Error Correction Method Research Articles

Improving Diffusion-Based Image Restoration with Error Contraction and Error Correction

Generative diffusion prior captured from the off-the-shelf denoising diffusion generative model has recently attained significant interest. However, several attempts have been made to adopt diffusion models to noisy inverse problems either fail to achieve satisfactory results or require a few thousand iterations to achieve high-quality reconstructions. In this work, we propose a diffusion-based image restoration with error contraction and error correction (DiffECC) method. Two strategies are introduced to contract the restoration error in the posterior sampling process. First, we combine existing CNN-based approaches with diffusion models to ensure data consistency from the beginning. Second, to amplify the error contraction effects of the noise, a restart sampling algorithm is designed. In the error correction strategy, the estimation-correction idea is proposed on both the data term and the prior term. Solving them iteratively within the diffusion sampling framework leads to superior image generation results. Experimental results for image restoration tasks such as super-resolution (SR), Gaussian deblurring, and motion deblurring demonstrate that our approach can reconstruct high-quality images compared with state-of-the-art sampling-based diffusion models.

Abstract 2432: Improved liquid biopsy assay performance using sequencing by binding (SBB)

Abstract Liquid biopsy is revolutionizing the field of early cancer detection research through non-invasive detection of tumor DNA in the blood. However, existing liquid biopsy assays are limited in their sensitivity for ctDNA detection at low variant allele frequencies (VAFs), with most relying on extreme sequencing depth and computational error correction to separate the true ctDNA signal from background errors. This limitation is particularly problematic in the area of early cancer detection, in which expected ctDNA allele frequencies are extremely low. Novel strategies are therefore needed to help improve liquid biopsy assay sensitivity and reduce per-sample sequencing requirements. Here we describe PacBio’s application of the Onso short-read sequencing system to enable detection of ctDNA at low VAFs using the SeraCare Complete ctDNA Mutation Mix reference standard. The Onso system makes use of a novel sequencing by binding (SBB) method to achieve up to 15x greater quality scores, with ≥90% of reads at Q40 or above. We performed targeted capture and sequencing of libraries prepared from the SeraCare reference mix diluted into WT human DNA at the following VAFs: 0.00% (WT), 0.05%, 0.10%, 0.25%, and 0.50%, and compared the sensitivity at each VAF for SBB compared to a competitor method using sequencing by synthesis (SBS) at varying sequencing depths. We observed superior sensitivity for ctDNA detection at low VAFs (0.05%, 0.1%) using SBB at half the sequencing depth compared to SBS, in part due to reduced false positive calling in the WT sample for SBB. Furthermore, SBB was able to achieve comparable sensitivity results to SBS using four-fold less sequencing, and without the use of computational error correction. Finally, combining SBB with computational error-correction methods boosted sensitivity even further, suggesting an additive value for these technologies. Taken together, our results demonstrate the potential of SBB to improve upon existing methods of liquid biopsy and better enable research on early cancer detection. Citation Format: Dan Nasko, Phillip Pham, Stuti Joshi, Kristi Kim, Nairi Pezeshkian, Young Kim, Alexandra Sockell, Jonas Korlach. Improved liquid biopsy assay performance using sequencing by binding (SBB) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2432.

Composite Model Analysis in Forecasting the Malaysian Imports

For more than a century, forecasting models have been crucial in a variety of fields. Models can offer the most accurate forecasting outcomes if error terms are normally distributed. Finding out a good statistical model for time series predicting imports in Malaysia is the main target of this study. The decision made during this study mostly addresses Vector Error Correction Method (VECM), composite model (Combined regression-ARIMA), and ARIMA model. The imports of Malaysia from the first quarter of 1991 to the first quarter of 2023 are employed in this study’s quarterly time series data. The forecasting outcomes of the current study demonstrated that the composite model offered more probabilistic data, which improved forecasting the volume of Malaysia’s imports. The (ARIMA) mode, composite model, and VECM model in this study are linear models based on responses to Malaysia’s imports. Future studies might compare the performance of nonlinear and linear models in forecasting.

