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2. Editorial June 2024 (Vol 40, Issue 6).
- Author
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Magnenat-Thalmann, Nadia
- Subjects
POSE estimation (Computer vision) ,OBJECT recognition (Computer vision) ,BIAS correction (Topology) ,HIGH dynamic range imaging ,OPTICAL remote sensing - Abstract
The June 2024 issue of Visual Computer includes six best papers from the Cyberworlds 2022 conference, five best papers from the second part of the special issue on Deep Learning for 3D Segmentation, and 34 regular papers. The selected papers from the Cyberworlds conference cover topics such as procedural modeling, context-aware personality estimation, immersive haptic simulation, video compression-cum-classification, semantics-aware addition and LoD of 3D window details, and military education in extended reality. The papers from the Deep Learning for 3D Segmentation section focus on point cloud semantic segmentation, structure-preserving image smoothing, multimodal feature fusion network, unsupervised contrastive learning, and 6D pose estimation of reflective texture-less objects. The regular papers cover a wide range of topics in computer graphics and visualization. The issue is edited by Nadia Magnenat Thalmann, the Editor-in-Chief of the Visual Computer. [Extracted from the article]
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- 2024
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3. RRT Autonomous Detection Algorithm Based on Multiple Pilot Point Bias Strategy and Karto SLAM Algorithm.
- Author
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Lieping Zhang, Xiaoxu Shi, Liu Tang, Yilin Wang, Jiansheng Peng, and Jianchu Zou
- Subjects
MOBILE robots ,BIAS correction (Topology) ,ALGORITHMS ,REFERENCE values ,STATISTICAL sampling ,PROBLEM solving - Abstract
A Rapid-exploration Random Tree (RRT) autonomous detection algorithm based on the multi-guide-node deflection strategy andKarto Simultaneous Localization andMapping (SLAM) algorithmwas proposed to solve the problems of low efficiency of detecting frontier boundary points and drift distortion in the process of map building in the traditional RRT algorithm in the autonomous detection strategy of mobile robot. Firstly, an RRT global frontier boundary point detection algorithm based on the multi-guide-node deflection strategy was put forward, which introduces the reference value of guide nodes' deflection probability into the random sampling function so that the global search tree can detect frontier boundary points towards the guide nodes according to random probability. After that, a new autonomous detection algorithm for mobile robots was proposed by combining the graph optimization-based Karto SLAM algorithm with the previously improved RRT algorithm. The algorithm simulation platform based on the Gazebo platform was built. The simulation results show that compared with the traditional RRT algorithm, the proposed RRT autonomous detection algorithm can effectively reduce the time of autonomous detection, plan the length of detection trajectory under the condition of high average detection coverage, and complete the task of autonomous detection mapping more efficiently. Finally, with the help of the ROS-based mobile robot experimental platform, the performance of the proposed algorithm was verified in the real environment of different obstacles. The experimental results show that in the actual environment of simple and complex obstacles, the proposed RRT autonomous detection algorithm was superior to the traditional RRT autonomous detection algorithm in the time of detection, length of detection trajectory, and average coverage, thus improving the efficiency and accuracy of autonomous detection. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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4. Using Genetic Algorithms and Core Values of Cooperative Games to Solve Fuzzy Multiobjective Optimization Problems.
- Author
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Wu, Hsien-Chung
- Subjects
COOPERATIVE game theory ,GENETIC algorithms ,ASSIGNMENT problems (Programming) ,BIAS correction (Topology) ,GAMES - Abstract
A new methodology for solving the fuzzy multiobjective optimization problems is proposed in this paper by considering the fusion of cooperative game theory and genetic algorithm. The original fuzzy multiobjective optimization problem needs to be transformed into a scalar optimization problem, which is a conventional optimization problem. Usually, the assignments of suitable coefficients to the corresponding scalar optimization problem are subjectively determined by the decision makers. However, these assignments may cause some biases by their subjectivity. Therefore, this paper proposes a mechanical procedure to avoid this subjective biases. We are going to formulate a cooperative game using the α -level functions of the multiple fuzzy objective functions. Under this setting, the suitable coefficients can be determined mechanically by involving the core values of the cooperative game, which is formulated using the multiple fuzzy objective functions. We shall prove that the optimal solutions of the transformed scalar optimization problem are indeed the nondominated solutions of fuzzy multiobjective optimization problem. Since the core-nondominated solutions will depend on the coefficients that are determined by the core values of cooperative game, there will be a lot of core-nondominated solutions that will also depend on the corresponding coefficients. In order to obtain the best core-nondominated solution, we shall invoke the genetic algorithms by evolving the coefficients. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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5. Fourier Ptychographic Neural Network Combined with Zernike Aberration Recovery and Wirtinger Flow Optimization.
- Author
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Wang, Xiaoli, Lin, Zechuan, Wang, Yan, Li, Jie, Wang, Xinbo, and Wang, Hao
- Subjects
ZERNIKE polynomials ,BACK propagation ,OPTICAL aberrations ,TRANSFER functions ,BIAS correction (Topology) ,OPTICAL images ,MICROSCOPY - Abstract
Fourier ptychographic microscopy, as a computational imaging method, can reconstruct high-resolution images but suffers optical aberration, which affects its imaging quality. For this reason, this paper proposes a network model for simulating the forward imaging process in the Tensorflow framework using samples and coherent transfer functions as the input. The proposed model improves the introduced Wirtinger flow algorithm, retains the central idea, simplifies the calculation process, and optimizes the update through back propagation. In addition, Zernike polynomials are used to accurately estimate aberration. The simulation and experimental results show that this method can effectively improve the accuracy of aberration correction, maintain good correction performance under complex scenes, and reduce the influence of optical aberration on imaging quality. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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6. An Improved RRT* UAV Formation Path Planning Algorithm Based on Goal Bias and Node Rejection Strategy.
- Author
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Liu, Haiying, Chen, Jie, Feng, Jianxin, and Zhao, Haiping
- Subjects
DRONE aircraft ,BIAS correction (Topology) ,ALGORITHMS - Published
- 2023
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7. A Bayesian Approach to Uncertainty in Word Embedding Bias Estimation.
- Author
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Dobrzeniecka, Alicja and Urbaniak, Rafal
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ESTIMATION bias ,RACE ,SOURCE code ,STATISTICAL significance ,BIAS correction (Topology) ,SAMPLE size (Statistics) - Abstract
Multiple measures, such as WEAT or MAC, attempt to quantify the magnitude of bias present in word embeddings in terms of a single-number metric. However, such metrics and the related statistical significance calculations rely on treating pre-averaged data as individual data points and utilizing bootstrapping techniques with low sample sizes. We show that similar results can be easily obtained using such methods even if the data are generated by a null model lacking the intended bias. Consequently, we argue that this approach generates false confidence. To address this issue, we propose a Bayesian alternative: hierarchical Bayesian modeling, which enables a more uncertainty-sensitive inspection of bias in word embeddings at different levels of granularity. To showcase our method, we apply it to Religion, Gender, and Race word lists from the original research, together with our control neutral word lists. We deploy the method using Google, GloVe, and Reddit embeddings. Further, we utilize our approach to evaluate a debiasing technique applied to the Reddit word embedding. Our findings reveal a more complex landscape than suggested by the proponents of single-number metrics. The datasets and source code for the paper are publicly available. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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8. A magnetic-mechanical strong coupling FEM model and vibration characteristic analysis of transformer core under DC bias.
- Author
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Wang, Yaqi, Li, Lin, Sun, Jia'an, and Zhao, Xiaojun
- Subjects
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BIAS correction (Topology) , *FINITE element method , *ELECTROMAGNETIC induction , *VARIATIONAL principles , *MAGNETIC fields , *MAGNETIC circuits - Abstract
The vibration of a transformer core induced by magnetostriction is closely related to the environment and safety of devices. The DC bias excitation leads to the over saturation of the transformer core and causes more severe vibration. In this paper, a magnetic-mechanical strong coupling model based on a fixed-point time-periodic 2D finite element method with excitation voltage and DC bias current as the input is established in which the vector magnetic potential A, winding current I, and displacement in the core u are simultaneously taken as solution variables, and the fixed-point reluctivity is introduced to deal with the nonlinear problem of the core. In this model, the magnetic field and mechanical field are associated with the piezomagnetic equations and variational principle, and the circuit and magnetic fields are connected through the law of electromagnetic induction. The equivalent thickness of the core model and the determination of the initial value are discussed. A test transformer with a laminated core is made for experimental measurement. The accuracy and efficiency of the model proposed are verified by comparing the calculated results with the experimental results, and the influence of DC bias components on the magnetic characteristics and vibration of the transformer core is analyzed. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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9. Computing the Modified Bessel Function Ratio for Sine-Model Circular Distributions.
