reliable reference materials, translation processes often lack verifiable evidence, impacting both efficiency and quality. This paper addresses these challenges by first developing a basic syntactic error correction model that leverages the structural features of recurrent neural networks (RNNs) and gated recurrent unit (GRU) networks to establish a Seq2Seq syntactic error correction framework. To enhance this model, we incorporate an Attention mechanism into the Seq2Seq-based English grammar error correction model. This innovation enables the model to swiftly focus on segments most pertinent to the current context, thereby boosting operational efficiency. Subsequently, we create a college English text translation data corpus using Numerical Intelligence techniques to maintain grammatical accuracy within the corpus. Comparative analysis of the model training reveals that the Seq2Seq model with the Attention mechanism achieves an accuracy rate of 41.7%, which represents a 9.19% improvement over the basic model, underscoring its significant advantage. Furthermore, the average accuracy rate for grammatical error correction stands at 72.87%. A practical application analysis shows a minimal difference of only 0.05 points between the model’s grammar correction scores and those of human teachers. The corpus developed using this enhanced grammar error correction model scored 86 overall, outperforming other corpora. Therefore, the augmented Seq2Seq model with the Attention mechanism proves highly effective for developing English text translation corpora in collegiate environments.

When Invariant Representation Learning Meets Label Shift: Insufficiency and Theoretical Insights.

As a crucial step toward real-world learning scenarios with changing environments, dataset shift theory and invariant representation learning algorithm have been extensively studied to relax the identical distribution assumption in classical learning setting. Among the different assumptions on the essential of shifting distributions, generalized label shift (GLS) is the latest developed one which shows great potential to deal with the complex factors within the shift. In this paper, we aim to explore the limitations of current dataset shift theory and algorithm, and further provide new insights by presenting a comprehensive understanding of GLS. From theoretical aspect, two informative generalization bounds are derived, and the GLS learner are proved to be sufficiently close to optimal target model from the Bayesian perspective. The main results show the insufficiency of invariant representation learning, and prove the sufficiency and necessity of GLS correction for generalization, which provide theoretical supports and innovations for exploring generalizable model under dataset shift. From methodological aspect, we provide a unified view of existing shift correction frameworks, and propose a kernel embedding-based correction algorithm (KECA) to minimize the generalization error and achieve successful knowledge transfer. Both theoretical results and extensive experiment evaluations demonstrate the sufficiency and necessity of GLS correction for addressing dataset shift and the superiority of proposed algorithm.

Performance and usefulness evaluation of a software-based scatter correction technique for mammographic images

Although physical grids improve contrast in radiographic images by reducing scattered radiation, various artifacts such as grid shadow, moire, and cutoff result in increased patient doses. To overcome these problems, this study evaluates the applicability and usefulness of a material thickness-based scatter-correction technique for mammography. Specifically, this study aims to compare and evaluate the performance of mammography using the proposed software-based scatter correction framework and a physical grid. The proposed technique enables scatter correction based on pre-calculated parameters of a thickness-based scatter kernel at a water slab phantom and an empirical quantity of scatter components in a mammographic system. In the Monte Carlo simulation and experiment, the proposed framework displayed an intensity profile and full width at half maximum that closely approximated those seen in the physical grid. In addition, by applying the proposed framework to the ACR phantom, it was verified that all structures, including specks, were distinctly distinguished. The results demonstrate that the X-ray scatter-correction method with a software-based framework for mammography is applicable to the field of diagnostic imaging, as this approach yields image quality equivalent to that achieved with physical grids while also enabling a reduction in radiation doses for patients.

Stochastic model corrections for reduced Lotka-Volterra models exhibiting mutual, commensal, competitive, and predatory interactions.

We explore model-form error and how to correct it in systems of ordinary differential equations. In particular, we focus on the Lotka-Volterra equations, which are used broadly in fields such as ecology, biology, economics, chemistry, and physics. Accounting for every object and their complex interactions with a complete model often becomes infeasible, thereby requiring reduced models. However, reduced models may omit vital relationships, resulting in discrepancies between reduced model predictions and observations from the true system. In this work, we propose a model correction framework for decreasing such discrepancies. Specifically, we embed a stochastic enrichment operator into the reduced model's system of equations. The enrichment operator is theory-informed, calibrated with observations from the complete model, and extended to extrapolative combinations of parameters and initial conditions. The complete model involves N species, while the reduced and enriched models only track M<N species. Numerical results show the enriched models significantly decrease discrepancies, consistently predict equilibria, and improve the species' transient behavior.