Estimation and Analysis of Glacier Mass Balance in the Southeastern Tibetan Plateau Using TanDEM-X Bi-Static InSAR during 2000–2014

In recent decades, glaciers in the southeastern Tibetan Plateau (SETP) have been rapidly melting and showing a large scale of glacier mass loss. Due to the lack of large-scale, high-resolution, and high-precision observations, knowledge on the spatial distribution of the glacier mass balance and the response to climate change is limited in this region. We propose a TanDEM-X bi-static InSAR (Interferometric Synthetic Aperture Radar) algorithm with a non-local mean filter method and difference strategy, to improve the precision of glacier surface elevation change detection. Moreover, we improved the glacier mass balance estimation algorithm with a correction method for multi-source system errors and an uncertainty evaluation method based on error propagation theory to reduce the uncertainty of estimations. We used 13 pairs of TanDEM-X bi-static InSAR images to obtain the glacier mass balance data for the entire SETP. The total area of glaciers monitored was 5821 km2 and the total number of glaciers monitored was 2321; the glacier surface elevation change rate was −0.505 ± 0.005 m/yr, and the glacier mass balance estimation was −454.5 ± 13.1 mm w.eq. during 2000–2014. Additionally, we analyzed the spatial distribution of the glacier mass balance within the SETP using the sub-watershed analysis method. The results showed that the mass loss rate had a decreasing trend from the southeast to the northwest. Furthermore, the temperature change and the glacier mass loss rate showed a positive correlation from the southeast to the northwest in this region. This study greatly advances our understanding of the regularities of glacier dynamics in this region, and can provide scientific support for major national goals such as the rational utilization of surrounding water resources and construction of important transportation projects.

An adaptive kernel correlation filter algorithm and synchronization error correction method for 3D motion reconstruction of flexible flapping wings using binocular vision

In order to measure the kinematic parameters and deformation of flapping wings precisely, this paper presents a wing motion reconstruction method utilizing a binocular vision system (BVS) comprising two action cameras. We designed an Adaptive Kernel Correlation Filter algorithm based on target scaling prediction for wing reconstruction to improve target tracking accuracy and robustness. A binocular error correction method with a startup time difference-induced error model was proposed, which helps the action cameras achieve the same high-precision synchronization as the industrial cameras with external trigger mode. Moreover, an infrared motion capture system is applied to assess and compensate for errors in the BVS. The experimental results show that the maximum value of the average errors of the BVS is 1.352 mm, and the minimum similarity value of the two wings is 0.996. The results contribute to the deformation measurement and optimization of soft materials such as the flapping wings.

Quantile Hedging in the complete financial market under the mixed fractional Brownian motion model and the liquidity constraint

This article proposes a pricing framework for European option that utilizes a Quantile hedging strategy in a complete financial market. The methodology involves applying the long memory Geometric Brownian motion model, utilizing the generalized mixed fractional Girsanov theorem, and incorporating relevant findings related to quasi-conditional expectation. The first step in this framework is to derive the option price formula using the equivalent martingale measure. This approach enables investors to use the entire option price without any restrictions to hedge their contingent claim. Then, we add the initial wealth condition to hedge and determine the option price with the Quantile hedging strategy. To do this, we find the maximum probability of success in complete risk hedging for different amounts of model parameters and then use the probability values to determine the option price. Moreover, we apply the minimum variance algorithms and the error correction method to find the hedge ratio. Finally, we illustrate the hedging strategy and provide numerical results that assist investors in determining the required capital for covering the option risk under market data. This calculation takes into account both the level of risk tolerance and the anticipated return on investment.

