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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]

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]

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]

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]

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]

The equation currently reads as: Deviance residual: HT ht and it should be corrected to read as follows: Deviance residual: HT ht Please note that this is an error in the manuscript and is written correctly in the R script used to conduct this analysis; therefore, it does not impact the results. In the paper by Powell-Romero et al. (2023), there was an error in the equation for deviance residuals. Correction to: Improving the predictability and interpretability of co-occurrence modelling through feature-based joint species distribution ensembles. [Extracted from the article]

Estimating the uncertainty in annual riverine constituent loads, which is the mass that passes through a river cross‐section into a receiving water body, using infrequent water quality (WQ) observations is a difficult and unsolved task. Therefore, we propose an unbiased point estimation method and interval estimation method for river loads based on the rating curve (RC) method using importance sampling and the bootstrap method, respectively. In this paper, we first statistically explain the unbiasedness of load estimates using the proposed method. Second, the effectiveness of point and interval estimates by the proposed method is demonstrated for river loads from a small catchment and from large watersheds based on discharge and WQ data of solutes, nutrients, and suspended sediments with 10‐min to daily intervals. The results show that the proposed method provides unbiased estimates and appropriate coverage of confidence intervals regardless of the RC model used. The results also reveal that the dominant cause of bias in load estimates based on ordinary RC methods, such as the Loadest model, is not due to the poor simulation of observed loading rates by the RCs or because of the nonnormality of the regression residuals but rather improper sampling strategies. The proposed method is currently not feasible for WQ monitoring sites in large rivers due to the unmanageability of missing observations or censored data and an inefficient sampling strategy, although the requirement of unbiased estimation explained here can aid in scheduling high‐flow sampling for monitoring sites. Key Points: The unbiased riverine load estimator is theoretically explained based on the rating curve method using importance samplingThe effectiveness of unbiased point estimation and interval estimation is demonstrated for river loads from various watershedsDifficulties in implementing this method at monitoring sites are discussed [ABSTRACT FROM AUTHOR]

The Yangtze River Basin has periodically been subject to torrential rains and floods. It is of great significance to characterize the extreme precipitation patterns to learn about frequent flood characteristics in the Yangtze River Basin. Commonly, spatiotemporal characteristics of extreme precipitation was studied by regional frequency analysis method with site data. Spatial sparse site data may cause imprecise divisions of homogeneous regions. In this paper, the spatiotemporal characteristics of extreme precipitation was studied by regional frequency analysis with corrected satellite‐based grid precipitation data (Global Land Data Assimilation System, GLDAS) rather than site data. The results show that: (1) The corrected GLDAS daily precipitation data had greatly improved its ability to capture extreme precipitation events in Yangtze River Basin, as the data average accuracy increased from 0.215 before correction to 0.849 after correction. It is feasible to use satellite‐based grid precipitation data to replace the site data for the regional frequency analysis of extreme precipitation. (2) The Yangtze River Basin was categorized into seven homogeneous regions for the annual maximum 1‐day (RX1DAY) index with an automatic subjective adjustment method. (3) The regional growth curves and quantiles of the Yangtze River Basin were drawn for the return period for 2–100 years. (4) Spatial patterns of extreme daily precipitation series with a return period of 100 years indicated that the precipitation amount increases gradually from the upper to the lower Yangtze River Basin, from the "arid zone" to the "wet zone" and then to the "special wet zone", and the 100‐year return level of RX1DAY varied from 30.3 to 301.8 mm. There were three main precipitation centres, the Sichuan Basin, Dongting Lake Basin, and a great triangle area covering the Poyang Lake Basin and the south foot of Dabie Mountain. [ABSTRACT FROM AUTHOR]

SUMMARY This paper estimates a dynamic ordered probit model of self-assessed health with two fixed effects: one in the linear index equation and one in the cut-points. This robustly controls for heterogeneity in unobserved health status and in reporting behavior, although we cannot separate both sources of heterogeneity. We find important state dependence effects, and small but significant effects of income and other socioeconomic variables. Having dynamics and flexibly accounting for unobserved heterogeneity matters for those estimates. We also contribute to the bias correction literature in nonlinear panel models by comparing and applying two of the existing proposals to our model. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

ABSTRACT A general circulation model (GCM) is an alternative way for predicting Indian summer monsoon rainfall (ISMR) over the existing empirical/statistical models in recent time. However, the inherent biases present in the GCM affect its performance. Therefore, there is a high requirement for bias correction of the GCM. Few studies on bias correction of GCMs are available in the context of ISMR. A comparative study is reported in this paper on the six different bias correction methods by applying on the hindcast (May start, June-July-August-September) of the climate forecast system (CFS) model from the National Centers for Environmental Prediction (NCEP) for 27 years (1982-2008). Among the six methods discussed in this paper, three methods did not use any statistical transformation (Mean Bias-remove technique (U), Multiplicative shift technique (M) and Standardized-reconstruction technique (Z)) and the remaining three methods used statistical transformation (Regression technique (R), Quantile Mapping Method (Q), Principal Component Regression (PCR)). Finally, it was found that the Standardized-reconstruction technique (Z) and Quantile Mapping Method (Q) are more skilful than the others and both are equally skilful in simulating ISMR. Bias-corrected rainfall in four extreme years, out of which 1988 and 1994 are characterized by excess rainfall and 1987 and 2002 are characterized by deficit, are also examined here. Results indicate that both methods efficiently capture the extreme rainfall cases. [ABSTRACT FROM AUTHOR]

ABSTRACT The biases in the Global Circulation Models ( GCMs) are crucial for understanding future climate changes. Currently, most bias correction methodologies suffer from the assumption that model bias is stationary. This paper provides a non-stationary bias correction model, termed residual-based bagging tree ( RBT) model, to reduce simulation biases and to quantify the contributions of single models. Specifically, the proposed model estimates the residuals between individual models and observations, and takes the differences between observations and the ensemble mean into consideration during the model training process. A case study is conducted for 10 major river basins in Mainland China during different seasons. Results show that the proposed model is capable of providing accurate and stable predictions while including the non-stationarities into the modelling framework. Significant reductions in both bias and root mean squared error are achieved with the proposed RBT model, especially for the central and western parts of China. The proposed RBT model has consistently better performance in reducing biases when compared with the raw ensemble mean, the ensemble mean with simple additive bias correction, and the single best model for different seasons. Furthermore, the contribution of each single GCM in reducing the overall bias is quantified. The single model importance varies between 3.1% and 7.2%. For different future scenarios ( RCP 2.6, RCP 4.5, and RCP 8.5), the results from RBT model suggest temperature increases of 1.44, 2.59, and 4.71 °C by the end of the century, respectively, when compared with the average temperature during 1970-1999. [ABSTRACT FROM AUTHOR]

We examine estimation and inference of the ratio of linear trend slopes between two time series. Our methodological results are motivated by recent empirical climate papers that estimate and test hypotheses about the relative warming rate in the lower-troposphere relative to surface warming, i.e. the amplification ratio. We analyze statistical properties of several estimators and test statistics that allow serial correlation. Relative merits of the estimators and test statistics depend on the magnitude of the trend slopes relative to the noise in the data. We make specific recommendations for practitioners. We revisit the empirical findings of Klotzbach et al. (2009, 2010) and obtain similar results using our recommended approach. [ABSTRACT FROM AUTHOR]