When Fact-Checking Is Not WEIRD: Negotiating Consensus Outside Western, Educated, Industrialized, Rich, and Democratic Countries

This study unpacks the emerging framework of detection, verification, and correction of falsehoods developed by fact-checkers outside Western, Educated, Industrialized, Rich, and Democratic countries. We explore a series of semistructured interviews carried out in several languages with thirty-seven fact-checking experts from thirty-five organizations in twenty-seven countries across Africa, Asia, Latin America, and Eastern Europe. Our findings emphasize the contextual nature of the falsehoods that these professionals deal with on a daily basis, and the many strategies they employ to navigate cultural and political obstacles while strengthening social cohesion locally. We review these findings against the literature in the area and argue that the prevailing framework of fact-checking, where misinformation and disinformation are reduced to individual and behavioral problems, underplays the social and historical dimensions driving disinformation and propaganda.

Omnidirectional-Sensor-System-Based Texture Noise Correction in Large-Scale 3D Reconstruction.

The evolution of cameras and LiDAR has propelled the techniques and applications of three-dimensional (3D) reconstruction. However, due to inherent sensor limitations and environmental interference, the reconstruction process often entails significant texture noise, such as specular highlight, color inconsistency, and object occlusion. Traditional methodologies grapple to mitigate such noise, particularly in large-scale scenes, due to the voluminous data produced by imaging sensors. In response, this paper introduces an omnidirectional-sensor-system-based texture noise correction framework for large-scale scenes, which consists of three parts. Initially, we obtain a colored point cloud with luminance value through LiDAR points and RGB images organization. Next, we apply a voxel hashing algorithm during the geometry reconstruction to accelerate the computation speed and save the computer memory. Finally, we propose the key innovation of our paper, the frame-voting rendering and the neighbor-aided rendering mechanisms, which effectively eliminates the aforementioned texture noise. From the experimental results, the processing rate of one million points per second shows its real-time applicability, and the output figures of texture optimization exhibit a significant reduction in texture noise. These results indicate that our framework has advanced performance in correcting multiple texture noise in large-scale 3D reconstruction.

Price Discovery and Market Efficiency in India's Financial Futures Market within the Derivatives Landscape: An Empirical Analysis

This study offers a robust, long-term analysis of price discovery and the persisting lead-lag relationship between India’s equity futures and spot markets. Utilising monthly data from April 2005 to December 2022, it filters transient noise typically associated with high-frequency data. Information Share and Common Factor Weight methodologies within the Vector Error Correction (VEC) framework consistently reveal the dominance of futures markets in the price discovery process. The insights remain consistent across three distinct periods spanning the global financial crisis and the COVID-19 pandemic. The volatility dynamics and asymmetry effects in the Indian equity futures and spot markets using the Vector Error Correction - Exponential General Autoregressive Conditional Heteroskedastic (VEC-EGARCH) approach uncover notable asymmetry effects, signifying a strong market sensitivity to negative news. This highlights the necessity for comprehensive risk management strategies and stringent regulatory supervision, especially in light of the significant growth and systemic risks in the Indian derivatives market.

Multi-taper-based automatic correction of non-anechoic antenna measurements

Prototype measurements belong to the key steps in the development of antenna structures. Although accurate validation of their far-field performance can be realized in dedicated facilities, such as anechoic chambers, the high cost of their construction and maintenance might not be justified if the main goal of measurements is to support teaching or low-budget research. Instead, they can be performed in non-anechoic conditions and then refined using appropriate correction algorithms. Unfortunately, the existing post-processing methods suffer from multiple challenges that include manual setup of parameters as well as validation of performance in idealized conditions. In this communication, a multi-taper-based framework for correction of antenna characteristics obtained in non-anechoic environments has been proposed. The algorithm augments one-shot measurements of the structure under test in order to extract the line-of-sight responses while attenuating the interferences pertinent to multi-path propagation and noise from external sources of radiation. The performance of the proposed correction routine has been demonstrated in two test sites using a geometrically small Vivaldi radiator and validated against state-of-the-art techniques from the literature. The uncertainty budget for the measurements performed using the approach amounts to 0.26 dB, which is low given challenging propagation conditions considered for experiments.