- Author
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Abushilah, Samira Faisal and Khalf, Ayaat Maan
- Subjects
BESSEL functions ,NEWTON-Raphson method ,BIAS correction (Topology) - Abstract
One difficulty in using circular distribution is the existence of the modified Bessel function, I
p (κ), which cannot be computed easily. In this paper, we consider the problem of calculating the solution of the ratio, I1 (κ)(I0 (κ))-1 , to extract the value of the parameter κ, one of the parameters of the sine-model circular distribution. At first, a direct calculation is suggested in this paper to compute the solution of the ratio by using optimization approach with the nonlinear minimization (Newton-Raphson method). After that, to check whether the direct computation for the solution of the ratio is better than the function, Alinv(⋅), in R's circular package, or not, we consider a simulation study designed to compare the results of the function that we have suggested with the estimator Alinv(⋅) . Also, we examine the difference between the true value of κ and the estimated values using a t-test with level of significance α=0.05 and we compare the estimated biases of the estimators with the approximation to the bias for the concentration parameter κ . [ABSTRACT FROM AUTHOR]- Published
- 2024
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10. Performance comparison of multiple scoring algorithms: A study of automatic scoring of English text.
- Author
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Hu, Lingling
- Subjects
GRAPH neural networks ,ENGLISH language ,MULTIPLE comparisons (Statistics) ,BIAS correction (Topology) ,FEATURE selection ,ALGORITHMS - Abstract
Objective:In order to save teachers' correcting time, improve the accuracy and efficiency of English composition grading.Methods: This paper briefly introduces the algorithm of deep sentence smoothness and text semantic matching based on graph neural network, and then designs an automatic scoring algorithm for English text. Result: The experimental data was collected from 12,000 essays written by international students in the United States in the Pratt & Whitney Foundation's Automated Student Value Assessment Project (ASAP), and these data were graded through a comparative experiment,Through comparative tests, the automatic scoring algorithm designed in this paper can achieve better scoring results and better handle automatic essay scoring problems. Among all the experimental mean values of evaluation methods, the experimental mean value of the algorithm designed in this paper is 0.790, the smoothness algorithm is 0.768, and the text matching vector is 0.759. The experimental mean values of the other two traditional automatic scoring algorithms are 0.710 and 0.712 respectively, and the results are lower than the algorithm designed in this paper. Conclusion: According to the experimental results, it can be concluded that good feature selection can give good scoring performance to the algorithm and cope with the problem of automatic scoring. At the same time, it also confirms the feasibility of the algorithm designed in this paper, which can be effectively applied in practical English composition scoring. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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11. Evaluation and Improvement of the Method for Selecting the Ridge Parameter in System Differential Response Curves.
- Author
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Xiao, Hao, Qu, Simin, Zhang, Xumin, Shi, Peng, You, Yang, Li, Fugang, Yang, Xiaoqiang, and Chen, Qihui
- Subjects
EVALUATION methodology ,STREAMFLOW ,BIAS correction (Topology) ,CURVES - Abstract
The selection of an appropriate ridge parameter plays a crucial role in ridge estimation. A smaller ridge parameter leads to larger residuals, while a larger ridge parameter reduces the unbiasedness of the estimation. This paper proposes a constrained L-curve method to accurately select the optimal ridge parameter. Additionally, the constrained L-curve method, traditional L-curve method, and ridge trace method are individually coupled with the system differential response curve to update the streamflow in the Jianyang Basin using the SWAT model. Multiple evaluation criteria are employed to analyze the efficacy of the three methods for correction. The results demonstrate that the constrained L-curve method accurately identifies the optimal ridge parameter in the actual model. Furthermore, the coupling of the constrained L-curve method with the system differential response curve exhibits markedly superior accuracy of simulated streamflow compared to the traditional L-curve and ridge trace methods, with the mean Nash–Sutcliffe efficiency (NSE) improving from 0.71 to 0.88 after correction. The constrained L-curve method, which incorporates the physical interpretation of the estimated parameters, effectively identifies the optimal ridge parameter in practical scenarios. As a result, it demonstrates superior usability and applicability when compared to the traditional L-curve method. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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12. U-net based vortex detection in Bose–Einstein condensates with automatic correction for manually mislabeled data.
- Author
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Ye, Jing, Huang, Yue, and Liu, Keyan
- Subjects
BOSE-Einstein condensation ,CONDENSED matter physics ,MACHINE learning ,BIAS correction (Topology) ,IMAGE recognition (Computer vision) ,VORTEX methods ,PETRI nets - Abstract
Quantum vortices in Bose–Einstein condensates (BECs) are essential phenomena in condensed matter physics, and precisely locating their positions, especially the vortex core, is a precondition for studying their properties. With the rise of machine learning, there is a possibility to expedite the localization process and provide accurate predictions. However, traditional machine learning requires particular considerable amount of manual data annotation, leading to uncontrollable accuracy. In this paper, we utilize the U-Net method to detect vortex positions accurately at the pixel level and propose an Automatic Correction Labeling (ACL) approach to optimize the acquisition of data sets for vortex localization in BECs. This approach addresses inaccuracies in the labeled vortex positions and improves the accuracy of vortex localization, especially the vortex core positions, while enhancing the tolerance for human mislabeling. The main process involves Rough Labeling → Machine Learning → Probability Region Search → Data Relabeling → Machine Learning again. The objective of ACL is to secure more accurate labeled data for model retraining. Through vortex localization experiments conducted in a two-dimensional Bose-Einstein condensate, our results establish the following: 1. Even under conditions of biased and missing manual annotations, U-Net can still accurately locate vortex positions; 2. Vortices exhibit certain regularities, and training U-Net with a small number of samples yields excellent predictive consequences; 3. The machine learning vortex locator based on the ACL method effectively corrects errors in manually annotated data, significantly improving the model's performance metrics, thus enhancing the precision and metrics of vortex localization. This substantial advancement in the application of machine learning in vortex localization provides an effective way for vortex dynamics localization. Furthermore, this method of obtaining more accurate positions of approximate human labels through machine learning offers new insights for machine learning in other types of image recognition problems. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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13. Image-based bolt self-localization and bolt-loosening detection using deep learning and an improved homography-based prospective rectification method.
- Author
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Xie, ChengQian, Luo, Jun, Tang, KaiSen, and Zhong, Yongli
- Subjects
DEEP learning ,MACHINE learning ,LOCALIZATION (Mathematics) ,BIAS correction (Topology) ,FLANGES - Abstract
The bolt loosening detection method has been paid attention by engineering and academic scholars. The presented methods only focus on the identification of the bolt based on deep learning, and the problem of bolt localization and the influence of shadow on distortion correction is seldom studied. In this paper, a bolt self-localization method based on YOLOv4 deep learning algorithm is proposed. A bolt numbering rule is established and the deep learning is introduced to identify the number and locate the bolt. A new square bolt gasket is proposed and four corner points are used for distortion correction. In order to reduce the influence of shadows, a grayscale enhancement strategy is proposed to improve the correction stability. Finally, a laboratory flange joint is used to verify the proposed method. The results show that the bolt self-localization method is feasible and the new bolt gasket can effectively improve the stability of distortion correction and bolt loosening detection. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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14. A Hybrid Feature Pyramid CNN-LSTM Model with Seasonal Inflection Month Correction for Medium- and Long-Term Power Load Forecasting.