Optimizing Time-Series forecasting using stacked deep learning framework with enhanced adaptive moment estimation and error correction

Time-series forecasting is essential for decision-making activities, and deep learning models have shown promising results in this field. However, existing models face challenges in capturing the complex non-linear patterns and randomness of real-world time-series data. To address this issue, we propose a novel stacked deep learning framework that combines a bi-directional long-short-term memory network, convolutional 1D, and max-pooling-1D layers in each stack. We also introduce an enhanced version of adaptive moment estimation and an error correction method to improve the model's accuracy and convergence speed. We evaluate our model's performance using four real-world datasets (air quality, PM2.5, electricity price, and securities lending) and compare it with existing models based on statistical measures such as mean absolute error, mean squared error, and standard deviation. The results demonstrate that our proposed model outperforms existing models significantly, achieving a ∼ 52 % improvement in mean absolute error for air quality data, a ∼ 36 % improvement in mean squared error for the PM2.5 dataset, and a ∼ 27.5 % improvement in mean squared error for electricity price data. The enhanced adaptive moment estimation and error correction method further improve the model's accuracy and generalization capabilities, making it suitable for multi-domain data sets. Our proposed model has potential applications in various fields, including manufacturing, traffic prediction, weather forecasting, environmental monitoring, finance, and energy management.

Computational label-free microscope through a custom-built high-throughput objective lens and Fourier ptychography

Label-free microscopy directly images live samples without fluorescent markers, providing a panoramic view of biological structures and functions. However, pursuing high-throughput and high-content live cell imaging requires both higher spatial resolution and larger field of view, which are limited by the optical system. In this study, we custom-built a 5▪/0.35 objective with a field number of 28 mm, providing a space-bandwidth product of 34 megapixels, which is four times greater than that of commercial high-throughput objectives such as the Nikon Lambda series (4▪/0.2, 10▪/0.45, 20▪/0.75). Furthermore, we incorporated Fourier ptychography microscopy (FPM) into the system, achieving a synthetic numerical aperture of 0.72, suitable for digital pathology and long-term live cell observation. To improve the FPM performance, we proposed a phantom-based calibration method for quantitative correction of illumination angle errors. Additionally, this method can also serve as an initial step for the on-line calibration. The remarkable capabilities of our 5▪/0.35 objective have been demonstrated, as well as the effectiveness of computational label-free microscopy. Furthermore, by combining our original FPM with more advanced hardware and algorithms, our FPM can achieve higher performance.

Enhancing detection of topological order by local error correction

The exploration of topologically-ordered states of matter is a long-standing goal at the interface of several subfields of the physical sciences. Such states feature intriguing physical properties such as long-range entanglement, emergent gauge fields and non-local correlations, and can aid in realization of scalable fault-tolerant quantum computation. However, these same features also make creation, detection, and characterization of topologically-ordered states particularly challenging. Motivated by recent experimental demonstrations, we introduce a paradigm for quantifying topological states—locally error-corrected decoration (LED)—by combining methods of error correction with ideas of renormalization-group flow. Our approach allows for efficient and robust identification of topological order, and is applicable in the presence of incoherent noise sources, making it particularly suitable for realistic experiments. We demonstrate the power of LED using numerical simulations of the toric code under a variety of perturbations. We subsequently apply it to an experimental realization, providing new insights into a quantum spin liquid created on a Rydberg-atom simulator. Finally, we extend LED to generic topological phases, including those with non-abelian order.

Method of compensating for instrumental uncertainty in measurements using a coordinate measuring ARM

Due to the influence of dynamic factors in various measurement configurations, the degree of uncertainty in measurements using a Coordinate Measuring Arm (CMA) is directly related to the measurement configuration. However, existing models for compensating CMA errors do not account dynamic factors, which impose certain limits for improving the accuracy of CMAs. To solve this issue, a method for residual error correction based on a polynomial model for single-point measurements was proposed. The influence of the CMA configuration on the residual probe error was analyzed. To enhance accuracy, a polynomial model has been developed by studying the relationship between the rotation angles of the CMA's moving elements and the probe coordinates in a cylindrical coordinate system. Experimental results demonstrate that the residual error correction method significantly compensates for instrumental uncertainty, greatly improving the accuracy of measurements using CMAs. Keywords: coordinate measuring arm, measurement error, coordinate measurements, calculation method, single-point residual correction, compensation.