Investigating the Performance of CMIP6 Seasonal Precipitation Predictions and a Grid Based Model Heterogeneity Oriented Deep Learning Bias Correction Framework

AbstractClimate change is expected to alter the magnitude and spatiotemporal patterns of hydro‐climate variables such as precipitation, which has significant impacts on the ecosystem, human societies and water security. Global Climate Models are the major tools to simulate historical as well as future precipitation. However, due to imperfect model structures, parameters and boundary conditions, direct model outputs are subject to large uncertainty, which needs serious evaluation and bias correction before usage. In this study, seasonal precipitation predictions from 30 Coupled Model Inter‐comparison Project Phase 6 (CMIP6) models and Climate Research Unit observations are used to evaluate historical precipitation climatology in global continents during 1901–2014. A grid based model heterogeneity oriented Convolutional Neural Network (CNN) is proposed to correct the ensemble mean precipitation bias ratio. Besides, regression based Linear Scaling (LS), distribution based Quantile Mapping (QM) and spatial correlation CNN bias correction approaches are employed for comparison. Results of model performance evaluation indicate that generally precipitation prediction is more reliable in JJA than DJF on the global scale. Most models tend to have larger bias ratio for extreme precipitation. In addition, current CMIP6 models still have certain issues in accurate simulation of precipitation in mountainous regions and the regions affected by complex climate systems. Moreover, the proposed grid based model heterogeneity oriented CNN has better performance in ensemble mean bias correction than LS, QM, and spatial correlation CNN, which could consider the relative model performance and capture the features similar to actual climate dynamics.

MCP-Net: Introducing Patlak Loss Optimization to Whole-body Dynamic PET Inter-frame Motion Correction.

In whole-body dynamic positron emission tomography (PET), inter-frame subject motion causes spatial misalignment and affects parametric imaging. Many of the current deep learning inter-frame motion correction techniques focus solely on the anatomy-based registration problem, neglecting the tracer kinetics that contains functional information. To directly reduce the Patlak fitting error for 18F-FDG and further improve model performance, we propose an interframe motion correction framework with Patlak loss optimization integrated into the neural network (MCP-Net). The MCP-Net consists of a multiple-frame motion estimation block, an image-warping block, and an analytical Patlak block that estimates Patlak fitting using motion-corrected frames and the input function. A novel Patlak loss penalty component utilizing mean squared percentage fitting error is added to the loss function to reinforce the motion correction. The parametric images were generated using standard Patlak analysis following motion correction. Our framework enhanced the spatial alignment in both dynamic frames and parametric images and lowered normalized fitting error when compared to both conventional and deep learning benchmarks. MCP-Net also achieved the lowest motion prediction error and showed the best generalization capability. The potential of enhancing network performance and improving the quantitative accuracy of dynamic PET by directly utilizing tracer kinetics is suggested.

Off‐policy correction algorithm for double Q network based on deep reinforcement learning

AbstractA deep reinforcement learning (DRL) method based on the deep deterministic policy gradient (DDPG) algorithm is proposed to address the problems of a mismatch between the needed training samples and the actual training samples during the training of intelligence, the overestimation and underestimation of the existence of Q‐values, and the insufficient dynamism of the intelligence policy exploration. This method introduces the Actor‐Critic Off‐Policy Correction (AC‐Off‐POC) reinforcement learning framework and an improved double Q‐value learning method, which enables the value function network in the target task to provide a more accurate evaluation of the policy network and converge to the optimal policy more quickly and stably to obtain higher value returns. The method is applied to multiple MuJoCo tasks on the Open AI Gym simulation platform. The experimental results show that it is better than the DDPG algorithm based solely on the different policy correction framework (AC‐Off‐POC) and the conventional DRL algorithm. The value of returns and stability of the double‐Q‐network off‐policy correction algorithm for the deep deterministic policy gradient (DCAOP‐DDPG) proposed by the authors are significantly higher than those of other DRL algorithms.

