Error Distribution Research Articles

Big data benchmarking: how do DFT methods across the rungs of Jacob's ladder perform for a dataset of 122k CCSD(T) total atomization energies?

Total atomization energies (TAEs) are a central quantity in density functional theory (DFT) benchmark studies. However, so far TAE databases obtained from experiment or high-level ab initio wavefunction theory included up to hundreds of TAEs. Here, we use the GDB-9 database of 133k CCSD(T) TAEs generated by Curtiss and co-workers [B. Narayanan, P. C. Redfern, R. S. Assary and L. A. Curtiss, Chem. Sci., 2019, 10, 7449] to evaluate the performance of 14 representative DFT methods across the rungs of Jacob's ladder (namely, PBE, BLYP, B97-D, M06-L, τ-HCTH, PBE0, B3LYP, B3PW91, ωB97X-D, τ-HCTHh, PW6B95, M06, M06-2X, and MN15). We first use the A25[PBE] diagnostic for nondynamical correlation to eliminate systems that potentially include significant multireference effects, for which the CCSD(T) TAEs might not be sufficiently reliable. The resulting database (denoted by GDB9-nonMR) includes 122k species. Of the considered functionals, B3LYP attains the best performance relative to the G4(MP2) reference TAEs, with a mean absolute deviation (MAD) of 4.09 kcal mol-1. This first-generation hybrid functional, in which the three mixing coefficients were fitted against a small set of TAEs, is one of the few functionals that are not systematically biased towards overestimating the G4(MP2) TAEs, as demonstrated by a mean-signed deviation (MSD) of 0.45 kcal mol-1. The relatively good performance of B3LYP is followed by the heavily parameterized M06-L meta-GGA functional, which attains a MAD of 6.24 kcal mol-1. The PW6B95, M06, M06-2X, and MN15 functionals tend to systematically overestimate the G4(MP2) TAEs and attain MADs ranging between 18.69 (M06) and 28.54 (MN15) kcal mol-1. However, PW6B95 and M06-2X exhibit particularly narrow error distributions. Thus, scaling their TAEs by an empirical scaling factor reduces their MADs to merely 3.38 (PW6B95) and 2.85 (M06-2X) kcal mol-1. Empirical dispersion corrections (e.g., D3 and D4) are attractive, and therefore, their inclusion worsens the performance of methods that systematically overestimate the TAEs.

Importance of the innovation term for GARCH modelling of direct real estate returns

ABSTRACT The autoregressive heteroscedastic effects of the conditional volatility processes of direct real estate (capital value) returns are subject to a broad range of econometrics. However, while many specifications have been utilised in the empirical design, the literature has commonly modelled the innovation term within the GARCH volatility processes of real estate capital values through a Gaussian normal distribution. This a priori assumption falls short of the data characteristics exhibited by capital value returns, implying that capital value returns cannot be adequately modelled without adapting to the innovation term distribution. Misspecification can underestimate risk and lead to the over-allocation of riskier assets. This study investigates the impact of the a priori assumption about the innovation terms applied to capital value returns, as well as their robustness, through a time-varying framework. The main findings are that misspecification and parameterisation occur when assuming the normality of the innovation term, and that the application of a priori assumptions of the innovation term beyond those of a normal, Student’s t, or generalised error distribution currently employed in the literature can increase the validity of volatility models when applied to capital value returns. The application of the Johnson – SU distribution yields the best overall performance.