The variability of sea surface temperatures (SSTs) is crucial in climate dynamics, influencing marine ecosystems and human activities. This study leverages graph neural networks (GNNs), specifically a GraphSAGE model, to forecast SSTs and their anomalies (SSTAs), focusing on the global scale structure of climatological data. We introduce an improved graph construction technique for SST teleconnection representation. Our results highlight the GraphSAGE model's capability in 1‐month‐ahead global SST and SSTA forecasting, with SST predictions spanning up to 2 years with a recursive approach. Notably, regions with persistent currents exhibited enhanced SSTA predictability, contrasting with equatorial and Antarctic areas. Our GNN outperformed both the persistence model and traditional methods. Additionally, we offer an SST and SSTA graph data set based on ERA5 and a graph generation tool. This GNN case study has shown how the GraphSAGE can be used in SST and SSTA forecasting, and will provide a foundation for further refinements such as adjusting graph construction, optimizing imbalanced regression techniques for extreme SSTAs, and integrating GNNs with other temporal pattern learning methods to improve long‐term predictions. Plain Language Summary: Have you ever wondered how scientists predict climate changes like warming oceans or unusual weather patterns? Our team is improving these predictions by focusing on sea surface temperatures (SSTs) and SST anomalies (SSTAs)—changes in ocean temperatures that greatly impact our environment and society. Traditionally, scientists use grid‐based machine learning models for these forecasts. We have taken a new approach using graph neural networks (GNNs), which are excellent at learning spatial patterns. This method does not just predict temperatures; it understands how different ocean areas are interconnected. Our technique successfully forecasts SSTs up to 2 years ahead and SSTAs 1 month in advance, particularly in regions with strong ocean currents. This accuracy is crucial for anticipating and preparing for climate impacts on marine and human activities. We have developed tools and data sets for researchers, which will foster advancements in graph‐based climate forecasting. Our findings demonstrate that using appropriate graph re‐sampling and GNNs can help understand the complex climate system. In summary, we are pioneering new ways to predict ocean temperature changes through machine learning, contributing to our understanding of and adaptation to climate change. This work benefits not just scientists, but anyone interested in climate and ocean studies. Key Points: Explores the power of graph representation for global sea surface temperature (SST) and SST anomaly (SSTA) forecastingAddresses the issues with traditional grid‐based models and provides an SST graph data set and a tool for converting SST grid data to graphsShows the efficacy of the GraphSAGE model for global SST and SSTA forecasting and identifies the regions where SSTAs are more predictable [ABSTRACT FROM AUTHOR]

This paper presents the performance analysis of signals from the Galileo satellites in the E1 and E5a frequency bands and GPS L5 signals as measured by DLR's experimental ground-based augmentation system. The results show that the raw noise and multipath level of Galileo signals and of the GPS L5 signals are smaller than that of GPS L1. The new signals are also less sensitive to the choice of carrier-smoothing time constant. Furthermore, the inter-frequency biases that affect dual-frequency processing are investigated. These biases differ between satellites and depend on satellite and receiver hardware, but they can be determined a priori. With known receiver and antenna configurations, it is possible to correct for these biases. A residual uncertainty associated with the bias correction has to be taken into account. This can be modeled as part of the ground and airborne bounding standard deviations ( σ pr_ gnd and σ pr_ air) used in GBAS processing. Copyright © 2017 Institute of Navigation. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the impact of MRI patient‐specific geometrical distortion (PSD) on the quality of Gamma Knife stereotactic radiosurgery (GK‐SRS) plans of the vestibular schwannoma (VS) tumors. Methods and materials: Three open access datasets including the MPI‐Leipzig Mind‐Brain‐Body (318 patients), the slow event‐related fMRI designs dataset (62 patients), and the VS dataset (242 patients) were used. We used first two datasets to train a 3D convolution network to predict the distortion map of third dataset that were then used to calculate and correct the PSD. GK‐SRS plans of VS dataset were used to evaluate dose distribution of PSD‐corrected MRI images. GK‐SRS prescription dose of VS cases was 12 Gy. Geometric and dosimetric discrepancies were assessed between the dose distributions and contours before and after the PSD corrections. Geometry indices were center of the contours, Dice coefficient (DC), Hausdorff distance (HD), and dosimetric indices were Dμ${D_\mu }$, Dmax${D_{max}}$, Dmin${D_{min}}$, and D95%${D_{95{\mathrm{\% }}}}$ doses, target coverage (TC), Paddick's conformity index (PCI), Paddick's gradient index (GI), and homogeneity index (HI). Results: Geometric distortions of about 1.2 mm were observed at the air‐tissue interfaces at the air canal and nasal cavity borders. Average center of the targets was significantly distorted along the frequency encoding direction after the PSD‐correction. Average DC and HD metrics were 0.90 and 2.13 mm. Average Dμ${D_\mu }$, D95%,${D_{95{\mathrm{\% ,}}}}$ and Dmin${D_{min}}$ in Gy significantly increased after PSD correction from 16.85 to 17.25, 12.30 to 12.77, and from 8.98 to 9.92. Dmax${D_{max}}$ did not significantly change after the correction. Average TC and PCI significantly increased from 0.97 to 0.98, and 0.94 to 0.96. Average GI decreased significantly from 2.24 to 2.15 after PSD correction. However, HI did not significantly change after the correction. Conclusion: The proposed method could predict and correct the PSD that indicates the importance of PSD correction before GK‐SRS plans of the VS patients. [ABSTRACT FROM AUTHOR]

A more accurate representation of ice‐phase processes in numerical models necessitates an enhanced understanding of ice‐particle microphysics and their respective formation conditions. Prior in situ measurements have noted distinctive ice‐particle shape characteristics associated with different cloud systems and geographical locations. The recent advancement in ice‐particle backscattering theories enables a more comprehensive exploration of the geographical distribution and seasonal dependence of ice‐particle shape categories than ever before. This exploration is being undertaken for the first time using space‐borne lidar measurements. Distinct geographical preferences were observed for five different ice‐particle categories. Bullets/rosettes were the most common, followed by Voronois, which were especially prevalent in high‐level tropical clouds, and 2D columns, which were commonly found in mid‐ and low‐level clouds. Droxtals were primarily observed in high‐level subtropical regions. The global distribution of ice‐particle types provides valuable insights into the physical processes related to ice cloud particle shape formation, cloud duration, and radiative impacts. Plain Language Summary: An enhanced understanding of ice‐particle microphysics associated with different cloud systems and geographical locations is expected to improve the representation of ice clouds in numerical models for better future climate predictions. For such purpose, space‐borne lidar observations have been intensively used to characterize the global distribution of cloud phases, as well as ice particle types. Latest theoretical studies have indicated the possibility of further decomposing these ice particle types into more specific ice‐particle category information, but had not been applied to actual global observation data. In this work, the geographical distribution of five ice‐particle categories was derived based on the theoretical studies using spaceborne lidar data for the first time. It was found that different ice‐particle categories had a unique geographical preference for occurrence, along with seasonal and height‐dependent characteristics. The global distribution of ice‐particle categories obtained in the present study is expected to be useful for understanding the physical processes related to ice‐particle shape formation and the ice‐particle terminal velocity characteristics relevant to cloud duration. Key Points: For the first time, spaceborne lidar was used to study meticulously the height‐resolved global distribution of five ice‐particle categoriesEach cloud‐particle type displayed a unique geographical preference for occurrence, along with seasonal and height‐dependent characteristicsThe global data on ice‐particle types and particle size from radar‐lidar synergies promises to be valuable for future cloud modeling studies [ABSTRACT FROM AUTHOR]