- Author
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Cheng, Zizhen, Wang, Li, and Yang, Yumeng
- Subjects
LOAD forecasting (Electric power systems) ,PYRAMIDS ,INFLECTION (Grammar) ,FORECASTING ,SEASONS ,FEATURE extraction ,BIAS correction (Topology) - Abstract
Accurate medium- and long-term power load forecasting is of great significance for the scientific planning and safe operation of power systems. Monthly power load has multiscale time series correlation and seasonality. The existing models face the problems of insufficient feature extraction and a large volume of prediction models constructed according to seasons. Therefore, a hybrid feature pyramid CNN-LSTM model with seasonal inflection month correction for medium- and long-term power load forecasting is proposed. The model is constructed based on linear and nonlinear combination forecasting. With the aim to address the insufficient extraction of multiscale temporal correlation in load, a time series feature pyramid structure based on causal dilated convolution is proposed, and the accuracy of the model is improved by feature extraction and fusion of different scales. For the problem that the model volume of seasonal prediction is too large, a seasonal inflection monthly load correction strategy is proposed to construct a unified model to predict and correct the monthly load of the seasonal change inflection point, so as to improve the model's ability to deal with seasonality. The model proposed in this paper is verified on the actual power data in Shaoxing City. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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15. Off-grid DOA estimation using improved root sparse Bayesian learning for non-uniform linear arrays.
- Author
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Shen, Jiajun, Gini, Fulvio, Greco, Maria Sabrina, and Zhou, Tian
- Subjects
FALSE alarms ,COMPUTATIONAL complexity ,NUMERICAL analysis ,BIAS correction (Topology) - Abstract
This paper concerns direction of arrival (DOA) estimation based on a sparse Bayesian learning (SBL) approach. We address two inherent problems of this class of DOA estimation methods: (i) a predefined dictionary can generate off-grid problems to a SBL DOA estimator; (ii) a parametric prior generally enforces the solution to be sparse, but the existence of noise can greatly affect the sparsity of the solution. Both of these issues may have a negative impact on the estimation accuracy. In this paper, we propose an improved root SBL (IRSBL) method for off-grid DOA estimation that adopts a coarse grid to generate an initial dictionary. To reduce the bias caused by dictionary mismatch, we integrate the polynomial rooting approach into the SBL method to refine the spatial angle grid. Then, we integrate a constant false alarm rate rule in the SBL framework to enforce sparsity and improve computational efficiency. Finally, we generalize the IRSBL method to the case of non-uniform linear arrays. Numerical analysis demonstrates that the proposed IRSBL method provides improved performance in terms of both estimation accuracy and computational complexity over the most relevant existing method. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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16. Classification of breast cancer histopathological image with deep residual learning.
- Author
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Hu, Chuhan, Sun, Xiaoyan, Yuan, Zhenming, and Wu, Yingfei
- Subjects
DEEP learning ,BREAST cancer ,HISTOPATHOLOGY ,DATA augmentation ,TUMOR classification ,CONVOLUTIONAL neural networks ,BIAS correction (Topology) ,AFFINE transformations - Abstract
Breast cancer has high incidences and mortality rates in women worldwide. Malignancy could be detected manually by experienced pathologists based on Hematoxylin and Eosin (H&E) stained images. However, it is time‐consuming and experience‐dependent, making early diagnosis a big challenge. In this paper, a methodology for breast cancer classification based on histopathological images with deep learning was described. A residual learning‐based convolutional neural network named myResNet‐34 was designed for malignancy‐and‐benign classification. In addition, an algorithm automatically generating the target image for stain normalization was proposed, which eliminated the bias caused by manual selection of the reference image. Elastic distortion was introduced and combined with affine transformation for data augmentation considering the characteristics of the H&E images. Experiments were conducted on BreakHis dataset with the proposed framework. Promising results were achieved with an average classification accuracy of around 91% on image‐level classification. Results indicated that both our data augmentation and stain normalization effectively improved the classification accuracy by 2‐3%. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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17. Correction to: On statistical convergence and strong Cesàro convergence by moduli for double sequences.
- Author
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León-Saavedra, Fernando, Listán-García, María del Carmen, and Romero de la Rosa, María del Pilar
- Subjects
LOGIC ,BIAS correction (Topology) ,TECHNOLOGY convergence - Abstract
We correct a logic mistake in our paper "On statistical convergence and strong Cesàro convergence by moduli for double sequences" (León-Saavedra et al. in J. Inequal. Appl. 2022:62, 2022). [ABSTRACT FROM AUTHOR]
- Published
- 2023
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18. Information criteria bias correction for group selection.
- Author
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Marquis, Bastien and Jansen, Maarten
- Subjects
ORTHOGRAPHIC projection ,BIAS correction (Topology) ,GROUP extensions (Mathematics) ,MALVACEAE ,REGRESSION analysis - Abstract
The main contribution of this paper lies in the extension towards group lasso of a Mallows' Cp-like information criterion used in finetuning the lasso selection in a high-dimensional, sparse regression model. The optimisation of an information criterion paired with an ℓ 1 -norm regularisation method of the lasso leads to an overestimation of the model size. This is because the shrinkage following from the ℓ 1 regularisation is too permissive towards false positives, since shrinkage reduces the effects of false positives. The problem does not arise with ℓ 0 -norm regularisation but this is a combinatorial problem, which is computationally unfeasible in the high-dimensional setting. The strategy adopted in this paper is to select the non-zero variables with ℓ 1 method and estimate their values with the ℓ 0 , meaning that lasso is used for selection, followed by an orthogonal projection, i.e., debiasing after selection. This approach necessitates the information criterion to be adapted, in particular, by including what is called a "mirror correction", leading to smaller models. A second contribution of the paper is situated at the methodological level, more precisely in the development of the corrected information criterion using random hard thresholds as a model for the selection process. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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19. Record-based transmuted generalized linear exponential distribution with increasing, decreasing and bathtub shaped failure rates.
- Author
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Arshad, Mohd, Khetan, Mukti, Kumar, Vijay, and Pathak, Ashok Kumar
- Subjects
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DISTRIBUTION (Probability theory) , *MONTE Carlo method , *PROBABILITY density function , *LEAST squares , *MAXIMUM likelihood statistics , *BIAS correction (Topology) , *EXPONENTIAL functions , *BAYES' estimation - Abstract
The linear exponential distribution is a generalization of the exponential and Rayleigh distributions. This distribution is one of the best models to fit data with increasing failure rate (IFR). But it does not provide a reasonable fit for modeling data with decreasing failure rate (DFR) and bathtub shaped failure rate (BTFR). To overcome this drawback, we propose a new record-based transmuted generalized linear exponential (RTGLE) distribution by using the technique of Balakrishnan and He. The family of RTGLE distributions is more flexible to fit the data sets with IFR, DFR, and BTFR, and also generalizes several well-known models as well as some new record-based transmuted models. This paper aims to study the statistical properties of RTGLE distribution, like, the shape of the probability density function and hazard function, quantile function and its applications, moments and its generating function, order and record statistics, Rényi entropy. The maximum likelihood estimators, least squares and weighted least squares estimators, Anderson-Darling estimators, Cramér-von Mises estimators of the unknown parameters are constructed and their biases and mean squared errors are reported via Monte Carlo simulation study. Finally, the real data sets illustrate the goodness of fit and applicability of the proposed distribution; hence, suitable recommendations are forwarded. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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20. Locally Anisotropic Nonstationary Covariance Functions on the Sphere.
- Author
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Cao, Jian, ZHANG, Jingjie, SUN, Zhuoer, and Katzfuss, Matthias
- Subjects
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GEOSPATIAL data , *FLEXIBLE structures , *INFERENTIAL statistics , *STATISTICAL correlation , *BIAS correction (Topology) - Abstract
Rapid developments in satellite remote-sensing technology have enabled the collection of geospatial data on a global scale, hence increasing the need for covariance functions that can capture spatial dependence on spherical domains. We propose a general method of constructing nonstationary, locally anisotropic covariance functions on the sphere based on covariance functions in R 3 . We also provide theorems that specify the conditions under which the resulting correlation function is isotropic or axially symmetric. For large datasets on the sphere commonly seen in modern applications, the Vecchia approximation is used to achieve higher scalability on statistical inference. The importance of flexible covariance structures is demonstrated numerically using simulated data and a precipitation dataset. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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21. Reduced implication-bias logic loss for neuro-symbolic learning.