FGO-MFI: factor graph optimization-based multi-sensor fusion and integration for reliable localization

Reliable and precise information pertaining to the position, velocity, and attitude is essential for automated driving. This paper proposes FGO-MFI, a cost-effective and robust multi-sensor fusion and integration localization framework that utilizes factor graph optimization. Firstly, a tightly coupled Global Navigation Satellite Systems (GNSS)/on-board sensor fusion localization framework is established to estimate vehicle states, including position, velocity, and attitude. To address the large drift rate of the Inertial Measurement Unit (IMU), this study introduces a novel IMU/Dynamics pre-integration method based on the vehicle dynamics model. We establish a two-degree-of-freedom vehicle dynamics model utilizing measurements from the wheel speed sensor and steering wheel angle sensor. The IMU/Dynamics factor is devised through a close integration of the model output and IMU pre-integration, enabling the construction of precise odometry with low-cost on-board sensors. Then, to address the issue of the non-Gaussian distribution of GNSS pseudorange error, this paper employs a Gaussian Mixture Model (GMM) to characterize the pseudorange noise, which is then applied to further sensor fusion. Given the time-varying nature of pseudorange noise, the expectation maximization algorithm is utilized to estimate GMM parameters online, leveraging the pseudorange residuals within a sliding window. Comprehensive experiments, inclusive of challenging scenarios such as urban canyons, tunnels, and wooded areas, have been carried out. They affirm the superior performance of the proposed method. Experiments have shown that our method demonstrates reliable localization across different statuses of GNSS signal, exhibiting a 39.4% improvement in the root mean square error of position error when compared to the state-of-the-art. Additionally, this FGO-MFI is a general sensor data fusion framework and is able to incorporate diverse sensor measurements, for example, from cameras and LiDARs to provide more reliable and accurate localization information.

Vitamin D and it’s Association with FokI VDR Polymorphism in Patients with Asthma-COPD Overlap

Background: Asthma-COPD overlap is a new phenotype in respiratory ailments. It has been shown that this group of patients might possess vitamin D3 deficiency. The association of vitamin D receptor (VDR) gene polymorphism with serum vitamin D status in ACO patients has not been investigated yet. Objective: To assess the association of VDR gene polymorphism (FokI) with serum vitamin D status in patients with Asthma-COPD overlap. Methods: This cross-sectional study was carried out in the Department of Physiology of Bangabandhu Sheikh Mujib Medical University, Dhaka, from January 2018 to July 2018 on 23 (twenty-three) patients (aged ≥40 years) with ACO. For comparison, 24 (twenty-four) apparently healthy age, smoking duration and BMI matched subjects were selected. For all participants single nucleotide polymorphism of VDR gene FokI (rs10735810) was done by DNA extraction, polymerase chain reaction (PCR) and restriction fragment length polymorphism (RFLP). Vitamin D3 was measured by using automated analyzer: ARCHITECT Plus ci4100. The results were expressed as mean with standard error (mean±SEM) and frequency distribution. The data were statistically analyzed by Graphpad prism (Version 7) using independent sample ‘t’ test, two sample proportion test, Fisher exact test and ANOVA test. In the interpretation of results, ≤0.05 level of probability (p) was accepted as significant. Results: The mean±SEM of serum 25(OH)D were 16.37±0.78 and 18.46±1.01 ng/ml in control and study groups, respectively. The frequency distribution of FokI genotype was 86.95% (FF), 8.69% (Ff), 4.35% (ff) and 91.66% (FF), 4.17% (Ff), 4.17% (ff) in ACO patients and healthy subjects, respectively. When FoKI VDR SNP was analyzed with serum vitamin D status in patients with ACO, statistically no association was seen. Conclusions: FokI VDR SNP is not associated with serum vitamin D status in patients with ACO. J Rang Med Col. March 2024; Vol. 9, No. 1: 22-27

Practical Method for Identifying Model Parameters for Machining Error Simulation in End Milling Through Sensor-Less Monitoring and On-Machine Measurement

Depending on cutting conditions, unacceptable machining errors are caused by tool deflection in end milling operations. Many studies have proposed methods for predicting the machining error owing to the tool deflection to achieve the theoretical optimization of the cutting conditions. However, the conventional machining error simulation is not practically utilized to determine the optimal cutting conditions. Tool system stiffness parameters and cutting coefficients must be identified in advance to simulate machining errors. However, dynamometers and displacement sensors are required for parameter identification. Therefore, it is impossible to identify the required parameters in typical factories, which do not possess such special equipment. In this study, a practical method was developed to identify the stiffness parameters that can be determined in factories. The proposed method employs on-machine measurement and sensor-less cutting force monitoring to achieve practical parameter identification. In the proposed method, the profile milling is first conducted. During the milling operation, the cutting force and cutting torque are monitored through a controller based on the sensor-less monitoring technique. After the operation, the machining error distribution on the machined surface is measured on machine using a touch probe. The required parameters are identified by minimizing the differences between the measured and theoretical forces, torques, and machining error distributions.