An important component of the assimilation of radiance observations from the AIRS and IASI satellite instruments is the radiative transfer modelling. Currently, the RTTOV model used in the ECMWF IFS system uses a fixed value for CO 2. Neglecting the spatio-temporal variability of CO 2 introduces an error in the simulation of the satellite radiances, which could affect the quality of the analyses and forecasts. The current assumption is that variational bias correction corrects most of this error and therefore minimizes the impact on the forecast scores. This paper investigates the possibility of modelling CO 2 within the IFS to improve the radiative transfer modelling. Results show that the required bias correction is significantly reduced when using more realistic CO 2 values. The impact on the analysis quality and forecast scores is mostly neutral with some indication of improvement in the Tropics and the stratosphere. [ABSTRACT FROM AUTHOR]

We derive new theoretical results on the properties of the adaptive least absolute shrinkage and selection operator (adaptive lasso) for possibly nonlinear time series models. In particular, we investigate the question of how to conduct inference on the parameters given an adaptive lasso model. Central to this study is the test of the hypothesis that a given adaptive lasso parameter equals zero, which therefore tests for a false positive. To this end, we introduce a recentered bootstrap procedure and show, theoretically and empirically through extensive Monte Carlo simulations, that the adaptive lasso can combine efficient parameter estimation, variable selection, and inference in one step. Moreover, we analytically derive a bias correction factor that is able to significantly improve the empirical coverage of the test on the active variables. Finally, we apply the adaptive lasso and the recentered bootstrap procedure to investigate the relation between the short rate dynamics and the economy, thereby providing a statistical foundation (from a model choice perspective) for the classic Taylor rule monetary policy model. [ABSTRACT FROM AUTHOR]

The species–area relationship (SAR) has over a 150‐year‐long history in ecology, but how its shape and origins vary across scales and organisms remains incompletely understood. This is the first subcontinental freshwater study to examine both these properties of the SAR in a spatially explicit way across major organismal groups (diatoms, insects, and fish) that differ in body size and dispersal capacity. First, to describe the SAR shape, we evaluated the fit of three commonly used models, logarithmic, power, and Michaelis–Menten. Second, we proposed a hierarchical framework to explain the variability in the SAR shape, captured by the parameters of the SAR model. According to this framework, scale and species group were the top predictors of the SAR shape, climatic factors (heterogeneity and median conditions) represented the second predictor level, and metacommunity properties (intraspecific spatial aggregation, γ‐diversity, and species abundance distribution) the third predictor level. We calculated the SAR as a sample‐based rarefaction curve using 60 streams within landscape windows (scales) in the United States, ranging from 160,000 to 6,760,000 km2. First, we found that all models provided good fits (R2 ≥ 0.93), but the frequency of the best‐fitting model was strongly dependent on organism, scale, and metacommunity properties. The Michaelis–Menten model was most common in fish, at the largest scales, and at the highest levels of intraspecific spatial aggregation. The power model was most frequent in diatoms and insects, at smaller scales, and in metacommunities with the lowest evenness. The logarithmic model fit best exclusively at the smallest scales and in species‐poor metacommunities, primarily fish. Second, we tested our framework with the parameters of the most broadly used SAR model, the log–log form of the power model, using a structural equation model. This model supported our framework and revealed that the SAR slope was best predicted by scale‐ and organism‐dependent metacommunity properties, particularly spatial aggregation, whereas the intercept responded most strongly to species group and γ‐diversity. Future research should investigate from the perspective of our framework how shifts in metacommunity properties due to climate change may alter the SAR. [ABSTRACT FROM AUTHOR]

Rear‐edge populations at the warm margin of species distribution are small, isolated and face environmental conditions at the limit of species bioclimatic envelope. Intraspecific phenotypic variation contributing to the persistence of peripheral populations is expected to become increasingly important under future climate conditions to avoid local extirpation where range shifts lag behind climate change velocity.We investigated the putative role of intraspecific phenotypic variation for the maintenance of rear‐edge populations of fire‐prone jack pine (Pinus banksiana), an obligate pyriscent boreal species. We assessed whether variation in cone serotiny is associated with the population trajectory of marginal stands located south of the boreal biome, in the temperate forest where natural wildfires are infrequent and unpredictable. To this end, we estimated stand‐scale serotiny, minimal age and tree size structure in 26 jack pine stands from the rear edge (n = 17 sites) and the core (n = 9 sites) of the species' range in eastern Canada.On average, rear‐edge jack pine populations are less serotinous albeit more variably compared with range‐core populations where serotiny is more uniformly high. Rear‐edge stands are generally older and display reverse J‐shape tree size structure indicative of a multi‐aged demographic equilibrium, whereas range‐core stands are younger and show a unimodal stand structure depicting a single ageing cohort generally lacking interfire recruitment. Eco‐evolutionary dynamics shifts from a dependency on wildfires in range‐core populations to stands that can regenerate and persist without recurrent fires at the rear edge, where stand‐scale serotiny reaches values below 85%.Synthesis. Unlike range‐core populations, rear‐edge jack pine populations can locally rely on a dual life‐history strategy to ensure both steady recruitment during fire‐free intervals and successful postfire regeneration. This capacity to cope with infrequent and unpredictable fire regime should increase the resilience and resistance of jack pine populations as global changes alter fire dynamics of the boreal forest. More generally, unique intraspecific phenotypic variation in rear‐edge populations contributes to long‐term species persistence in marginal environmental conditions that might scale up with global changes. The conservation of rear‐edge populations and their genetic legacy appears crucial for the resilience of species. [ABSTRACT FROM AUTHOR]

Bootstrap-based methods for bias-correcting the first-stage parameter estimates used in some recently developed bootstrap implementations of co-integration rank tests are investigated. The procedure constructs estimates of the bias in the original parameter estimates by using the average bias in the corresponding parameter estimates taken across a large number of auxiliary bootstrap replications. A number of possible implementations of this procedure are discussed and concrete recommendations made on the basis of finite sample performance evaluated by Monte Carlo simulation methods. The results show that bootstrap-based bias-correction methods can significantly improve the small sample performance of the bootstrap co-integration rank tests. [ABSTRACT FROM AUTHOR]