- Author
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He, Hao-Yuan, Dai, Wang-Zhou, and Li, Ming
- Subjects
LOGIC ,MACHINE learning ,FUZZY logic ,KNOWLEDGE base ,BIAS correction (Topology) ,DIFFERENTIABLE dynamical systems - Abstract
Integrating logical reasoning and machine learning by approximating logical inference with differentiable operators is a widely used technique in the field of Neuro-Symbolic Learning. However, some differentiable operators could introduce significant biases during backpropagation, which can degrade the performance of Neuro-Symbolic systems. In this paper, we demonstrate that the loss functions derived from fuzzy logic operators commonly exhibit a bias, referred to as Implication Bias. To mitigate this bias, we propose a simple yet efficient method to transform the biased loss functions into Reduced Implication-bias Logic Loss (RILL). Empirical studies demonstrate that RILL outperforms the biased logic loss functions, especially when the knowledge base is incomplete or the supervised training data is insufficient. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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22. The Impact of Top Incomes Biases on the Measurement of Inequality in the United States.
- Author
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Hlasny, Vladimir and Verme, Paolo
- Subjects
BIAS correction (Topology) ,INCOME inequality ,DEMOGRAPHIC surveys ,EQUALITY ,ESTIMATION bias ,BELL'S theorem - Abstract
The paper assesses how top incomes biases affect the estimation of income inequality in the United States using the Current Population Survey (the March Annual Social and Economic Supplement) and two alternative correction methods – a stochastic approach based on reweighting and a semi‐parametric approach based on replacing observations. Consistently with previous studies, both methods and their joint application show that income inequality in the United States between 1979 and 2014 has been consistently underestimated by several percentage points. The level of underestimation is positively and significantly associated with mean income, non‐response rates and the initial level of inequality. Reweighting is found to address top incomes biases – specifically those related to unit and item non‐response – consistently and more effectively than replacing, possibly because widely used parametric distributions do not represent US top incomes accurately. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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23. Asymptotic properties of Spearman's footrule and Gini's gamma in bivariate normal model.
- Author
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Chen, Changrun, Xu, Weichao, Zhang, Weifeng, Zhu, Hongbin, and Dai, Jisheng
- Subjects
- *
ASYMPTOTIC efficiencies , *TIME delay estimation , *STATISTICAL correlation , *SIGNAL processing , *BIAS correction (Topology) - Abstract
This paper aims at rejuvenating the two rank correlation coefficients, Spearman's footrule (SF) and Gini's gamma (GG), which were forgotten in the literature for a long time due to lack of knowledge concerning their statistical properties. Under the common bivariate normal model, we establish the asymptotic analytical expressions of the mean and variance of SF and GG, and investigate the performances of SF and GG from the aspects of biased effect, approximate variance and asymptotic relative efficiency (ARE). Moreover, we further study the robustness of SF and GG under contaminated normal models. In order to get a deeper understanding of their performances, we also compare SF and GG with Kendall's tau (KT) and Spearman's rho (SR), the most widely used rank correlation coefficients, in terms of bias and mean square error (MSE) under both the normal and contaminated normal models. Finally we show an application of SF and GG in the field of signal processing through the example of time-delay estimation. Simulation results indicate that SF and GG outperform SR and KT in some cases. The new findings discovered in this paper enable SF and GG to play complementary roles to KT and SR in practice. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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24. 基于深度学习的参考作物蒸散量预测模型.
- Author
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潘振华, 刘子菡, 沈 欣, 张钟莉莉, 史凯丽, and 张石锐
- Subjects
DEEP learning ,PREDICTION models ,METEOROLOGICAL stations ,SENSITIVITY analysis ,CONSTRUCTION costs ,BIAS correction (Topology) - Abstract
Copyright of Journal of Shanxi Agricultural Sciences is the property of Shanxi Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2023
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25. Image‐based scatter correction for cone‐beam CT using flip swin transformer U‐shape network.
- Author
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Zhang, Xueren, Jiang, Yangkang, Luo, Chen, Li, Dengwang, Niu, Tianye, and Yu, Gang
- Subjects
TRANSFORMER models ,CONE beam computed tomography ,CONVOLUTIONAL neural networks ,IMAGE-guided radiation therapy ,STANDARD deviations ,IMAGE reconstruction algorithms ,BIAS correction (Topology) - Abstract
Background: Cone beam computed tomography (CBCT) plays an increasingly important role in image‐guided radiation therapy. However, the image quality of CBCT is severely degraded by excessive scatter contamination, especially in the abdominal region, hindering its further applications in radiation therapy. Purpose: To restore low‐quality CBCT images contaminated by scatter signals, a scatter correction algorithm combining the advantages of convolutional neural networks (CNN) and Swin Transformer is proposed. Methods: In this paper a scatter correction model for CBCT image, the Flip Swin Transformer U‐shape network (FSTUNet) model, is proposed. In this model, the advantages of CNN in texture detail and Swin Transformer in global correlation are used to accurately extract shallow and deep features, respectively. Instead of using the original Swin Transformer tandem structure, we build the Flip Swin Transformer Block to achieve a more powerful inter‐window association extraction. The validity and clinical relevance of the method is demonstrated through extensive experiments on a Monte Carlo (MC) simulation dataset and frequency split dataset generated by a validated method, respectively. Result: Experimental results on the MC simulated dataset show that the root mean square error of images corrected by the method is reduced from over 100 HU to about 7 HU. Both the structural similarity index measure (SSIM) and the universal quality index (UQI) are close to 1. Experimental results on the frequency split dataset demonstrate that the method not only corrects shading artifacts but also exhibits a high degree of structural consistency. In addition, comparison experiments show that FSTUNet outperforms UNet, Deep Residual Convolutional Neural Network (DRCNN), DSENet, Pix2pixGAN, and 3DUnet methods in both qualitative and quantitative metrics. Conclusions: Accurately capturing the features at different levels is greatly beneficial for reconstructing high‐quality scatter‐free images. The proposed FSTUNet method is an effective solution to CBCT scatter correction and has the potential to improve the accuracy of CBCT image‐guided radiation therapy. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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26. RF linearity trade-offs for varying T-gate geometries of GaN HEMTs on Si.
- Author
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ElKashlan, Rana, Khaled, Ahmad, Rodriguez, Raul, Sibaja-Hernandez, Arturo, Peralagu, Uthayasankaran, Alian, AliReza, Collaert, Nadine, Wambacq, Piet, and Parvais, Bertrand
- Subjects
GALLIUM nitride ,MODULATION-doped field-effect transistors ,BIAS correction (Topology) ,GEOMETRY ,MICROWAVE measurements ,CHARGE carrier mobility - Abstract
Short-channel Gallium Nitride (GaN) high-electron-mobility transistors (HEMTs) often utilize T-shape gates due to their large gate-line cross-sectional area and subsequent f
MAX increase. In this paper, we report the linearity trade-offs associated with varying the T-gate geometries of AlGaN/GaN HEMTs on Si, specifically the gate extensions which serve as field plates and their impact on the large-signal performance. Small-signal characterization and modeling, in addition to TCAD, provide initial guidelines for the optimal dimensions for the gate field plates using the ratio of fT and the product of the gate resistance and the gate-to-drain capacitance. We utilize various characterization methods, including 6 GHz non-linear vector network analyzer characterization in addition to load-pull, to quantify the amplitude and phase distortion and their subsequent impact on the large-signal metrics of the devices under differing matching conditions and bias points. We deduce that the influence of the gate field plates on the amplitude and phase distortion is non-negligible, particularly under matched conditions. [ABSTRACT FROM AUTHOR]- Published
- 2023
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- View/download PDF
27. Robust graph representation clustering based on adaptive data correction.
- Author
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Guo, Li, Zhang, Xiaoqian, Zhang, Rui, Wang, Qian, Xue, Xuqian, and Liu, Zhigui
- Subjects
REPRESENTATIONS of graphs ,DATA scrubbing ,UNDIRECTED graphs ,BIAS correction (Topology) ,WEIGHTED graphs - Abstract
Impressive performance has been achieved when learning graphs from data in clustering tasks. However, real data often contain considerable noise, which leads to unreliable or inaccurate constructed graphs. In this paper, we propose adaptive data correction-based graph clustering (ADCGC), which can be used to adaptively remove errors and noise from raw data and improve the performance of clustering. The ADCGC method mainly contains three advantages. First, we design the weighted truncated Schatten p-norm (WTSpN) instead of the nuclear norm to recover the low-rank clean data. Second, we choose clean data samples that represent the essential properties of the data as the vertices of the undirected graph, rather than using all the data feature points. Third, we adopt the block-diagonal regularizer to define the edge weights of the graph, which helps to learn an ideal affinity matrix and improve the performance of clustering. In addition, an efficient iterative scheme based on the generalized soft-thresholding operator and alternating minimization is developed to directly solve the nonconvex optimization model. Experimental results show that ADCGC both quantitatively and visually outperforms existing advanced methods. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