Evaluation of the offset couch parameter between kilovoltage on-board imaging and cone-beam computed tomography in patients with prostate cancer

Background: External Beam Radiation Therapy (EBRT) is a curative therapy technique for prostate cancer. Since the prostate is unstable and surrounded by the bladder and rectum, precision of the target location is critical. Image Guided Radiation Therapy (IGRT) can improve treatment precision. The bladder and rectum may alter volume during IGRT, shifting the prostate’s position and resulting in missed target volume doses and extra organs at risk (OARs) doses. Objective: To assess setup error and residual error during patient positioning, as well as the current IGRT protocol efficiency, in prostate cancer patients while recommending a planning target volume (PTV) margin. Materials and methods: The offset couch parameter of on-board imaging (OBI) and cone-beam computed tomography (CBCT) was computed to determine the error distribution, magnitude, and error difference between treatment phases. The systematic and random errors were calculated using the van Herk equation to determine the planning target volume (PTV) margin. Results: The setup error was -0.86 to 0.25 mm, and the residual error was -0.15 to 0.32 mm. The couch displacement percentage for OBI was 29.44% to 58.89%, and for CBCT was 8.10% to 34.12%. The systematic error was 1.65 to 3.21. The random error was 1.78 to 3.29. The setup error was greatest in the longitudinal (Lng) direction, residual error was greatest in the vertical (Vrt) direction, and systematic and random error were greatest in the Vrt and lateral (Lat) direction, respectively. The PTV margin was greatest in the Vrt direction, while the Lng direction was the narrowest margin for every treatment phase. Conclusion: The highest setup error occurs in the Lng direction for all treatment phases. For the 46 Gy and 60 Gy phases, the highest residual error is in the Vrt direction. However, in the 78 Gy phase, the error is relatively close to 0.01mm in every direction. The current IGRT protocol is effective in detecting setup and residual errors. The 78 Gy phase has the greatest PTV margin, whereas the 46 Gy phase shows the narrowest margins in all directions.

Rank-based regression with bootstrapping and the least square regression for analysis of small sample interrupted time-series data: simulation studies and application to illegal organ transplants

For interrupted time series with small samples and outliers, the standard least squares regression for estimating intervention effects can be biased when the assumption of normality is violated. This article proposes a rank-based regression method combined with bootstrapping to address outliers and non-normality. The proposed method minimizes the Jaeckel dispersion function based on ranked residuals, offering robustness against outliers, skewed error distributions, and correlated errors. Simulation studies and a real-world example compare the performance of the proposed method with the least squares method under various conditions. Both produce unbiased estimates for the intervention effects under normality. However, with non-normal errors, outliers, and correlated errors, the rank-based bootstrapping method outperforms least squares producing narrower confidence intervals, higher efficiency, lower type I error rates, and greater power. Application to the Istanbul declaration on illegal organ activities confirms the improved robustness and precision of the rank-based method with bootstrapping over least squares.

Predicting Social Inequality in Poland Using Price Dispersion on the Real Estate Market

AbstractMeasuring and predicting price dispersion on the real estate market is an important issue for both investors and policymakers. Price dispersion in the housing market can be seen as an additional dimension for measuring social inequality and one of the main goals of public policies that focus on life satisfaction and the accumulation of permanent wealth. The article considers the Polish real estate market and proposes the prediction methods of four measures of dispersion. They are based on quantiles and allow overall measurement of dispersion. In the prediction of dispersion measures the plug-in predictors utylizing longitudinal mixed models are proposed. Furthermore, the ex ante prediction accuracy measure called the quantile of absolute prediction errors (QAPE) is assessed using the residual bootstrap estimators. QAPE allows for a comprehensive description of the distribution of prediction errors, thus fostering discussion of possible various market scenarios.