Improving precipitation accuracy over a watershed is one of the highest priorities in water resources studies and management. Several global precipitation datasets are available for estimating precipitation over any region in the world. However, local or regional application of these datasets should account for and correct potential errors in the original products. This article presents a novel spatiotemporal calibration framework to improve the accuracy (bias and correlation) of global precipitation datasets in regional applications. The proposed methodology consists of two steps. First, gridded global precipitation datasets are regressed pointwise against rain gauge data. This yields downscaled and bias‐corrected precipitation values at the point scale. Second, the resulting point‐scale regression parameters are used to build a geostatistical model that predicts the regression parameters across the region of interest, allowing for bias‐correcting the precipitation datasets at the regional scale. The framework is applied to the Urmia Lake Watershed in northwestern Iran. Eight global high‐resolution monthly precipitation datasets (CHIRPS, ERA‐5, IMERG, PERSIANN, PERSIANN‐CCS, PERSIANN‐CDR, TRMM and Terra) are evaluated and three downscaling approaches including linear, Q‐Q and Linear Scaling (LS) regression methods are used to calibrate the precipitation datasets based on a regional network of rain gauge observations. Ordinary kriging is subsequently used to predict the regression parameters at ungauged locations. Out of all combinations (i.e., eight datasets and three methods), downscaled IMERG using linear and Q‐Q regression methods showed the best performance in estimating the spatiotemporal variations of monthly precipitation across the watershed of interest. The original IMERG dataset overestimated the monthly precipitation by approximately 20% compared to the precipitation from rain gauges. After applying the proposed methodology in this article, the IMERG bias was reduced by 93%, with an additional 26% decrease in the RMSE. [ABSTRACT FROM AUTHOR]

In this article, we revisit the problem of how to construct better nonparametric confidence intervals for the conditional quantile function from an optimization perspective. We apply the fully data‐driven bias correction procedure based on local polynomial smoothing to estimate the conditional quantile. To account for the effect of the estimated bias, we apply an asymptotic framework that the ratio of the bandwidth to the pilot bandwidth tends to some positive constant rather than zero as the sample size grows. We derive an alternative asymptotic normality of the proposed bias‐corrected quantile estimator as well as a new asymptotic variance formula. Based on theoretical results, two new pointwise confidence intervals are constructed through resampling strategies. Extensive simulation studies show that our proposed confidence intervals enjoy better performance than other competitors in terms of coverage probabilities and interval lengths and are not sensitive to the choice of bandwidth. Finally, our proposed procedure is further illustrated through United States' natality birth data in 2017. [ABSTRACT FROM AUTHOR]

Estimates of effective parameters for unsaturated flow models are typically based on observations taken on length scales smaller than the modeling scale. This complicates parameter estimation for heterogeneous soil structures. In this paper we attempt to account for soil structure not present in the flow model by using so-called external error models, which correct for bias in the likelihood function of a parameter estimation algorithm. The performance of external error models are investigated using data from three virtual reality experiments and one real world experiment. All experiments are multistep outflow and inflow experiments in columns packed with two sand types with different structures. First, effective parameters for equivalent homogeneous models for the different columns were estimated using soil moisture measurements taken at a few locations. This resulted in parameters that had a low predictive power for the averaged states of the soil moisture if the measurements did not adequately capture a representative elementary volume of the heterogeneous soil column. Second, parameter estimation was performed using error models that attempted to correct for bias introduced by soil structure not taken into account in the first estimation. Three different error models that required different amounts of prior knowledge about the heterogeneous structure were considered. The results showed that the introduction of an error model can help to obtain effective parameters with more predictive power with respect to the average soil water content in the system. This was especially true when the dynamic behavior of the flow process was analyzed. [ABSTRACT FROM AUTHOR]

The fidelity of climate projections is often undermined by biases in climate models due to their simplification or misrepresentation of unresolved climate processes. While various bias correction methods have been developed to post‐process model outputs to match observations, existing approaches usually focus on limited, low‐order statistics, or break either the spatiotemporal consistency of the target variable, or its dependency upon model resolved dynamics. We develop a Regularized Adversarial Domain Adaptation (RADA) methodology to overcome these deficiencies, and enhance efficient identification and correction of climate model biases. Instead of pre‐assuming the spatiotemporal characteristics of model biases, we apply discriminative neural networks to distinguish historical climate simulation samples and observation samples. The evidences based on which the discriminative neural networks make distinctions are applied to train the domain adaptation neural networks to bias correct climate simulations. We regularize the domain adaptation neural networks using cycle‐consistent statistical and dynamical constraints. An application to daily precipitation projection over the contiguous United States shows that our methodology can correct all the considered moments of daily precipitation at approximately 1° resolution, ensures spatiotemporal consistency and inter‐field correlations, and can discriminate between different dynamical conditions. Our methodology offers a powerful tool for disentangling model parameterization biases from their interactions with the chaotic evolution of climate dynamics, opening a novel avenue toward big‐data enhanced climate predictions. Plain Language Summary: Accurate climate prediction is crucial for understanding climate change and implementing effective climate adaptation strategies. However, climate models that are used to generate climate predictions have multifaceted biases that often need to be corrected before predictions can be considered usable. We develop a data‐driven methodology that detects and corrects climate model biases using a game theory inspired machine learning technique. By applying physical and statistical constraints, our predictions are not only more accurate, but also more trustworthy as judged by our physical understandings. Key Points: Identify and correct climate projection biases using unpaired climate simulation and observation dataRegularize the data‐driven bias corrector using statistical and dynamical constraintsSignificant improvement of daily precipitation estimation regarding a broad range of spatiotemporal statistics [ABSTRACT FROM AUTHOR]

Aim: Ecological data collected by the general public are valuable for addressing a wide range of ecological research and conservation planning, and there has been a rapid increase in the scope and volume of data available. However, data from eBird or other large‐scale projects with volunteer observers typically present several challenges that can impede robust ecological inferences. These challenges include spatial bias, variation in effort and species reporting bias. Innovation: We use the example of estimating species distributions with data from eBird, a community science or citizen science (CS) project. We estimate two widely used metrics of species distributions: encounter rate and occupancy probability. For each metric, we critically assess the impact of data processing steps that either degrade or refine the data used in the analyses. CS data density varies widely across the globe, so we also test whether differences in model performance are robust to sample size. Main conclusions: Model performance improved when data processing and analytical methods addressed the challenges arising from CS data; however, the degree of improvement varied with species and data density. The largest gains we observed in model performance were achieved with 1) the use of complete checklists (where observers report all the species they detect and identify, allowing non‐detections to be inferred) and 2) the use of covariates describing variation in effort and detectability for each checklist. Occupancy models were more robust to a lack of complete checklists. Improvements in model performance with data refinement were more evident with larger sample sizes. In general, we found that the value of each refinement varied by situation and we encourage researchers to assess the benefits in other scenarios. These approaches will enable researchers to more effectively harness the vast ecological knowledge that exists within CS data for conservation and basic research. [ABSTRACT FROM AUTHOR]