28. Probability-guaranteed state estimation for nonlinear delayed systems under mixed attacks.
- Author
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Yi, Xiaojian, Yu, Huiyang, Fang, Ziying, and Ma, Lifeng
- Subjects
NONLINEAR estimation ,TIME delay estimation ,NONLINEAR systems ,HATE crimes ,CYBERTERRORISM ,TIME-varying systems ,BIAS correction (Topology) - Abstract
In this paper, the problem of the networked set-membership state estimation is discussed for a class of nonlinear discrete time-varying systems subject to cyber attacks and time delays. Two forms of malicious attacks (i.e. Denial-of-Service (DoS) attack and bias injection attack) are taken into account to describe the adversary's attempt to destroy/deteriorate the system performance via communication network. It is the aim of the investigated issue to propose a set-membership state estimator ensuring the required estimation performance despite the existence of both the external mixed attacks and internal time delays. By resorting to the feasibility of a series of matrix inequalities, sufficient conditions are provided for the solvability of the addressed state estimator design problem. Furthermore, an optimisation strategy is developed with the purpose of seeking the local optimal estimator parameters. At last, a numerical simulation example is presented to demonstrate the effectiveness of the proposed theoretical algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
29. Improving the bias of a pseudo-maximum likelihood estimate of the extreme value index by k-records.
- Author
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Louzaoui, Abderrahim and El Arrouchi, Mohamed
- Subjects
MAXIMUM likelihood statistics ,BIAS correction (Topology) ,ASYMPTOTIC distribution ,NUMERICAL analysis ,EXTREME value theory - Abstract
The paper focusses on the estimation of the extreme value index in terms of k-records based on a maximum likelihood approach, which is suggested recently by Louzaoui and El Arrouchi (J Probab Stat, 2020). Its asymptotic normality is well investigated in order to propose a bias correction while ensuring that the new estimator becomes asymptotically unbiased and still normal. Some numerical studies are also provided in order to show how the proposed estimators behave in practice. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
30. Future changes in the risk of compound hot and dry events over China estimated with two large ensembles.
- Author
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Tang, Zhenfei, Yang, Ting, Lin, Xin, Li, Xinxin, Cao, Rong, and Li, Wei
- Subjects
COPULA functions ,GLOBAL warming ,BIAS correction (Topology) - Abstract
Under the context of global warming, compound dry and hot events (CDHEs) will increase and bring serious losses to society and the economy. The projection of CDHEs is of great significance for policy-making and risk assessment. In this paper, two large ensemble simulations, CanESM2-LE and CESM-LE, are used to estimate the risk of extreme CDHEs under different warming scenarios in China. First, the biases of the model in the simulation of the temperature and precipitation over the China region are corrected, and the index of CDHEs is established based on a copula function. The results show that extreme CDHEs will occur more often in China with the increase in global warming and the more severe extreme CDHEs are, the greater the risk will be in the future with higher uncertainties. Events that would be attained once every 50 and 100 years in the current climate from CESM-LE (CAanESM2-LE) will be 1.2/1.6 (1.1/1.5) times and 1.3/2.3 (1.5/2.0) times more likely to occur in a 1.5°C/2.0°C warmer climate, respectively. Northwestern China will experience the greatest increase in the risk of extreme CDHEs. Extreme CDHEs expected once every 100 years in the current period over NW China are expected to occur approximately every 5 and 4 years under a 4.0°C warmer world in CanESM2-LE and CESM-LE, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
31. Testing Several Rival Models Using the Extension of Vuong's Test and Quasi Clustering.
- Author
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Sayyareh, Abdolreza
- Subjects
HOMOGENEITY ,MULTIVARIATE analysis ,AKAIKE information criterion ,RANDOM variables ,BIAS correction (Topology) - Abstract
The two main goals in model selection are firstly introducing an approach to test homogeneity of several rival models and secondly selecting a set of reasonable models or estimating the best rival model to the true one. In this paper we extend Vuong's method for several models to cluster them. Based on the working paper of Katayama (2008), we propose an approach to test whether rival models have expected relations. The multivariate extension of Vuong's test gives the opportunity to examine some hypotheses about the rival models and their relations with respect to the unknown true model. On the other hand, the standard method of model selection provides an implementation of Occam's razor, in which parsimony or simplicity is balanced against goodness of fit. Therefore, we are interested in clustering the rival models based on their divergence from the true model to select a suitable set of rival models. In this paper we have introduced two approaches to select suitable sets of rival models based on the multivariate extension of Vuong's test and quasi clustering approach. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
32. FCDS-DETR: detection transformer based on feature correction and double sampling.
- Author
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Wang, Min, Jiao, Zhiqiang, Huang, Zhanhua, and Yu, Shihang
- Subjects
- *
BIAS correction (Topology) , *PROBLEM solving , *POINT set theory - Abstract
The recently proposed semantic-aligned matching detection transformer (SAM–DETR model) accelerates the convergence of the detection transformer (DETR) by mapping object queries into an identical embedding space as the encoder's output feature map. However, SAM–DETR model has the problem of low detection accuracy compared to other DETR variants. We observe that the lower detection accuracy of SAM–DETR model is caused by the insufficient number of sample points and the inaccurate localization of the sample points during re-sampling, which blurs the generated attention map. This paper proposes an object detector based on a feature correction and double sampling DETR (FCDS-DETR) to solve this problem. FCDS-DETR takes SAM–DETR model as a baseline and builds on it by adding a feature correction module and a double sampling mechanism to achieve further improvement in detection accuracy with a limited number of additional parameters without sacrificing convergence speed. Firstly, FCDS-DETR improves the sampling point localization accuracy by adding a feature correction module to model the inter-channel dependence of the feature maps to be sampled. Secondly, the number of sampled points is increased by the double sampling mechanism, and attention fusion is used to fuse the attention weight maps corresponding to the two sets of sampled points to improve the recognizability of the attention weight maps. The experimental results show that the average precision is improved by +0.7 on the COCO dataset compared with the SAM–DETR model, and the number of parameters is increased by only 10.34 % , which improves the detection performance of the model very well. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
33. 开都河源区径流量演变规律及影响因素分析.
- Author
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郑灵巧, 邢坤, 刘思海, and 郭春红
- Subjects
EFFECT of human beings on climate change ,ELASTICITY (Economics) ,WAVELETS (Mathematics) ,RUNOFF ,CLIMATE change ,CURVES ,BIAS correction (Topology) - Abstract
Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2023
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34. BDS-3 Triple-Frequency Timing Group Delay/Differential Code Bias and Its Effect on Positioning.
- Author
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Du, Yanjun, Yang, Yuanxi, Jia, Xiaolin, Yao, Wanqiang, Li, Jiahao, and Li, Qin
- Subjects
BEIDOU satellite navigation system ,GLOBAL Positioning System ,BIAS correction (Topology) ,AMBIGUITY - Abstract
BeiDou Global Navigation Satellite System (BDS-3) broadcasts multifrequency signals that offer more choices of frequencies and more signal combinations for positioning. This paper analyzes the effect of timing group delay (TGD) and differential code bias (DCB) of BDS-3 on the corresponding triple-frequency positioning. The triple-frequency observation models of BDS-3 are summarized and the DCB correction models are derived for the four different frequency combinations of triple-frequency ionospheric-free (IF) combination (IF123), two dual-frequency IF combinations (IF1213) and triple-frequency uncombined (UC123) positioning modes. Standard point positioning (SPP) and precise point positioning (PPP) experiments were conducted using 30 days of observations from 25 multi-GNSS experiment (MGEX) stations. The results show that the TGD/DCB correction has a significant impact on the accuracy of SPP. The positioning accuracy using IF123 and IF1213 models improved by about 73~90% after TGD correction, in comparison to a 27~30% improvement achieved using the UC123 model. In addition, the correction effect of DCB is slightly better than TGD. The DCB correction significantly improves accuracy in the initial epoch of the PPP, which helps the convergence of the filtering and reduces the convergence time. The average convergence times of IF123, IF1213 and UC123 are 26.1, 26.9 and 38.3 min, respectively, which are reduced by 6.79, 2.54 and 8.59% with DCB correction. The pseudorange residuals are closer to zero-mean random noise after DCB correction. Furthermore, the DCB affects the evaluation of the inter-frequency bias (IFB), ionospheric delay and floating ambiguity parameters. However, the tropospheric delay is almost unaffected by DCB. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
35. Multiplicative Noise Removal and Contrast Enhancement for SAR Images Based on a Total Fractional-Order Variation Model.