Classification with noisy labels through tree-based models and semi-supervised learning: A case study of lithology identification

Lithology identification is a crucial task for reservoir characterization and evaluation. There exists an intricate non-linear response between formation lithology and logging data. However, it is difficult to avoid lithology mislabeling due to human error and interpretation coarsening, and label quality can seriously affect the effectiveness of supervised learning. The presence of noisy labels makes it essential to learn with noisy labels. Noise-filtering methods and noise-robust algorithms only concentrate on a singular aspect of data or algorithm. In this paper, hybrid noise label filtering and correction framework for lithology identification (HNFCL) is proposed. Isolation forest is utilized to detect suspicious data, as it is efficient and fast. Baseline classifiers are built by ensemble tree models. In particular, the labels of abnormal data are removed and Tri-training semi-supervised method is introduced to relabel these data, which minimizes the loss of valid training data. Comprehensive experiments of the HNFCL framework, noise filtering methods and deep neural network methods with optimized loss functions were carried out in the industrial application of logging lithology identification. HNFCL achieved average accuracy of 87.94% and 94.93% in two study wells. These results outperformed the noise filtering methods and showed no significant difference from the state-of-the-art method. The correction of noise by HNFCL will provide a prospect for lithology identification applications.

Spatially Explicit Correction of Simulated Urban Air Temperatures Using Crowdsourced Data.

Urban climate model evaluation often remains limited by a lack of trusted urban weather observations. The increasing density of personal weather sensors (PWSs) make them a potential rich source of data for urban climate studies that address the lack of representative urban weather observations. In our study, we demonstrate that carefully quality-checked PWS data not only improve urban climate models' evaluation but can also serve for bias correcting their output prior to any urban climate impact studies. After simulating near-surface air temperatures over London and south-east England during the hot summer of 2018 with the Weather Research and Forecasting (WRF) Model and its building Effect parameterization with the building energy model (BEP-BEM) activated, we evaluated the modeled temperatures against 402 urban PWSs and showcased a heterogeneous spatial distribution of the model's cool bias that was not captured using official weather stations only. This finding indicated a need for spatially explicit urban bias corrections of air temperatures, which we performed using an innovative method using machine learning to predict the models' biases in each urban grid cell. This bias-correction technique is the first to consider that modeled urban temperatures follow a nonlinear spatially heterogeneous bias that is decorrelated from urban fraction. Our results showed that the bias correction was beneficial to bias correct daily minimum, daily mean, and daily maximum temperatures in the cities. We recommend that urban climate modelers further investigate the use of quality-checked PWSs for model evaluation and derive a framework for bias correction of urban climate simulations that can serve urban climate impact studies.

Lévy-Leblond equation and Eisenhart–Duval lift in Koopman–von Neumann mechanics

The Koopman–von Neumann (KvN) mechanics is an approach that was formulated long ago to answer the question regarding the existence of a Hilbert space representation of classical mechanics. KvN mechanics is a non-relativistic theory, and it is not clear how spin can be included in it, since spin is widely regarded as a relativistic property. Cabrera et al., in Eur. Phys. J. Spec. Top. 227, 2195 (2019) argued that the Spohn equation [Spohn, Ann. Phys. 282, 420 (2000)] is the correct classical framework for the Koopman–von Neumann theory corresponding to the Dirac equation. However, after Lévy-Leblond’s seminal work on this topic, it became clear that spin naturally arises also from the Galilean invariant wave equations, without any need of relativistic considerations. Inspired by this, we propose another possibility of including spin in the KvN formalism: the Lévy-Leblond equation in the Koopman–von Neumann formalism can be obtained as a null reduction of the massless Dirac equation in the Eisenhart–Duval lift metric. To illustrate the idea, we implement it for a one-dimensional classical system without magnetic interactions.