Miracle-dz/UniFAR-FTUs huggingface dataset:
Sequential Design and Spatial Modeling for Portfolio Tail Risk Measurement

We consider calculation of capital requirements when the underlying economic scenarios are determined by simulatable risk factors. In the respective nested simulation framework, the goal is to estimate portfolio tail risk, quantified via VaR or TVaR of a given collection of future economic scenarios representing factor levels at the risk horizon. Traditionally, evaluating portfolio losses of an outer scenario is done by computing a conditional expectation via inner-level Monte Carlo and is computationally expensive. We introduce several inter-related machine learning techniques to speed up this computation, in particular by properly accounting for the simulation noise. Our main workhorse is an advanced Gaussian Process (GP) regression approach which uses nonparametric spatial modeling to efficiently learn the relationship between the stochastic factors defining scenarios and corresponding portfolio value. Leveraging this emulator, we develop sequential algorithms that adaptively allocate inner simulation budgets to target the quantile region. The GP framework also yields better uncertainty quantification for the resulting VaR/TVaR estimators that reduces bias and variance compared to existing methods. We illustrate the proposed strategies with two case-studies in two and six dimensions.
Plate Recognition Based on Deep Learning

In this context, the artificial intelligence method is used to make plate recognition based on deep learning and to help the restaurant implement an automatic settlement system. The customer simply places the plate in the “clearing area” and calculates the price to be paid and settles based on the results of the plate identification. This system has the characteristics of fast speed, accurate accounting, good experience and no manpower. It can improve the operation efficiency of the restaurant and provide customers with a better self-service settlement experience.
The Commitment of Special School Teachers Towards The Implementation of Distance Learning During the Covid-19 Pandemic

The COVID-19 pandemic is a challenge for teachers to keep learning. One alternative that teachers can do is distance learning. The continuity of learning is largely determined by the commitment of the teacher. The purpose of this study is to reveal the contribution of teacher commitment to the continuity of learning, as well as to find out the effective contribution and relative commitment of teachers to learning in special schools. This research approach is quantitative research. The sampling technique used is accidental sampling, where the determination of the sample is based on chance, anyone who responds to the researcher can be used as a sample. Data collection techniques using a questionnaire. The teacher commitment instrument was adapted from the commitment scale developed by Allen and Meyer which has sufficient validity and reliability. The learning instrument uses content validity, and the instrument reliability uses the interrater technique. Questionnaires were sent via WhatsApp to all teacher communities throughout Indonesia which were limited to 3 days and collected 872 respondents. The data were analyzed by multiple regression with the help of SPSS 23. The results showed that the teacher's commitment to the implementation of distance learning was influenced by the teacher's commitment by 2.5% and influenced by other variables not included in the linear regression model by 97.5%. Normative commitment has an effective contribution (1.31%) and a relative contribution (51.77%), which is the largest contribution compared to the other two aspects of commitment.
Effect of Tip Clearance Dimensions and Control of Unsteady Flows in a Multi-Stage High-Pressure Compressor

This paper describes the investigations performed to better understand unsteady flows that develop in a three-stage high-pressure compressor. More specifically, this study focuses on rotor-stator interactions and tip leakage flow effects on overall performance and aerodynamic stability. The investigation method is based on three-dimensional unsteady RANS simulations, considering the natural spatial periodicity of the compressor. Indeed, all information related to rotor-stator interactions can be computed. A comparison is first done with experimental measurements to outline the capacity of the numerical method to predict overall performance and unsteady flows. The results show that the simulation correctly estimates most flow features in the multistage compressor. Then numerical data obtained for three configurations of the same compressor are analyzed and compared. Configurations 1 and 2 consider two sets of tip clearance dimensions and a casing treatment based on a honeycomb design is applied for configuration 3. Detailed investigations of the flow at the same operating line show that the tip leakage flow is responsible for the loss of stability in the last stage. An increase by 30% of the tip clearance dimension dramatically reduces the stable operating range (by 40% with respect to the standard configuration). A modal analysis shows that the stall process in this case involves the perturbation of the flow in the last rotor by upstream stator wakes, leading to the development of a rotating instability. The control device designed and investigated in this study allows for reducing the sensitivity of the compressor to tip leakage flow by recovering the initial stable operating range.
Evaluation of the relationship of intuitive eating behaviour between body mass index and waist circumference in university students