- Author
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Zhou, Yamei, Li, Yao, Guo, Zhichang, Wu, Boying, and Zhang, Dazhi
- Subjects
BIAS correction (Topology) ,IMAGE intensifiers ,DISCRETE Fourier transforms ,NOISE - Abstract
In this paper, we propose a total fractional-order variation model for multiplicative noise removal and contrast enhancement of real SAR images. Inspired by the high dynamic intensity range of SAR images, the full content of the SAR images is preserved by normalizing the original data in this model. Then, we propose a degradation model based on the nonlinear transformation to adjust the intensity of image pixel values. With MAP estimator, a corresponding fidelity term is introduced into the model, which is beneficial for contrast enhancement and bias correction in the denoising process. For the regularization term, a gray level indicator is used as a weighted matrix to make the model adaptive. We first apply the scalar auxiliary variable algorithm to solve the proposed model and prove the convergence of the algorithm. By virtue of the discrete Fourier transform (DFT), the model is solved by an iterative scheme in the frequency domain. Experimental results show that the proposed model can enhance the contrast of natural and SAR images while removing multiplicative noise. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
36. Tuning Random Forests for Causal Inference under Cluster-Level Unmeasured Confounding.
- Author
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Suk, Youmi and Kang, Hyunseung
- Subjects
CAUSAL inference ,RANDOM forest algorithms ,MACHINE learning ,BIAS correction (Topology) ,LOGISTIC regression analysis ,CONFOUNDING variables - Abstract
Recently, there has been growing interest in using machine learning methods for causal inference due to their automatic and flexible ability to model the propensity score and the outcome model. However, almost all the machine learning methods for causal inference have been studied under the assumption of no unmeasured confounding and there is little work on handling omitted/unmeasured variable bias. This paper focuses on a machine learning method based on random forests known as Causal Forests and presents five simple modifications for tuning Causal Forests so that they are robust to cluster-level unmeasured confounding. Our simulation study finds that adjusting the default tuning procedure with the propensity score from fixed effects logistic regression or using variables that are centered to their cluster means produces estimates that are more robust to cluster-level unmeasured confounding. Also, when these parametric propensity score models are mis-specified, our modified machine learning methods remain robust to bias from cluster-level unmeasured confounders compared to existing parametric approaches based on propensity score weighting. We conclude by demonstrating our proposals in a real data study concerning the effect of taking an eighth-grade algebra course on math achievement scores from the Early Childhood Longitudinal Study. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
37. On Fitting the Lomax Distribution: A Comparison between Minimum Distance Estimators and Other Estimation Techniques.
- Author
-
Nombebe, Thobeka, Allison, James, Santana, Leonard, and Visagie, Jaco
- Subjects
MAXIMUM likelihood statistics ,MOMENTS method (Statistics) ,BIAS correction (Topology) - Abstract
In this paper, we investigate the performance of a variety of frequentist estimation techniques for the scale and shape parameters of the Lomax distribution. These methods include traditional methods such as the maximum likelihood estimator and the method of moments estimator. A version of the maximum likelihood estimator adjusted for bias is included as well. Furthermore, an alternative moment-based estimation technique, the L-moment estimator, is included, along with three different minimum distance estimators. The finite sample performances of each of these estimators are compared in an extensive Monte Carlo study. We find that no single estimator outperforms its competitors uniformly. We recommend one of the minimum distance estimators for use with smaller samples, while a bias-reduced version of maximum likelihood estimation is recommended for use with larger samples. In addition, the desirable asymptotic properties of traditional maximum likelihood estimators make them appealing for larger samples. We include a practical application demonstrating the use of the described techniques on observed data. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
38. A method of credit evaluation modeling based on block-wise missing data.
- Author
-
Lan, Qiujun and Jiang, Shan
- Subjects
FEATURE selection ,ALGORITHMS ,EVALUATION methodology ,LOGISTIC regression analysis ,ELECTRONIC data processing ,BIAS correction (Topology) ,MISSING data (Statistics) - Abstract
Missing data is a common problem in credit evaluation practice and can obstruct the development and application of an evaluation model. Block-wise missing data is a particularly troublesome issue. Based on multi-task feature selection approach, this paper proposes a method called MMPFS to build a model for credit evaluation that primarily includes two steps: (1) dividing the dataset into several nonoverlapping subsets based on missing patterns, and (2) integrating the multi-task feature selection approach using logistic regression to perform joint feature learning on all subsets. The proposed method has the following advantages: (1) missing data do not need to be managed in advance, (2) available data can be fully used for model learning, (3) information loss or bias caused by general missing data processing methods can be avoided, and (4) overfitting risk caused by redundant features can be reduced. The implementation framework and algorithm principle of the proposed method are described, and three credit datasets from UCI are investigated to compare the proposed method with other commonly used missing data treatments. The results show that MMPFS can produce a better credit evaluation model than data preprocessing methods, such as sample deletion and data imputation. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
39. Comparison of different estimation methods for extreme value distribution.
- Author
-
Yılmaz, Asuman, Kara, Mahmut, and Özdemir, Onur
- Subjects
DISTRIBUTION (Probability theory) ,MARKOV chain Monte Carlo ,MAXIMUM likelihood statistics ,EXTREME value theory ,BIAS correction (Topology) ,LEAST squares ,GIBBS sampling - Abstract
The extreme value distribution was developed for modeling extreme-order statistics or extreme events. In this study, we discuss the distribution of the largest extreme. The main objective of this paper is to determine the best estimators of the unknown parameters of the extreme value distribution. Thus, both classical and Bayesian methods are used. The classical estimation methods under consideration are maximum likelihood estimators, moment's estimators, least squares estimators, and weighted least squares estimators, percentile estimators, the ordinary least squares estimators, best linear unbiased estimators, L-moments estimators, trimmed L-moments estimators, and Bain and Engelhardt estimators. We also propose new estimators for the unknown parameters. Bayesian estimators of the parameters are derived by using Lindley's approximation and Markov Chain Monte Carlo methods. The asymptotic confidence intervals are considered by using maximum likelihood estimators. The Bayesian credible intervals are also obtained by using Gibbs sampling. The performances of these estimation methods are compared with respect to their biases and mean square errors through a simulation study. The maximum daily flood discharge (annual) data sets of the Meriç River and Feather River are analyzed at the end of the study for a better understanding of the methods presented in this paper. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
40. Methodological limitations in studies assessing the effects of environmental and socioeconomic variables on the spread of COVID-19: a systematic review.
- Author
-
Barceló, Maria A. and Saez, Marc
- Subjects
SOCIOECONOMIC factors ,COVID-19 pandemic ,ERRORS-in-variables models ,AIR pollutants ,SOCIAL contact ,STATISTICAL bias ,BIAS correction (Topology) - Abstract
Background: While numerous studies have assessed the effects of environmental (meteorological variables and air pollutants) and socioeconomic variables on the spread of the COVID-19 pandemic, many of them, however, have significant methodological limitations and errors that could call their results into question. Our main objective in this paper is to assess the methodological limitations in studies that evaluated the effects of environmental and socioeconomic variables on the spread of COVID-19. Main body: We carried out a systematic review by conducting searches in the online databases PubMed, Web of Science and Scopus up to December 31, 2020. We first excluded those studies that did not deal with SAR-CoV-2 or COVID-19, preprints, comments, opinion or purely narrative papers, reviews and systematic literature reviews. Among the eligible full-text articles, we then excluded articles that were purely descriptive and those that did not include any type of regression model. We evaluated the risk of bias in six domains: confounding bias, control for population, control of spatial and/or temporal dependence, control of non-linearities, measurement errors and statistical model. Of the 5631 abstracts initially identified, we were left with 132 studies on which to carry out the qualitative synthesis. Of the 132 eligible studies, we evaluated 63.64% of the studies as high risk of bias, 19.70% as moderate risk of bias and 16.67% as low risk of bias. Conclusions: All the studies we have reviewed, to a greater or lesser extent, have methodological limitations. These limitations prevent conclusions being drawn concerning the effects environmental (meteorological and air pollutants) and socioeconomic variables have had on COVID-19 outcomes. However, we dare to argue that the effects of these variables, if they exist, would be indirect, based on their relationship with social contact. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