In this study, an innovative MODIS fractional snow cover (SCF) data assimilation (DA) prototype framework that invokes machine learning (ML) techniques and Common land model (CoLM) is proposed to improve the estimation of the snow depth (SD) and the SCF. To validate our new framework, we analyzed two snow seasons from 2013 to 2015 at 46 stations in Northern Xinjiang in China. We developed 12 SCF DA schemes that represent different DA methods (direct insertion (DI) and Ensemble Kalman Filter (EnKF)), observational data (original data and gap‐filled MODIS SCF data), and observation operators (five new snow depletion curves (SDCs) defined using traditional multivariate nonlinear regression and four ML methods). While improving the frequency of the SCF observations in the DI‐based DA scheme only resulted in a marginal improvement in the snow estimates, by adding new SDCs fitted by ML techniques (e.g., deep belief network), and the gap‐filled MODIS SCF data to the EnKF‐based DA scheme, we were able to reduce model structural uncertainties of CoLM and achieve marked improvement in the accuracy of the snow estimates (RMSE = 5.92 cm, mean bias error = −1.94 cm, and average degree of improvement = 32.18% for SD estimates and RMSE = 15. 79%, mean bias error = −1.21%, and average degree of improvement = 47.95% for SCF estimates). Our results demonstrate the feasibility of improving snow estimates by combining the ML techniques with physically based snowpack model in a SCF DA framework. Key Points: An innovative MODIS fractional snow cover assimilation framework invokes machine learning techniques and Common Land Model is proposedFive new snow depletion curves (SDCs) are defined by using multiple nonlinear regression and four machine learning techniquesNew SDCs functions as both the improved parameterization scheme of Common Land Model and the observation operator of assimilation frame [ABSTRACT FROM AUTHOR]

Affected by climate change and high‐intensity human activities, the precipitation series is characterized by nonlinear complex fluctuations. In order to accurately quantify the precipitation complexity, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method was introduced to address the shortcomings of the traditional multifractal detrended fluctuation analysis (MFDFA), and the CEEMDAN‐MFFA method was proposed to explore the spatiotemporal variability and possible causes of precipitation complexity in Northeast China (NEC). The results showed that the traditional MFDFA method generally overestimated precipitation complexity. However, the CEEMDAN‐MFFA method improved the accuracy and reliability of precipitation complexity analysis. Daily precipitation series in NEC were characterized by multifractal with high uncertainty and anti‐persistence. The lowest precipitation complexity was 0.573, which appeared in Qian'an, Jilin Province, and the highest precipitation complexity was 1.108, which appeared in the Boketu, Inner Mongolia. The cities with high complexity of daily precipitation are sporadically distributed in the three provinces of Heilongjiang, Jilin and Liaoning. The average complexity is ~0.892, accounting for 22.5% of the total administrative districts. The complexity of daily precipitation showed a statistically insignificant downward trend with time. The precipitation dynamics structure in the southeast and northwest of the study area was complex, and the precipitation was less predictable than other regions. However, the precipitation dynamics structure was relatively simple and the precipitation was easily predictable in the northeastern, central and southwestern regions. Elevation, topographic relief, changes in water area, forest area and agricultural production were the main influencing factors for the differences in precipitation complexity. Results also revealed that increasing the area of forestland by means of protecting and breeding forests, via regulation of the forest ecosystems to alleviate the complex precipitation process will become a reliable approach to cope with nonstationary precipitation. [ABSTRACT FROM AUTHOR]

Ecologists increasingly rely on complex computer simulations to forecast ecological systems. To make such forecasts precise, uncertainties in model parameters and structure must be reduced and correctly propagated to model outputs. Naively using standard statistical techniques for this task, however, can lead to bias and underestimation of uncertainties in parameters and predictions. Here, we explain why these problems occur and propose a framework for robust inference with complex computer simulations. After having identified that model error is more consequential in complex computer simulations, due to their more pronounced nonlinearity and interconnectedness, we discuss as possible solutions data rebalancing and adding bias corrections on model outputs or processes during or after the calibration procedure. We illustrate the methods in a case study, using a dynamic vegetation model. We conclude that developing better methods for robust inference of complex computer simulations is vital for generating reliable predictions of ecosystem responses. [ABSTRACT FROM AUTHOR]

The continued global biodiversity crisis necessitates the continuation and development of new well‐designed monitoring strategies and action plans with particular focus on under‐represented countries and regions. However, limited resources in terms of budget and availability of qualified field personnel can restrict the geographical coverage of monitoring efforts. Focusing monitoring efforts on a representative subset of species and locations can improve cost‐efficiency. Optimal performance of multi‐species indicators derived from such an approach requires objective methods for species selection and a sampling design that reduces inherent sampling bias caused by regional differences in habitat availability or accessibility. To explore the performance of a multi‐species indicator across different regions within a nation, we developed a multi‐species indicator (MSI) for farmland birds in Norway using objective niche‐based selection of species. We compare the performance of this indicator at national and regional scales (Central and East regions) in Norway, and between urban and rural sites within regions. The seven‐species indicator obtained from the species selection provided similar indicator values and trends for Norway and the Central and East regions, as well as for rural sites within the combined Central + East region. All trends were defined as showing moderate decline from 2007–2016. Urban sites within the combined Central + East region provided trend estimates that showed stronger decline than rural areas in the same region during the time span. Our results emphasize the need to control for sampling bias when structuring monitoring programmes such as a Breeding Bird Survey (BBS). This is especially important if limited resources restrict the geographical coverage of the monitoring scheme. We recommend that monitoring schemes follow a stratified random sampling design that represents both the availability of different land cover types and their distribution with regard to proximity to highly populated areas. If that is not possible, statistically weighting data from different regions or landscapes is likely to be necessary. [ABSTRACT FROM AUTHOR]

Aims: Numerous algorithms have been developed to guide warfarin dosing and improve clinical outcomes. We reviewed the algorithms available for various populations and the covariates, performances and risk of bias of these algorithms. Methods: We systematically searched MEDLINE up to 20 May 2020 and selected studies describing the development, external validation or clinical utility of a multivariable warfarin dosing algorithm. Two investigators conducted data extraction and quality assessment. Results: Of 10 035 screened records, 266 articles were included in the review, describing the development of 433 dosing algorithms, 481 external validations and 52 clinical utility assessments. Most developed algorithms were for dose initiation (86%), developed by multiple linear regression (65%) and mostly applicable to Asians (49%) or Whites (43%). The most common demographic/clinical/environmental covariates were age (included in 401 algorithms), concomitant medications (270 algorithms) and weight (229 algorithms) while CYP2C9 (329 algorithms), VKORC1 (319 algorithms) and CYP4F2 (92 algorithms) variants were the most common genetic covariates. Only 26% and 7% algorithms were externally validated and evaluated for clinical utility, respectively, with <2% of algorithm developments and external validations being rated as having a low risk of bias. Conclusion: Most warfarin dosing algorithms have been developed in Asians and Whites and may not be applicable to under‐served populations. Few algorithms have been externally validated, assessed for clinical utility, and/or have a low risk of bias which makes them unreliable for clinical use. Algorithm development and assessment should follow current methodological recommendations to improve reliability and applicability, and under‐represented populations should be prioritized. [ABSTRACT FROM AUTHOR]