Objective: This study was conducted to examine the relationship between intuitive eating behaviour, body mass index (BMI), and waist-hip circumference among university students. Methods: This study was conducted on 146 students studying at the Department of Nutrition and Dietetics of a foundation university on the European side of Istanbul in the 2020-2021 academic year. Socio-demographic characteristics, anthropometric measurements, and intuitive eating habits of the students were taken through an online questionnaire. Intuitive Eating Scale-2 (IES-2) was used to determine students’ intuitive eating behaviour. Results: 146 students with a mean age of 20.64±1.69 participated in the study. 62.3% of the students were female and 37.7% were male. Intuitive eating scale total score mean ± standard deviation values ​​were found to be 3.40±0.55. BMI mean ± standard deviation values were found to be 21.89 ± 3.94 kg / m2 while waist circumference mean ± standard deviation values were 69.78±9.32 cm. eating due to physical rather than emotional causes sub-dimension has a positive and significant correlation with the eating sub-dimension related to hunger and fullness signals, body-food choice congruity, and intuitive eating scale-total score. There is a significant and positive relationship between the eating sub-dimension related to hunger and fullness signals and the body-food choice congruity sub-dimension and the total score of the intuitive eating scale. A significant and positive correlation was found between the body-food choice congruity sub-dimension and the total score of the intuitive eating scale. Comparing the intuitive eating scale total scores and sub-dimensions according to BMI groups, there is a significant difference in the mean score of the body-food choice congruity sub-dimension according to the BMI classification. Conclusion: As intuitive eating increases in university students, the body mass index decreases.
A Monte Carlo Investigation of Several Person and Item Fit Statistics for Item Response Models

This study investigated the behavior of several per son and item fit statistics commonly used to test and obtain fit to the one-parameter item response model. Using simulated data for 500 persons and 15 items, the sensitivity of the total-t, mean-square residual, and between-t fit statistics to guessing, heterogeneity in discrimination parameters, and multidimensionality was examined. Additionally, 25 misfitting persons and a misfitting item were generated to test the power of the three fit statistics to detect deviations in a subset of observations. Neither the total-t nor the mean-square residual were able to detect deviation from any of the models fitted. Use of these statistics appears to be un warranted. The between-t was a useful indicator of guessing and heterogeneity in discrimination parame ters, but was unable to detect multidimensionality. These results show that use of person and item fit statistics to test and obtain overall fit to the one- parameter model can lead to acceptance of the model even when it is grossly inappropriate. Assessments of model fit based on this strategy are inadequate. Alter native methods must be sought.
Kernel based visual tracking with scale invariant features

The kernel based tracking has two disadvantages: the tracking window size cannot be adjusted efficiently, and the kernel based color distribution may not have enough ability to discriminate object from clutter background. For boosting up the feature's discriminating ability, both scale invariant features and kernel based color distribution features are used as descriptors of tracked object. The proposed algorithm can keep tracking object of varying scales even when the surrounding background is similar to the object's appearance.
Tweeting Adam Smith: Using Twitter to Engage Students in the History of Economic Thought

This paper presents a novel way to use Twitter to engage students in advanced economics courses. While prior research provides some uses of Twitter in introductory economics courses, little research exists on the use of Twitter in upper level economics courses. This paper provides one of the first pedagogical examples of Twitter use to engage students in an upper level economics elective.
Bayesian Computation and Stochastic Systems