41. Adaptive scatter kernel deconvolution modeling for cone‐beam CT scatter correction via deep reinforcement learning.
- Author
-
Piao, Zun, Deng, Wenxin, Huang, Shuang, Lin, Guoqin, Qin, Peishan, Li, Xu, Wu, Wangjiang, Qi, Mengke, Zhou, Linghong, Li, Bin, Ma, Jianhui, and Xu, Yuan
- Subjects
- *
DEEP reinforcement learning , *CONE beam computed tomography , *BIAS correction (Topology) , *DEEP learning , *REINFORCEMENT learning , *PHOTON scattering , *SIGNAL-to-noise ratio - Abstract
Background: Scattering photons can seriously contaminate cone‐beam CT (CBCT) image quality with severe artifacts and substantial degradation of CT value accuracy, which is a major concern limiting the widespread application of CBCT in the medical field. The scatter kernel deconvolution (SKD) method commonly used in clinic requires a Monte Carlo (MC) simulation to determine numerous quality‐related kernel parameters, and it cannot realize intelligent scatter kernel parameter optimization, causing limited accuracy of scatter estimation. Purpose: Aiming at improving the scatter estimation accuracy of the SKD algorithm, an intelligent scatter correction framework integrating the SKD with deep reinforcement learning (DRL) scheme is proposed. Methods: Our method firstly builds a scatter kernel model to iteratively convolve with raw projections, and then the deep Q‐network of the DRL scheme is introduced to intelligently interact with the scatter kernel to achieve a projection adaptive parameter optimization. The potential of the proposed framework is demonstrated on CBCT head and pelvis simulation data and experimental CBCT measurement data. Furthermore, we have implemented the U‐net based scatter estimation approach for comparison. Results: The simulation study demonstrates that the mean absolute percentage error (MAPE) of the proposed method is less than 9.72% and the peak signal‐to‐noise ratio (PSNR) is higher than 23.90 dB, while for the conventional SKD algorithm, the minimum MAPE is 17.92% and the maximum PSNR is 19.32 dB. In the measurement study, we adopt a hardware‐based beam stop array algorithm to obtain the scatter‐free projections as a comparison baseline, and our method can achieve superior performance with MAPE < 17.79% and PSNR > 16.34 dB. Conclusions: In this paper, we propose an intelligent scatter correction framework that integrates the physical scatter kernel model with DRL algorithm, which has the potential to improve the accuracy of the clinical scatter correction method to obtain better CBCT imaging quality. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
42. The polynomial-exponential distribution: a continuous probability model allowing for occurrence of zero values.
- Author
-
Chesneau, Christophe, Bakouch, Hassan S., Ramos, Pedro L., and Louzada, Francisco
- Subjects
DISTRIBUTION (Probability theory) ,CONTINUOUS distributions ,MAXIMUM likelihood statistics ,WEIBULL distribution ,STATISTICAL reliability ,LOGNORMAL distribution ,BIAS correction (Topology) - Abstract
This paper deals with a new two-parameter lifetime distribution with increasing, decreasing and constant hazard rate. This distribution allows the occurrence of zero values and involves the exponential, linear exponential and other combinations of Weibull distributions as submodels. Many statistical properties of the distribution are derived. Maximum likelihood estimation of the parameters and a bias corrective approach is investigated with a simulation study for performance of the estimators. Four real data sets are analyzed for illustrative purposes and it is noted that the distribution is a highly alternative to the gamma, Weibull, Lognormal and exponentiated exponential distributions. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
43. Geometric correction of satellite stereo images by DEM matching without ground control points and map projection step: tested on Cartosat-1 images.
- Author
-
Afsharnia, Hamed, Arefi, Hossein, and Abbasi, Madjid
- Subjects
MAP projection ,REMOTE-sensing images ,STEREO image ,IMAGE registration ,BIAS correction (Topology) ,GEOMETRIC modeling - Abstract
Ground Control Points (GCPs) are needed in most geometric processing of satellite images. Generally used geometric model is based on the use of rational polynomials. The Coefficients of Rational Polynomials (RPCs) are provided by image vendors which are contaminated by some biases. In this paper, a no-GCP bias correction method for RPCs of satellite stereo images is introduced. The method uses global DEMs as ground control information: First, a point cloud is generated from stereo pair, then using the DEM matching strategy it is aligned to the global DEM for estimation of 3D rigid transformation parameters. This transformation is performed with our originally developed method which separates three planimetric parameters from three leveling parameters. They are then employed for bias correction in object space. For DEM matching and also for bias correction, we developed new formulae given directly in geodetic longitude and latitude format, instead of Cartesian map projection coordinates. Numerical results of this research are reported in two categories: with or without (1) GCPs and (2) map projection step. Experiments on two Cartosat-1 stereo pairs show in category (1), improvement in geopositioning accuracy from 399.2 m and 124.0 m to 7.6 m and 2.6 m, respectively. In category (2), we observed RMS improvement in both datasets and in all components up to 3.1 m in longitude, 6.8 m in latitude and 1.2 m in height. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
44. Non-gravitational force measurement and correction by a precision inertial sensor of TianQin-1 satellite.
- Author
-
Zhou, An-Nan, Cai, Lin, Xiao, Chun-Yu, Tan, Ding-Yin, Li, Hong-Yin, Bai, Yan-Zheng, Zhou, Ze-Bing, and Luo, Jun
- Subjects
ORBIT determination ,DETECTORS ,ORBITS of artificial satellites ,GRAVITATIONAL waves ,BIAS correction (Topology) - Abstract
Non-gravitational force models are critical not only for the applications of satellite orbit determination and prediction, but also for the studies of gravitational reference sensors in space-based gravitational wave detection missions and accelerometers in gravity satellite missions. In this paper, based on the inertial sensor data from the TianQin-1 (TQ-1) mission, a correction has been made in the non-gravitational force models by applying additional terms related to the orbital periods. After taking into account this correction, about 37 hours of TQ-1 inertial sensor data is calibrated in the sensitive axes, i.e. y - and z -axes, by comparing with the simulated non-gravitational accelerations. It is indicated that the peak-to-peak value of the non-gravitational acceleration correction terms are about 2% and 13% of the measured accelerations in the y - and z -axes, respectively. Within the frequency band below 0.01Â Hz, the root mean square of calibration residual errors in y - and z -axes are suppressed from 1.03 Ă— 10
â'9 and 3.872 Ă— 10â'9  m sâ'2 to 8.14 Ă— 10â'10 and 1.343 Ă— 10â'9  m sâ'2 , respectively. The bias and scale factor of the inertial sensor are also obtained from the calibration by the method of least-squares fit. Meanwhile, the inertial sensor measurements are validated and their signal compositions are analyzed. [ABSTRACT FROM AUTHOR]- Published
- 2022
- Full Text
- View/download PDF
45. Bootstrap VAR forecasts: The effect of model uncertainties.
- Subjects
BIAS correction (Topology) ,INDUSTRIAL production index ,MONTE Carlo method ,VECTOR autoregression model ,FORECASTING ,DIVIDEND yield - Abstract
VAR models are popular to forecast macroeconomic time series. However, the model, the parameters, and the error distribution are rarely known without uncertainty, so bootstrap methods are applied to deal with these sources of uncertainties. In this paper, the performance of the popular forecast Bonferroni cubes based on the Gaussian method and variants of the bootstrap procedure that incorporate error distribution, parameter uncertainty, bias correction, and lag order uncertainty are compared. Monte Carlo simulations suggest that the best performance of bootstrap cubes are obtained when the parameter uncertainty is considered, being the bias and model uncertainties important for long‐run forecast regions in persistent VAR models. Similar conclusions are found in an empirical application based on a four variate system containing US monthly percent changes of the industrial production index, the S&P500 stock market index, its dividend yield, and the unemployment rate. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
46. Evaluation of Image Features Within and Surrounding Lesion Region for Risk Stratification in Breast Ultrasound Images.