Quality Assessment for The Translations of Museum Texts: A Case Study of The Archaeology and Ethnography Museum of Edirne

Museums have significant roles in promoting and presenting cultural heritage and cultural values in various ways. An intrinsic part of intercultural communication in museums, bilingual museum texts (e.g. labels, brochures, explanatory texts etc.) have become an important field to be investigated within the framework of Translation Studies. In order to make a contribution to the analysis of translation of museum texts, this study aims to provide a quality assessment of English translations of texts in the Archeology and Ethnography Museum of Edirne. Even though English translations of different texts types such as labels, brochures and explanatory texts are available in the museum, this study specifically focuses on the translation of explanatory texts presenting the ethnographic identity and archeological findings of the city. For this purpose, twenty-three texts displayed in information boards have been selected and their translations have been analyzed according to translation quality assessment model of Multidimensional Quality Metrics (MMQ). As a result of a detailed qualitative and quantitative analysis of the selected texts, the type, distribution and frequency of translation errors encountered in the selected corpus have been described according to eight dimensions of the metric. Based on this text-based and linguistically-oriented methodology, the functionality of the translated museum texts has been evaluated. It is concluded that this study will provide a theoretical framework and methodological guide to improve the quality assessment competences of lectures and students at the departments of translation studies as well as professional translators and researchers working in the area of museum translation.

Testing distribution for multiplicative distortion measurement errors

. In this article, we study a goodness of fit test for a multiplicative distortion model under a uniformly distributed but unobserved random variable. The unobservable variable is distorted in a multiplicative fashion by an observed confounding variable. The proposed k-th power test statistic is based on logarithmic transformed observations and a correlation coefficient-based estimator without distortion measurement errors. The proper choice of k is discussed through the empirical coverage probabilities. The asymptotic null distribution of the test statistics are obtained with known asymptotic variances. Next, we proposed the conditional mean calibrated test statistic when a variable is distorted in a multiplicative fashion. We conduct Monte Carlo simulation experiments to examine the performance of the proposed test statistics.

Evaluation of multiple machine learning models for ASR expansion of concrete

Machine learning algorithms (MLA) show promise in predicting alkali-silica reaction (ASR) expansion. Nevertheless, the predictive performance of different machine learning algorithms (MLAs) varies across various engineering scenarios and datasets. Given that, this study was devoted to constructing an appropriate predictive model for ASR expansion of concrete. Multiple MLAs including evolutionary-based, support vector-based, assembly-based, and neural network-based algorithms, were employed to establish predictive models. An optimization technique, the particle swarm optimization algorithm, was also utilized and integrated with the aforementioned MLA models to effectively search for optimal hyperparameters and topological structures. The predictive performance was evaluated using statistical methods, which showed that the extreme gradient boosting (XGBoost) model had the best fitting goodness among the standard algorithms, with an R2 value of 0.9695. Moreover, the Particle Swarm Optimization (PSO) model significantly improved the predictive performance of all Machine Learning Algorithm (MLA) models. The error distribution analysis was then performed. There are fewer large errors in the predicted values of the PSO-XGBoost model. Monte Carlo simulation and probability distribution functions were used to thoroughly analyze the stability of each model after multiple independent runs. The results show that all models have excellent stability. The PSO-XGBoost model performed the best and was subsequently employed to conduct feature analysis to determine the significance of each input parameter and capture the underlying evidence of ASR expansion.

Adaptive Weighted Error-Correction Method Based on the Error Distribution Characteristics of Multi-Channel Alignment.