Active Compensation Technology for the Target Measurement Error of Two-Axis Electro-Optical Measurement Equipment.

For two-axis electro-optical measurement equipment, there are many error sources in parts manufacturing, assembly, sensors, calibration, and so on, which cause some random errors in the final measurement results of the target. In order to eliminate the random measurement error as much as possible and improve the measurement accuracy, an active compensation technique for target measurement error is proposed in this paper. Firstly, the error formation mechanism and error transfer model establishment of the two-axis electro-optical measurement equipment were studied, and based on that, three error compensation and correction methods were proposed: the least square (LS)-based error compensation method, adaptive Kalman filter(AKF)-based error correction method, and radial basis function neural network (RBFNN)-based error compensation method. According to the theoretical analysis and numerical simulation comparison, the proposed RBFNN-based error compensation method was identified as the optimal error compensation method, which can approximate the random error space surface more precisely, so that a more accurate error compensation value can be obtained, and in order to improve the measurement accuracy with higher precision. Finally, the experimental results proved that the proposed active compensation technology was valid in engineering applicability and could efficiently enhance the measurement accuracy of the two-axis electro-optical measurement equipment.

Image Identification and Error Correction Method for Test Report Based on Deep Reinforcement Learning and IoT Platform in Smart Laboratory

In order to solve the problems that most models are complex, time-consuming, and have difficulty in identifying image errors, an image identification and error correction method of test report based on deep reinforcement learning and the internet of things platform in the smart lab was proposed. Firstly, a smart lab architecture was designed based on the internet of things platform, achieving efficient operation of the laboratory through cloud edge collaboration. Then, the depth separable convolution improved convolutional neural network is used to extract image features, and the features are input into bidirectional recurrent neural networks (BiLSTM) for analysis to complete image recognition. Finally, the ICNN-BiLSTM model is used as the agent of reinforcement learning, and image error correction is completed by identifying the distance between the image and the key points of the reference image. Based on the Python platform, the proposed method was experimentally demonstrated, and the results showed that its average error correction accuracy reached 96.75%, with a processing time of 15.37s.

A double‐layer neural network wind speed prediction framework based on training set segmentation and error correction

AbstractHighly accurate wind speed prediction is one of the effective ways to improve wind energy utilization. Therefore, this study proposes a novel double‐layer neural network (D‐NN) framework based on feedforward neural networks. First, a feedforward neural network with an extremely short training time is used as the basic learner to ensure that the D‐NN framework has a short training and optimization time. Then, the double‐layer neural network automatically segments and adjusts the length of the training set using an improved error correction method, thus improving the model's ability to capture sequence features and enhancing the model's generalization performance in a non‐directly‐connected superposition manner. Finally, a genetic algorithm is used to automatically optimize the hyperparameters of each sub‐neural network in the D‐NN and the optimal training set length of each sub‐neural network, resulting in the final optimized prediction model. The accuracy and reliability of the prediction models designed based on the D‐NN framework are verified by comparing several different models. The results of the five different prediction evaluation metrics show that the D‐NN framework can greatly improve the accuracy of wind speed prediction.