Nonparametric methodologies are proposed to assess college students' performance. Emphasis is given to gender and sector of high school. The application concerns the University of Campinas, a research university in Southeast Brazil. In Brazil college studies are based on a somewhat rigid set of subjects for each major. For this reason a simple GPA comparison may hide true performance. Therefore, we define individual vectors of course grades. These vectors are used in pairwise comparisons of common subject grades for individuals who entered college in the same year. The relative college performances of any two students are compared with their relative performances on the entrance exam score. A procedure based on generalized U‐statistics is developed to test if there is selection bias in the entrance exam by some predefined groups, which is equipped with asymptotically normal distribution under both null and alternative hypotheses. Maximum power is attained by employing the union intersection principle, and resampling techniques such as nonparametric bootstrap are employed to generate the empirical distribution of the test statistics and get p‐values. [ABSTRACT FROM AUTHOR]

Purpose: To assess the repeatability of radiomic features in magnetic resonance (MR) imaging of glioblastoma (GBM) tumors with respect to test–retest, different image registration approaches and inhomogeneity bias field correction. Methods: We analyzed MR images of 17 GBM patients including T1‐ and T2‐weighted images (performed within the same imaging unit on two consecutive days). For image segmentation, we used a comprehensive segmentation approach including entire tumor, active area of tumor, necrotic regions in T1‐weighted images, and edema regions in T2‐weighted images (test studies only; registration to retest studies is discussed next). Analysis included N3, N4 as well as no bias correction performed on raw MR images. We evaluated 20 image registration approaches, generated by cross‐combination of four transformation and five cost function methods. In total, 714 images (17 patients × 2 images × ((4 transformations × 5 cost functions) + 1 test image) and 2856 segmentations (714 images × 4 segmentations) were prepared for feature extraction. Various radiomic features were extracted, including the use of preprocessing filters, specifically wavelet (WAV) and Laplacian of Gaussian (LOG), as well as discretization into fixed bin width and fixed bin count (16, 32, 64, 128, and 256), Exponential, Gradient, Logarithm, Square and Square Root scales. Intraclass correlation coefficients (ICC) were calculated to assess the repeatability of MRI radiomic features (high repeatability defined as ICC ≥ 95%). Results: In our ICC results, we observed high repeatability (ICC ≥ 95%) with respect to image preprocessing, different image registration algorithms, and test–retest analysis, for example: RLNU and GLNU from GLRLM, GLNU and DNU from GLDM, Coarseness and Busyness from NGTDM, GLNU and ZP from GLSZM, and Energy and RMS from first order. Highest fraction (percent) of repeatable features was observed, among registration techniques, for the method Full Affine transformation with 12 degrees of freedom using Mutual Information cost function (mean 32.4%), and among image processing methods, for the method Laplacian of Gaussian (LOG) with Sigma (2.5–4.5 mm) (mean 78.9%). The trends were relatively consistent for N4, N3, or no bias correction. Conclusion: Our results showed varying performances in repeatability of MR radiomic features for GBM tumors due to test–retest and image registration. The findings have implications for appropriate usage in diagnostic and predictive models. [ABSTRACT FROM AUTHOR]

Comparative assessment of the relative information content of different independent spatial data types is necessary to evaluate whether they provide congruent biogeographic signals for predicting species ranges. Opportunistic occurrence records and systematically collected survey data are available from the Dominican Republic for Hispaniola's surviving endemic non‐volant mammals, the Hispaniolan solenodon (Solenodon paradoxus) and Hispaniolan hutia (Plagiodontia aedium); opportunistic records (archaeological, historical and recent) exist from across the entire country, and systematic survey data have been collected from seven protected areas. Species distribution models were developed in maxent for solenodons and hutias using both data types, with species habitat suitability and potential country‐level distribution predicted using seven biotic and abiotic environmental variables. Three different models were produced and compared for each species: (a) opportunistic model, with starting model incorporating abiotic‐only predictors; (b) total survey model, with starting model incorporating biotic and abiotic predictors; and (c) reduced survey model, with starting model incorporating abiotic‐only predictors to allow further comparison with the opportunistic model. All models predict suitable environmental conditions for both solenodons and hutias across a broadly congruent, relatively large area of the Dominican Republic, providing a spatial baseline of conservation‐priority landscapes that might support native mammals. Correlation between total and reduced survey models is high for both species, indicating the substantial explanatory power of abiotic variables for predicting Hispaniolan mammal distributions. However, correlation between survey models and opportunistic models is only moderately positive. Species distribution models derived from different data types can provide different predictions about habitat suitability and conservation‐priority landscapes for threatened species, likely reflecting incompleteness and bias in spatial sampling associated with both data types. Models derived using both opportunistic and systematic data must therefore be applied critically and cautiously. [ABSTRACT FROM AUTHOR]

We introduce a lagged nearest‐neighbour, stationary spatio‐temporal generalized autoregressive conditional heteroskedasticity (GARCH) model on an infinite spatial grid that opens for GARCH innovations in a space‐time ARMA model. This is illustrated by a real data application to a classical dataset of sea surface temperature anomalies in the Pacific Ocean. The model and its translation invariant neighbourhood system are wrapped around a torus forming a model with finite spatial domain, which we call circular spatio‐temporal GARCH. Such a model could be seen as an approximation of the infinite one and simulation experiments show that the circular estimator with a straightforward bias correction performs well on such non‐circular data. Since the spatial boundaries are tied together, the well‐known boundary issue in spatial statistical modelling is effectively avoided. We derive stationarity conditions for these circular processes and study the spatio‐temporal correlation structure through an ARMA representation. We also show that the matrices defined by a vectorized version of the model are block circulants. The maximum quasi‐likelihood estimator is presented and we prove its strong consistency and asymptotic normality by generalizing results from univariate GARCH theory. [ABSTRACT FROM AUTHOR]

The accuracy of gridded precipitation data depends on the availability of a uniformly spaced rain gauge network and an appropriate spatial interpolation method that considers the rainfall variability and other factors that influence the precipitation patterns in the region of interest. In the current study, conceptually superior variants of a widely used spatial interpolation algorithm, Shepard's method, are proposed, formulated and evaluated to overcome one of the major limitations in neighbourhood selection, that is, arbitrary selection of rain gauges. The variants provide mechanisms to objectively select the rain gauges (control points) based on correlation (variant 1), distribution similarity (variant 2) and a combination of both (variant 3). The improved variants were used in the development of gridded rainfall data at a resolution of 5 km over the Kabini River basin in south India, and in the state of Kentucky, United States. Results from multiple experiments using the original Shepard's method and its variants indicate improvements in the accuracy of precipitation estimates. Also, these variants have preserved the site‐specific statistics and distributional characteristics of the rainfall data. A variant 1 that uses a correlation‐based neighbourhood selection criterion performed better for daily and monthly data compared to others and is suitable for generation of gridded rainfall data. The variant 1 when used with information from clustering of sites for selection of the neighbours has led to improvement in gridded precipitation data estimates. The proposed variant 1 can also be used for point data estimation useful for filling missing data at any site. [ABSTRACT FROM AUTHOR]