As process nodes of advanced integrated circuits continue to decrease below 10 nm, the requirement for overlay accuracy is becoming stricter. The alignment sensor measures the position of the alignment mark relative to the wafer; thus, sub-nanometer alignment position accuracy is vital. The Phase Grating Alignment (PGA) method is widely used due to its high precision and stability. However, the alignment error caused by the mark asymmetry is the key obstacle preventing PGA technology from achieving sub-nanometer alignment accuracy. This error can be corrected using many methods, such as process verification and multi-channel weighted methods based on multi-diffraction, multi-wavelength and multi-polarization state alignment sensors. However, the mark asymmetry is unpredictable, complex and difficult to obtain in advance. In this case, the fixed-weight method cannot effectively reduce the alignment error. Therefore, an adaptive weighted method based on the error distribution characteristic of a multi-channel is proposed. Firstly, the simulation result proves that the error distribution characteristic of the multi-alignment result has a strong correlation with the mark asymmetry. Secondly, a concrete method of constructing weight values based on error distribution is described. We assume that the relationship between the weight value of each channel and the deviations of all channels' results is second-order linear. Finally, without other prior process correction in the simulation experiment, the residual error's Root Mean Square (RMS) of fixed weighted method is 14.0 nm, while the RMS of the adaptive weighted method is 0.01 nm, when dealing with five typical types of mark asymmetry. The adaptive weighted method exhibits a more stable error correction effect under unpredictable and complicated mark asymmetry.

Refined offshore wind speed prediction: Leveraging a two-layer decomposition technique, gated recurrent unit, and kernel density estimation for precise point and interval forecasts

Precise forecasting of offshore wind speeds is paramount for various applications, including offshore wind energy production, disaster prevention and mitigation, and maritime navigation. This study introduces a novel model for point and interval prediction of offshore wind speed, incorporating an innovative two-layer decomposition technique, gated recurrent unit (GRU), and kernel density estimation (KDE). Initially, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is utilized to decompose the raw series into multiple subsequences, thus alleviating the inherent data nonstationarity. Subsequently, we employ the refined composite multiscale fuzzy entropy (RCMFE) to reconstruct the subsequences into multiple components, thereby reducing the computational complexity. Variational mode decomposition (VMD) is utilized to enhance the modeling accuracy to decompose the high-frequency component, thereby generating a set of submodels. The GRU, a specialized recurrent neural network architecture, is leveraged to forecast the remaining components and submodels, producing a series of subresults. The subresults are linearly summed to obtain the final point prediction result. Finally, based on point prediction, employing KDE to gauge the probability density distribution of prediction errors culminates in the derivation of the offshore wind speed prediction interval. The experimental results substantiate the superior predictive performance of the proposed prediction model.

Examining Syntactic Errors in Essay Writing Among EFL Students: A Case Study of ESEF Ibn Tofail University

This article presents a thorough examination of error patterns within essays written by students enrolled at Ibn Tofail University, focusing on four major types of errors: Omission Addition, Mis-ordering, and Mis-formation. Utilizing data collected through essays and coded through content analysis. The data collected were analysed through SPSS, descriptive analysis, the study investigates the prevalence, distribution, and characteristics of these errors across a diverse range of essays. Through frequency distributions and skewness analysis, significant perspectives into error distributions and patterns emerged, revealing varying degrees of writing proficiency among the sample. The findings showed that the most frequent syntactic errors in students’ writings are addition errors and the most infrequent ones are mis-ordering. Professors may need to focus more attention on addressing and correcting addition errors, as they appear to be more prevalent and may have a greater impact on the overall clarity and coherence of students’ writing. Meanwhile, less emphasis may be needed on Mis-ordering errors, although they should still be addressed to ensure students develop strong syntactic skills and produce well-structured compositions.