Renewable Energy and Economic Growth: Evidence from India

This article examines the nexus between economic growth and two renewable energy sources, namely wind and solar, to separate out the contrast between these two sources, for India deploying system generalised method of moments and vector error correction method suitable to capture the dynamic nature of panel data. Unlike most of the earlier studies, it takes into account cross-sectional dependence and addresses the issue of endogeneity. India has been chosen because despite India being one of the largest producers of renewable energy globally; the nexus is under-studied. This article finds the installation of solar energy capacity positively influences the Gross State Domestic Product (GSDP) growth. Moreover, there is a bi-directional positive relation between the installation of wind energy capacity and GSDP growth. Hence the adoption of renewable energy is helping the states to grow faster. However, the study found not much difference in the nature of the nexus between solar energy growth and wind energy growth for India. JEL Codes: O13, P28, P44, Q56

Multivariate trace estimation in constant quantum depth

There is a folkloric belief that a depth-Θ(m) quantum circuit is needed to estimate the trace of the product of m density matrices (i.e., a multivariate trace), a subroutine crucial to applications in condensed matter and quantum information science. We prove that this belief is overly conservative by constructing a constant quantum-depth circuit for the task, inspired by the method of Shor error correction. Furthermore, our circuit demands only local gates in a two dimensional circuit – we show how to implement it in a highly parallelized way on an architecture similar to that of Google's Sycamore processor. With these features, our algorithm brings the central task of multivariate trace estimation closer to the capabilities of near-term quantum processors. We instantiate the latter application with a theorem on estimating nonlinear functions of quantum states with "well-behaved" polynomial approximations.

Misclassification in Automated Content Analysis Causes Bias in Regression. Can We Fix It? Yes We Can!

ABSTRACT Automated classifiers (ACs), often built via supervised machine learning (SML), can categorize large, statistically powerful samples of data ranging from text to images and video. They have become widely popular measurement devices in communication science and related fields. Despite this popularity, even highly accurate classifiers make errors that cause misclassification bias and misleading results when input to downstream statistical analyses–unless such analyses account for these errors. As we show in a systematic literature review of SML applications, communication scholars largely ignore misclassification bias. In principle, existing statistical methods can use “gold standard” validation data, such as that created by human annotators, to correct misclassification bias. We introduce and test such methods, including a new method we design and implement in the R package misclassification_models, via Monte Carlo simulations designed to reveal each method’s limitations, which we also release. Based on our results, we recommend our new error correction method as it is versatile and efficient. In sum, automated classifiers, even those below common accuracy standards or those making systematic misclassifications, can be useful for measurement with careful study design and appropriate error correction methods.

Measuring Nonlocal Brane Order with Error-Corrected Quantum Gas Microscopes

Exotic quantum many-body states, such as Haldane and spin liquid phases, can exhibit remarkable features like fractional excitations and non-Abelian statistics and offer new understandings of quantum entanglement in many-body quantum systems. These phases are classified by nonlocal correlators that can be directly measured in atomic analog quantum simulating platforms, such as optical lattices and Rydberg atom arrays. However, characterizing these phases in large systems is experimentally challenging because they are sensitive to local errors like atom loss, which suppress its signals exponentially. Additionally, protocols for systematically identifying and mitigating uncorrelated errors in analog quantum simulators are lacking. Here, we address these challenges by developing an error-correction method for large-scale neutral atom quantum simulators using optical lattices. Our error-correction method can distinguish correlated particle-hole pairs from uncorrelated holes in the Mott insulator. After removing the uncorrelated errors, we observe a dramatic improvement in the nonlocal parity correlator and find the perimeter scaling law. Furthermore, the error model provides a statistical estimation of fluctuations in site occupation, from which we measure the generalized brane correlator and confirm that it can be an order parameter for Mott insulators in two dimensions. Our work provides a promising avenue for investigating and characterizing exotic phases of matters in large-scale quantum simulators. Published by the American Physical Society 2024

Electricity demand error corrections with attention bi-directional neural networks