This study introduces a modified tensor-based power flow method designed using complex voltages represented in their rectangular form. Tensor-based methods employ the second-order Taylor series expansion of the power flow equations to estimate a correction vector to be added to the iterative increments of the Newton-Raphson method. In the proposed approach, the estimation of the correction vector is improved by taking advantage of a relation between the Jacobian and tensor arrays and symmetries between matrix-vector products involving Jacobian matrices. Result analysis highlights the efficiency and robustness of the proposed method in comparison with the original tensor-based approach using test systems and an actual system. [ABSTRACT FROM AUTHOR]

Purpose: Scatter contamination constitutes a dominant source of degradation of image quality in cone‐beam computed tomography (CBCT). We have recently developed an analytic image reconstruction method with a scatter correction capability from the partially blocked cone‐beam data out of a single scan. Despite its easy implementation and its computational efficiency, the developed method may result in additional image artifacts for a large cone angle geometry due to data inconsistency. To improve the image quality at a large cone angle, we propose a weighted rebinned backprojection‐filtration (wrBPF) algorithm in conjunction with a hybrid type scatter correction approach. Methods: The proposed method uses a beam‐blocker array that provides partial data for scatter correction and image reconstruction and that only blocks the beam within a limited cone angle. This design allows a chance to keep the image quality at larger cone angles by use of data redundancy since the projection data corresponding to larger cone angles are not blocked. However, the scatter correction would not be straightforward. In order to correct for the scatter in the projections at larger cone angles, we propose a novel scatter correction method combining a measurement‐based and a convolution‐based method. We first estimated the scatter signal using a measurement‐based method in the partially beam‐blocked regions, and then optimized the fitting parameters of a convolution‐kernel that can be used for scatter correction in the projections at larger cone angles. For image reconstruction, we developed a wrBPF with butterfly filtering. We have conducted an experimental study to validate the proposed algorithm for image reconstruction and scatter correction. Results: The experimental results revealed that the developed reconstruction method makes full use of the benefits of partial beam‐blocking for scatter correction and image reconstruction and at the same time enhances image quality at larger cone angles by use of an optimized convolution‐based scatter correction. Conclusions: The proposed method that enjoys the advantages of both measurement‐based and convolution‐based methods for scatter correction has successfully demonstrated its capability of reconstructing accurate images out of a single scan in circular CBCT. [ABSTRACT FROM AUTHOR]

In this article, local linear estimators are adapted for the unknown infinitesimal coefficients associated with continuous‐time asset return models with jumps, which can correct the bias automatically due to their simple bias representation. The integrated diffusion models with jumps, especially infinite activity jumps, are mainly investigated. In addition, under mild conditions, the weak consistency and asymptotic normality are provided through the conditional Lindeberg theorem as the time span T→∞ and the sample interval Δn→0. Furthermore, our method presents advantages in bias correction through simulation whether jumps belong to the finite activity case or infinite activity case. Finally, the estimators are illustrated empirically through the returns of stock index under 5‐minute high sampling frequency for real application. [ABSTRACT FROM AUTHOR]

Abstract: Bias correction methods are usually applied to climate model outputs before using these outputs for hydrological climate change impact studies. However, the use of a bias correction procedure is debatable, due to the lack of physical basis and the bias nonstationarity of climate model outputs between future and historical periods. The direct use of climate model outputs for impact studies has therefore been recommended in a few studies. This study investigates the possibility of using reanalysis‐driven regional climate model (RCM) outputs directly for hydrological modelling by comparing the performance of bias‐corrected and nonbias‐corrected climate simulations in hydrological simulations over 246 watersheds in the Province of Québec, Canada. When using RCM outputs directly, the hydrological model is specifically calibrated using RCM simulations. Two evaluation metrics (Nash–Sutcliffe efficiency [NSE] and transformed root mean square error [TRMSE]) and three hydrological indicators (mean, high, and low flows) are used as criteria for this comparison. Two reanalysis‐driven RCMs with resolutions of 45 km and 15 km are used to investigate the scale effect of climate model simulations and bias correction approaches on hydrology modelling. The results show that nonbias‐corrected simulations perform better than bias‐corrected simulations for the reproduction of the observed streamflows when using NSE and TRMSE as criteria. The nonbias‐corrected simulations are also better than or comparable with the bias‐corrected simulations in terms of reproducing the three hydrological indicators. These results imply that the raw RCM outputs driven by reanalysis can be used directly for hydrological modelling with a specific calibration of hydrological models using these datasets when gauged observations are scarce or unavailable. The nonbias‐corrected simulations (at a minimum) should be provided to end users, along with the bias‐corrected ones, especially for studying the uncertainty of hydrological climate change impacts. This is especially true when using an RCM with a high resolution, since the scale effect is observed when the RCM resolution increases from a 45‐km to a 15‐km scale. [ABSTRACT FROM AUTHOR]

Abstract: The proposed work introduces a modified method of fuzzy c means (FCM) algorithm using bias field correction and partial supervision techniques. The proposed method is named as bias corrected partial supervision FCM (BCPSFCM). The modified membership function takes the advantage of available knowledge from labeled patterns with the bias field correction. The experiment is tested on internet brain segmentation repository with their gold standard. The performance of the method is compared with three existing methods and 12 state of the art methods using dice coefficient, sensitivity, specificity, and accuracy. Accuracy of the proposed method reached upto 98%, 98%, and 99% of GM, WM, and CSF segmentation but required additional computation power from graphics processing unit (GPU). Further parallel BCPSFCM is proposed with the help of compute unified device architecture enabled GPU machine and the processing time is reduced up to 49 times than the serial implementation. [ABSTRACT FROM AUTHOR]

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Distribution mapping has been identified as the most efficient approach to bias-correct climate model rainfall, while reproducing its statistics at spatial and temporal resolutions suitable to run hydrologic models. Yet its implementation based on empirical distributions derived from control samples (referred to as nonparametric distribution mapping) makes the method's performance sensitive to sample length variations, the presence of outliers, the spatial resolution of climate model results, and may lead to biases, especially in extreme rainfall estimation. To address these shortcomings, we propose a methodology for simultaneous bias correction and high-resolution downscaling of climate model rainfall products that uses: (a) a two-component theoretical distribution model (i.e., a generalized Pareto (GP) model for rainfall intensities above a specified threshold u*, and an exponential model for lower rainrates), and (b) proper interpolation of the corresponding distribution parameters on a user-defined high-resolution grid, using kriging for uncertain data. We assess the performance of the suggested parametric approach relative to the nonparametric one, using daily raingauge measurements from a dense network in the island of Sardinia (Italy), and rainfall data from four GCM/RCM model chains of the ENSEMBLES project. The obtained results shed light on the competitive advantages of the parametric approach, which is proved more accurate and considerably less sensitive to the characteristics of the calibration period, independent of the GCM/RCM combination used. This is especially the case for extreme rainfall estimation, where the GP assumption allows for more accurate and robust estimates, also beyond the range of the available data. [ABSTRACT FROM AUTHOR]