The Star Formation History in Local Group Galaxies. I. Ten Dwarf Galaxies

The star formation histories (SFHs) of galaxies provide valuable insights into galaxy evolution and stellar physics. Understanding the SFHs enables the study of chemical enrichment of galaxies, star formation triggered by interactions, and the behavior of various stellar populations. This work investigates the SFHs of ten dwarf galaxies in the Local Group (LG), which spans a wide range of types, masses, luminosities, and metallicities. The analysis is based on our new sample of the member stars in the LG after removing foreground dwarf stars by using information from the near-infrared color–color diagram and Gaia astrometry. The sample includes the most complete and pure red supergiants and asymptotic giant branch stars to gain valuable insights into the recent SFHs of the galaxies. A fitting method based on the color–magnitude diagram (CMD) is introduced to measure the SFH. The Padova isochrones are used to generate initial model CMDs, accounting for photometric errors and completeness through star-field simulations to match the completeness and error distributions of the observed CMDs. Subsequently, the SFHs, distance modulus, and metallicity of the ten dwarf galaxies are determined by fitting the CMDs. The results indicate that the star formation rates of dwarf irregulars show a gradual increase, while those of dwarf ellipticals exhibit a gradual decrease from the past to the present. Furthermore, this work shows that the star formation activity in dwarf ellipticals persisted up to 30 Myr ago. A significant increasing feature in the SFH of NGC 6822 reveals star formation activity triggered by an interaction event.

Estimation and Prediction of Commodity Returns Using Long Memory Volatility Models

Modelling the volatility of commodity prices and creating more reliable models for estimating and forecasting commodity price returns are crucial. The body of research on statistical models that can fully reflect the empirical characteristics of commodity price returns is lacking. The main aim of this research was to develop a modelling framework that could be used to accurately estimate and forecast commodity price returns by combining long memory models with heavy-tailed distributions. This study employed dual hybrid long-memory generalised autoregressive conditionally heteroscedasticity (GARCH) models with heavy-tailed innovations, namely, the Student-t distribution (StD), skewed-Student-t distribution (SStD), and the generalised error distribution (GED). Based on the smallest forecasting metrics values for mean absolute error (MAE) and mean squared error (MSE) values, the best performing LM-GARCH-type model for lithium is the ARFIMA (1, o, 1)-FIAPARCH (1, ξ, 1) with normal innovations. For tobacco, the best model is ARFIMA (1, o, 1)-FIGARCH (1, ξ, 1) with SStD innovations. The robust performing model for gold is the ARFIMA (1, o, 1)-FIGARCH (1, ξ, 1)-GED model. The best performing forecasting model for crude oil and cotton returns are the FIAPARCH 1,ξ, 1−SStD model and HYGARCH 1,ξ, 1−StD model, respectively. The results obtained from this study would be beneficial to those concerned with financial market modelling techniques, such as derivative pricing, risk management, asset allocation, and valuation.

Machine learning-based method for predicting C–V-T characteristics and electrical parameters of GaAs/AlGaAs multi-quantum wells Schottky diodes

In this work, two models of artificial neural networks are developed to predict the electrical parameters and capacitance-voltage characteristics of GaAs/AlGaAs multi-quantum wells Schottky diodes at different temperatures. Capacitance-Voltage-Temperature (C–V-T) characteristics for voltages and temperatures in the ranges (-4 V–0 V) and (20 K–400 K), respectively, were used to assess the effectiveness of the proposed approach. The first model (Model 1) is used to evaluate how well the neural network predicts the C–V-T characteristics. The second simulation, known as Model 2, was constructed to simultaneously overcome the problems of determining the electrical parameters and predicting C–V-T characteristics. Model 2 allows the calculation of the built-in voltage, effective density, and capacitance. Three-fold cross-validation and mean square error are used to assess the effectiveness of the developed models. The results clearly demonstrate the high prediction accuracy of the electrical parameters and C–V characteristics at all temperatures. After training, Model 1 Mean Square Error performance is 1.5033×10−6 at 1450 epochs, whereas Model 2 MSE is 4.9951×10−6 at 642 epochs. According to the error distribution frequency histogram, about 95 % of errors for Model 1 and Model 2 lie between [0.00535 and 0.005608] and [0.00328 and 0.00333], respectively. The R-values that correspond to the training and validation datasets for both models are close to one (0.9999). Parameters determination results have been compared against those obtained using ant lion optimizer based method. It was found that the results obtained from the neural networks models strongly agree with the experimental data.