Reliable forecast of electricity demand is crucial to stability, supply, and management of electricity grids. Short-term hourly and sub-hourly demand forecasts are difficult due to the stochastic nature of electricity demand. To improve the electricity demand forecasts, artificial neural networks are combined with attention-based bi-directional neural network as well as Error Correction methods, and Bayesian hyperparameter optimizations. As part of the hybrid VMD-CABLSTM-ANN-EC model, the intrinsic mode functions, representing distinct predictive features, are separated from electricity demand data using variational mode decomposition algorithm. For predicting each intrinsic mode function and respective error time series, convolutional neural networks-bi-directional long short-term memory algorithms are applied. In a second stage, error time series are split, followed by a model to predict the error series’ intrinsic mode functions, and a final integration stage to predict the error. The results of the proposed VMD-CABLSTM-ANN-EC model are benchmarked against decomposition-based and standalone models at substations in Queensland, Australia. The proposed VMD-CABLSTM-ANN-EC model outperformed all benchmark models. By splitting the predictor and target variables, it will be shown that the variational model decomposition method improves error corrections by revealing key features of historical demand data. Using error correction method in short-term electricity demand modelling can provide greater confidence in the prediction of electricity demand. The models can be used by energy providers for market analysis and insight research, to manage power failure risk, and make financial decisions.

Large Range of a High-Precision, Independent, Sub-Mirror Three-Dimensional Co-Phase Error Sensing and Correction Method via a Mask and Population Algorithm.

The emergence of segmented mirrors is expected to solve the design, processing, manufacturing, testing, and launching of space telescopes of large apertures. However, with the increase in the number of sub-mirrors, the sensing and correction of co-phase errors in segmented mirrors will be very difficult. In this paper, an independent three-dimensional method for sub-mirror co-phase error sensing and correction method is proposed. The method is based on a wide spectral modulation transfer function (MTF), mask, population optimization algorithm, and online model-free correction. In this method, the sensing and correction process of each sub-mirror co-phase error is independent of each other, so the increase in the number of sub-mirrors will not increase the difficulty of the method. This method can sense and correct the co-phase errors of three dimensions of the sub-mirror, including piston, tip, and tilt, even without modeling the optical system, and has a wide detection range and high precision. And the efficiency is high because the sub-mirrors can be corrected simultaneously in parallel. Simulation results show that the proposed method can effectively sense and correct the co-phase errors of the sub-mirrors in the range [-50λ, 50λ] in three dimensions with high precision. The average RMSE value in 100 experiments of the true co-phase error values and the experimental co-phase error values of one of the six sub-mirrors is 2.358 × 10-7λ.

An Econometric Assessment of the Impact of Crude Oil Production on Health in Nigeria

Health is a fundamental human right. It additionally implies the ability to live socially and economically productive lives. With rapid expansion of the Petroleum industry with oil accounting for seventy percent of the total export earnings during the 1970s, the Nigerian economy was re-structured from agriculture-driven to oil dependent. Expenditure on health depends on several factors. It is assumed that as natural resource grows, health expenditure will grow. However in case of Nigeria, this will depend on the oil revenue available to the government from the exportation of oil. The most significant environmental challenge facing the nation at present is that of pollution arising from oil exploration and production particularly in the oil-rich Niger-Delta region. Residents of these communities have suffered greatly from the effects of oil-spillage, exposure to oil-spills, gas flaring and other diminishing effects. The major objective of this study is to assess the impact of crude oil production on the health sector in Nigeria. This research paper uses the co-integration and Error Correction Methods (ECM) to analyze the relationship between Health, oil-sourced health revenue, gas flaring and domestic consumption of crude oil. After deriving the results of the error correction representation, we come to the conclusion that crude oil production has a significant impact on health in Nigeria. The study also makes one remarkable discovery and that is the negative impacts gas flaring has on health in Nigeria. It is therefore recommended that the Nigerian government should stop preference treatment of oil Multinationals and deal decisively with erring companies who deliberately distort the standard of health of Nigerians through oil spillage and gas flaring. Also, incentives should be given to oil companies to undertake best practices together with adequate reward for prompt adherence while more revenue from oil earning should be devoted to the health sector as this will surely impact significantly on the improvement of healthcare of Nigerians.