Bedload yields were calculated by 39 methods at the East Tributary gauge, nine methods at Upper Swift Creek gauge and 11 methods at Swift Creek gauge in the Ngarradj Creek catchment in northern Australia. These methods involved combining various significant bedload rating curves determined for a measured bedload data set for a 4-year period with either the hourly or daily hydrographs or flow duration curves for the same period, 1 September 1998 to 31 August 2002. Bedload ratings were both statistically significant ( ρ ≤ 0.05) and explained at least 60% of the variance in bedload flux. Bias corrections were used with all methods based on log10-transformed ratings. Estimated mean annual bedload yields varied by three orders of magnitude at the East Tributary gauge and by two orders of magnitude at Upper Swift Creek and Swift Creek gauges. Hourly discharges usually produced higher estimated yields than daily discharges. The bedload rating-flow duration curve technique overestimates yields and bias correction methods always produce even higher yields. Ratings using both immersed bedload weight and adjusted immersed bedload weight always under-predicted yields because they contain an implicit threshold of motion condition that is at least four times greater than that predicted by Bagnold's threshold equation. Such a result questions the applicability of Bagnold's threshold equation to the Ngarradj Creek catchment. The best estimates of mean annual bedload yield at East Tributary, Upper Swift Creek and Swift Creek gauges are 600 ± 170 (SE), 1065 ± 150 and 1795 ± 270 t/year, respectively. © 2015 Commonwealth of Australia. Hydrological Processes © 2015 John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

A 2D depth-averaged model has been developed for simulating water flow, sediment transport and morphological changes in gravel-bed rivers. The model was validated with a series of laboratory experiments and then applied to the Nove reach of the Brenta River (Northern Italy) to assess its bed material transport, interpret channel response to a series of intensive flood events (R.I. ≈ 10 years) and provide a possible evolutionary scenario for the medium term. The study reach is 1400 m long with a mean slope of 0.0039 m m−1. High-resolution digital terrain models were produced combining LiDAR data with colour bathymetry techniques. Extensive field sedimentological surveys were also conducted for surface and subsurface material. Data were uploaded in the model and the passage of two consecutive high intensity floods was simulated. The model was run under several hypotheses of sediment supply: one considering substantial equilibrium between sediment input and transport capacity, and the others reducing the sediment supply. The sediment supply was then calibrated comparing channel morphological changes as observed in the field and calculated by the model. Annual bed material transport was assessed and compared with other techniques. Low-frequency floods (R.I. ≈ 1.5 years) are expected to produce negligible changes in the channel while high floods may erode banks rather than further incising the channel bed. Location and distribution of erosion and deposition areas within the Nove reach were predicted with acceptable biases stemming from imperfections of the model and the specified initial, boundary and forcing conditions. A medium-term evolutionary scenario simulation underlined the different response to and impact of a consecutive sequence of floods. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Bias correction is a necessary post-processing procedure in order to use regional climate model (RCM) simulated local climate variables as the input data for hydrological models owing to systematic errors of RCMs. Most of present bias correction methods adjust statistical properties between observed and simulated data on the basis of calendar periods, e.g. month or season. However, this matching statistic is only a necessary condition, not a sufficient condition, as temporal distribution of the precipitation between observed and simulated data is ignored. This study suggests an improved bias correction scheme that considers not only statistical properties but also the temporal distribution between the time series of observed and modelled data. The ratio of the observed precipitation to simulated precipitation is used to compare the behaviour between the observed and modelled precipitation data, and three criteria are proposed when dividing bias correction periods: (1) underestimation of precipitation, (2) stability of /underestimation of precipitation, (2) stability of precipitation ratio and (3) oscillation of precipitation ratio. The results show that the output of the proposed bias correction method follows the trend of the observed precipitation better than that of the conventional bias correction method. This study indicates that temporal distribution should not be ignored when choosing a comparison period for bias correction. However, the study is only a preliminary attempt to address this important issue, and we hope it will stimulate more research activities to improve the methodology. Future efforts on several unsolved problems have been suggested such as how to find out the optimal group number to avoid the overfitting and underfitting conditions. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Presence-only records may provide data on the distributions of rare species, but commonly suffer from large, unknown biases due to their typically haphazard collection schemes. Presence-absence or count data collected in systematic, planned surveys are more reliable but typically less abundant., We proposed a probabilistic model to allow for joint analysis of presence-only and survey data to exploit their complementary strengths. Our method pools presence-only and presence-absence data for many species and maximizes a joint likelihood, simultaneously estimating and adjusting for the sampling bias affecting the presence-only data. By assuming that the sampling bias is the same for all species, we can borrow strength across species to efficiently estimate the bias and improve our inference from presence-only data., We evaluate our model's performance on data for 36 eucalypt species in south-eastern Australia. We find that presence-only records exhibit a strong sampling bias towards the coast and towards Sydney, the largest city. Our data-pooling technique substantially improves the out-of-sample predictive performance of our model when the amount of available presence-absence data for a given species is scarce, If we have only presence-only data and no presence-absence data for a given species, but both types of data for several other species that suffer from the same spatial sampling bias, then our method can obtain an unbiased estimate of the first species' geographic range. [ABSTRACT FROM AUTHOR]

A cure rate model is a survival model incorporating the cure rate with the assumption that the population contains both uncured and cured individuals. It is a powerful statistical tool for prognostic studies, especially in cancer. The cure rate is important for making treatment decisions in clinical practice. The proportional hazards (PH) cure model can predict the cure rate for each patient. This contains a logistic regression component for the cure rate and a Cox regression component to estimate the hazard for uncured patients. A measure for quantifying the predictive accuracy of the cure rate estimated by the Cox PH cure model is required, as there has been a lack of previous research in this area. We used the Cox PH cure model for the breast cancer data; however, the area under the receiver operating characteristic curve (AUC) could not be estimated because many patients were censored. In this study, we used imputation-based AUCs to assess the predictive accuracy of the cure rate from the PH cure model. We examined the precision of these AUCs using simulation studies. The results demonstrated that the imputation-based AUCs were estimable and their biases were negligibly small in many cases, although ordinary AUC could not be estimated. Additionally, we introduced the bias-correction method of imputation-based AUCs and found that the bias-corrected estimate successfully compensated the overestimation in the simulation studies. We also illustrated the estimation of the imputation-based AUCs using breast cancer data. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Psychological researchers have examined a broad array of biases and shortcomings of social perceivers. Less attention has been paid to how people react when they become concerned about the possibility of bias and attempt to correct or overcome the potential for bias. An early approach was to think of bias correction as 'subtracting' information or reactions that are identified as coming from the biasing factor instead of the target. An alternative approach is to conceptualize bias correction as guided by social perceivers' naïve theories or perceptions of the bias at work. The subtraction theories were designed to deal with the assimilative biases typical of early priming experiments, but theory-based corrections can readily deal with either assimilative or contrastive biases. We describe both types of correction theories, with a focus on the empirical support for theory-based correction (and especially for the Flexible Correction Model). We also identify future directions for research on theory-based correction. [ABSTRACT FROM AUTHOR]