High Accuracy Localization Scheme Using 1-Bit Side Information: Achievability from a GDoP Perspective

In this paper, we provide a novel methodology for high-precision positioning that utilizes 1-bit additional information, which applies to various positioning techniques. The proposed approach leverages binary information to indicate if a user is within a specified space of interest and refines the estimated location information outside this area. By matching the estimated locations outside the area of interest with the valid location information within, this methodology corrects the positional data obtained through any arbitrary positioning technique, aligning the estimated positions with the intended spatial boundaries. Performance analysis metrics, such as Average Positioning Error (APE) and Cumulative Distribution Function for positioning coverage, were employed to assess the effectiveness of the proposed methods. Numerical simulations demonstrate how the proposed method enhances the averaged positioning accuracy, significantly outperforming the conventional time of arrival method. Furthermore, the proposed positioning correction methodology demonstrates validated feasibility applicable to an arbitrary existing positioning method.

Detection of atypical attentional behaviors in young subjects

BackgroundVigilance ability refers to the accuracy and speed with which a person performs a cognitive-motor task, either voluntarily (endogenous mode) or following a warning stimulus (exogenous mode). In the context of a force production task, our study focuses on the impact of the states of vigilance by proposing an original approach that allows distinguishing between good (inlier) and poor (outlier) participants. We assume that the use of an external signal and duration of the temporal preparation (foreperiod) increase the speed and the precision of motor responses. Our objective is particularly challenging in the context of a limited dataset with a high level of noise. New methodOur original methodological approach consists of coupling the RANSAC (RANdom SAmple Consensus) algorithm with a statistical machine learning algorithm to handle noise. Comparison with existing methodsOur clustering approach, based on the coupling of RANSAC methodology with ensemble classifiers, overcomes the limitations of conventional supervised algorithms that are either not robust to outliers (such as K-Nearest Neighbors) and/or not adapted to few-shot learning (such as Support Vector Machines and Artificial Neural Networks). ResultsThe clustering results were validated in terms of reaction time distributions and force error distributions with respect to participant groups. We show that the use of an external signal and duration of the temporal preparation (foreperiod) increase the speed and the precision of motor responses. ConclusionOur study has allowed us to detect atypical attentional patterns and succeeds in separating the inliers from the outliers.

Highly-accelerated CEST MRI using frequency-offset-dependent k-space sampling and deep-learning reconstruction.

To develop a highly accelerated CEST Z-spectral acquisition method using a specifically-designed k-space sampling pattern and corresponding deep-learning-based reconstruction. For k-space down-sampling, a customized pattern was proposed for CEST, with the randomized probability following a frequency-offset-dependent (FOD) function in the direction of saturation offset. For reconstruction, the convolution network (CNN) was enhanced with a Partially Separable (PS) function to optimize the spatial domain and frequency domain separately. Retrospective experiments on a self-acquired human brain dataset (13 healthy adults and 15 brain tumor patients) were conducted using k-space resampling. The prospective performance was also assessed on six healthy subjects. In retrospective experiments, the combination of FOD sampling and PS network (FOD + PSN) showed the best quantitative metrics for reconstruction, outperforming three other combinations of conventional sampling with varying density and a regular CNN (nMSE and SSIM, p < 0.001 for healthy subjects). Across all acceleration factors from 4 to 14, the FOD + PSN approach consistently outperformed the comparative methods in four contrast maps including MTRasym, MTRrex, as well as the Lorentzian Difference maps of amide and nuclear Overhauser effect (NOE). In the subspace replacement experiment, the error distribution demonstrated the denoising benefits achieved in the spatial subspace. Finally, our prospective results obtained from healthy adults and brain tumor patients (14×) exhibited the initial feasibility of our method, albeit with less accurate reconstruction than retrospective ones. The combination of FOD sampling and PSN reconstruction enabled highly accelerated CEST MRI acquisition, which may facilitate CEST metabolic MRI for brain tumor patients.