- Author
-
Panigrahi, Lipismita, Verma, Kesari, and Singh, Bikesh Kumar
- Subjects
BIAS correction (Topology) ,BREAST ultrasound ,ULTRASONIC imaging ,COMPUTER-aided diagnosis ,SPECKLE interference ,BREAST imaging ,IMAGE segmentation ,IMAGE representation - Abstract
Feature extraction and classification plays a crucial role in the automated analysis of breast ultrasound (BUS) images. Due to varying sonographic characteristics of benign and malignant lesions, the texture and shape features are mostly employed for designing computer-aided diagnosis (CAD) systems of BUS images. The existing CAD systems use features that are extracted either from the lesion segmented area obtained through segmentation techniques or a rectangular region of interest (ROI) extracted under the guidance of expert Radiologists. However, the significance of features extracted from region comprising only the lesion area is still little explored. This paper investigates the significance of features extracted from the lesion area, lesion surrounding area and rectangular ROI for classification of BUS images. The experiments were conducted on the database of 294 BUS images (104 benign and 190 malignant). Initially, the acquired BUS images were preprocessed through speckle reducing anisotropic diffusion (SRAD) for speckle noise removal. The preprocessed images are segmented using a hybrid segmentation approach including a combination of region-based active contour driven by region-scalable fitting (RBACM-RSF) model and multi-scale Gaussian kernel fuzzy c-means clustering with spatial bias correction (MsGKFCM_S) for getting ROI confined area. The segmented images were further partitioned into two parts (lesion area and lesion surrounding area). Subsequently, a total of 457 texture and shape attributes were extracted from within the lesion area, lesion surrounding area and rectangular ROI comprising of both lesion and its surrounding area. The significance of these features is evaluated using different classifiers (i.e. support vector machine (SVM), Back-propagation artificial neural network (BPANN), Random Forest, AdaBoost). The results indicate that features extracted from within lesion area achieve a maximum classification accuracy of 98.980% with the lowest computational time when linear kernel-based SVM is used. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
47. Citizen rain gauges improve hourly radar rainfall bias correction using a two-step Kalman filter.
- Author
-
Mapiam, Punpim Puttaraksa, Methaprayun, Monton, Bogaard, Thom, Schoups, Gerrit, and Ten Veldhuis, Marie-Claire
- Subjects
RAIN gauges ,KALMAN filtering ,BIAS correction (Topology) ,RADAR ,CORRECTION factors ,TIME series analysis ,CITIZENS - Abstract
The low density of conventional rain gauge networks is often a limiting factor for radar rainfall bias correction. Citizen rain gauges offer a promising opportunity to collect rainfall data at a higher spatial density. In this paper, hourly radar rainfall bias adjustment was applied using two different rain gauge networks: tipping buckets, measured by Thai Meteorological Department (TMD), and daily citizen rain gauges. The radar rainfall bias correction factor was sequentially updated based on TMD and citizen rain gauge data using a two-step Kalman filter to incorporate the two gauge datasets of contrasting quality. Radar reflectivity data from the Sattahip radar station, gauge rainfall data from the TMD, and data from citizen rain gauges located in the Tubma Basin, Thailand, were used in the analysis. Daily data from the citizen rain gauge network were downscaled to an hourly resolution based on temporal distribution patterns obtained from radar rainfall time series and the TMD gauge network. Results show that an improvement in radar rainfall estimates was achieved by including the downscaled citizen observations compared with bias correction based on the conventional rain gauge network alone. These outcomes emphasize the value of citizen rainfall observations for radar bias correction, in particular in regions where conventional rain gauge networks are sparse. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
48. An Innovative Bias-Correction Approach to CMA-GD Hourly Quantitative Precipitation Forecasts.
- Author
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LIU Jin-qing, DAI Guang-feng, and OU Xiao-feng
- Subjects
PRECIPITATION forecasting ,MARINE meteorology ,RAINSTORMS ,FORECASTING ,BIAS correction (Topology) - Abstract
This paper proposes a simple and powerful optimal integration (OPI) method for improving hourly quantitative precipitation forecasts (QPFs, 0-24 h) of a single-model by integrating the benefits of different biascorrected methods using the high-resolution CMA-GD model from the Guangzhou Institute of Tropical and Marine Meteorology of China Meteorological Administration (CMA). Three techniques are used to generate multi-method calibrated members for OPI: deep neural network (DNN), frequency-matching (FM), and optimal threat score (OTS). The results are as follows: (1) The QPF using DNN follows the basic physical patterns of CMA-GD. Despite providing superior improvements for clear-rainy and weak precipitation, DNN cannot improve the predictions for severe precipitation, while OTS can significantly strengthen these predictions. As a result, DNN and OTS are the optimal members to be incorporated into OPI. (2) Our new approach achieves state-of-the-art performances on a single model for all magnitudes of precipitation. Compared with the CMA-GD, OPI improves the TS by 2.5%, 5.4%, 7.8%, 8.3%, and 6.1% for QPFs from clear-rainy to rainstorms in the verification dataset. Moreover, OPI shows good stability in the test dataset. (3) It is also noted that the rainstorm pattern of OPI relies heavily on the original model and that OPI cannot correct for deviations in the location of severe precipitation. Therefore, improvements in predicting severe precipitation using this method should be further realized by improving the numerical model's forecasting capability. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
49. A causal inference approach to measure the vulnerability of urban metro systems.
- Author
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Zhang, Nan, Graham, Daniel J., Hörcher, Daniel, and Bansal, Prateek
- Subjects
CAUSAL inference ,PROPENSITY score matching ,URBANIZATION ,BIAS correction (Topology) ,AUTHORSHIP in literature - Abstract
Transit operators need vulnerability measures to understand the level of service degradation under disruptions. This paper contributes to the literature with a novel causal inference approach for estimating station-level vulnerability in metro systems. The empirical analysis is based on large-scale data on historical incidents and population-level passenger demand. This analysis thus obviates the need for assumptions made by previous studies on human behaviour and disruption scenarios. We develop four empirical vulnerability metrics based on the causal impact of disruptions on travel demand, average travel speed and passenger flow distribution. Specifically, the proposed metrics based on the irregularity in passenger flow distribution extends the scope of vulnerability measurement to the entire trip distribution, instead of just analysing the disruption impact on the entry or exit demand (that is, moments of the trip distribution). The unbiased estimates of disruption impact are obtained by adopting a propensity score matching method, which adjusts for the confounding biases caused by non-random occurrence of disruptions. An application of the proposed framework to the London Underground indicates that the vulnerability of a metro station depends on the location, topology, and other characteristics. We find that, in 2013, central London stations are more vulnerable in terms of travel demand loss. However, the loss of average travel speed and irregularity in relative passenger flows reveal that passengers from outer London stations suffer from longer individual delays due to lack of alternative routes. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
50. Estimating high-spatial resolution surface daily longwave radiation from the instantaneous Global LAnd Surface Satellite (GLASS) longwave radiation product.
- Author
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Zeng, Qi and Cheng, Jie
- Subjects
STANDARD deviations ,RADIATION ,BIAS correction (Topology) - Abstract
In this paper, time extension methods, originally designed for clear-sky land surface conditions, are used to estimate high-spatial resolution surface daily longwave (LW) radiation from the instantaneous Global LAnd Surface Satellite (GLASS) longwave radiation product. The performance of four time methods were first tested by using ground based flux measurements that were collected from 141 global sites. Combined with the accuracy of daily LW radiation estimated from the instantaneous GLASS LW radiation, the linear sine interpolation method performs better than the other methods and was employed to estimate the daily LW radiation as follows: The bias/Root Mean Square Error (RMSE) of the linear sine interpolation method were −6.30/15.10 W/m
2 for the daily longwave upward radiation (LWUP), −1.65/27.63 W/m2 for the daily longwave downward radiation (LWDN), and 4.69/26.42 W/m2 for the daily net longwave radiation (LWNR). We found that the lengths of the diurnal cycle of LW radiation are longer than the durations between sunrise and sunset and we proposed increasing the day length by 1.5 h. The accuracies of daily LW radiation were improved after adjusting the day length. The bias/RMSE were −4.15/13.74 W/m2 for the daily LWUP, −1.3/27.52 W/m2 for the daily LWDN, and 2.85/25.91 W/m2 for the daily LWNR. We are producing long-term surface daily LW radiation values from the GLASS LW radiation product. [ABSTRACT FROM AUTHOR]- Published
- 2021
- Full Text
- View/download PDF
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