Markov chain Monte Carlo (MCMC) methods have been used extensively in statistical physics over the last 40 years, in spatial statistics for the past 20 and in Bayesian image analysis over the last decade. In the last five years, MCMC has been introduced into significance testing, general Bayesian inference and maximum likelihood estimation. This paper presents basic methodology of MCMC, emphasizing the Bayesian paradigm, conditional probability and the intimate relationship with Markov random fields in spatial statistics. Hastings algorithms are discussed, including Gibbs, Metropolis and some other variations. Pairwise difference priors are described and are used subsequently in three Bayesian applications, in each of which there is a pronounced spatial or temporal aspect to the modeling. The examples involve logistic regression in the presence of unobserved covariates and ordinal factors; the analysis of agricultural field experiments, with adjustment for fertility gradients; and processing of low-resolution medical images obtained by a gamma camera. Additional methodological issues arise in each of these applications and in the Appendices. The paper lays particular emphasis on the calculation of posterior probabilities and concurs with others in its view that MCMC facilitates a fundamental breakthrough in applied Bayesian modeling.
Half-full half-empty functional: an extension to negative lotteries

In the paper Cenci et al . [3] an intuitive and parsimonious descriptive model of decision making under risk that can explain the Kahneman and Tversky’s [6] paradoxes has been proposed for non-negative lotteries. In this paper it is shown that a straightforward extension of the value function proposed in [3] explains the Kahneman and Tversky’s [6] paradoxes also for negative lotteries
A rule-based fuzzy logic controller for a PWM inverter in photo-voltaic energy conversion scheme

A rule-based controller based on fuzzy set theory for controlling the output power of a pulse width modulation (PWM) inverter in a photovoltaic (PV) energy conversion interface scheme is presented. The objective is to track and extract the maximum available solar power from the PV array under varying solar insolation levels. To achieve this the power error and the rate of change of this error are used as input signals to the fuzzy rule-based controller and its output signal is used to control the PWM inverter. The input error signals are fuzzified and expressed as linguistic labels characterized by their membership grades. Using a fuzzy relation matrix, a set of 49 rules find fuzzy logic operations, the controller output is obtained. The fuzzy controller output expressed in linguistic labels is defuzzified to obtain the actual analog signal to control the PWM inverter. The proposed fuzzy rule-based controller is simulated and experimentally verified, and is found to give good power tracking performance. <<ETX>>
On the performance analysis of rainfall prediction using mutual information with artificial neural network

Monsoon rainfall prediction over a small geographic region is indeed a challenging task. This paper uses monthly means of climate variables, namely air temperature (AT), sea surface temperature (SST), and sea level pressure (SLP) over the globe, to predict monthly and seasonal summer monsoon rainfall over the state of Maharashtra, India. Mutual information correlates the temperature and pressure from a grid of 10° longitude X 10° latitude with Maharashtra’s monthly rainfall time series. Based on the correlations, selected features over the respective latitude and longitudes are given as inputs to an artificial neural network. It was observed that AT and SLP could predict monthly monsoon rainfall with excellent accuracy. The performance of the test dataset was evaluated through mean absolute error; root mean square error, correlation coefficient, Nash Sutcliffe model efficiency coefficient, and maximum rainfall prediction capability of the network. The individual climate variable model for AT performed better in all evaluation parameters except maximum rainfall capability, where the combined model 2 with AT, SLP and SST as predictors outperformed. The SLP-only model’s performance was comparable to the AT-only model. The combined model 1 with AT and SLP as predictors was found better than the combined model 2.
Alzheimer’s detection through neuro imaging and subsequent fusion for clinical diagnosis

In recent years, vast improvement has been observed in the field of medical research. Alzheimer's is the most common cause for dementia. Alzheimer's disease (AD) is a chronic disease with no cure, and it continues to pose a threat to millions of lives worldwide. The main purpose of this study is to detect the presence of AD from magnetic resonance imaging (MRI) scans through neuro imaging and to perform fusion process of both MRI and positron emission tomography (PET) scans of the same patient to obtain a fused image with more detailed information. Detection of AD is done by calculating the gray matter and white matter volumes of the brain and subsequently, a ratio of calculated volume is taken which helps the doctors in deciding whether the patient is affected with or without the disease. Image fusion is carried out after preliminary detection of AD for MRI scan along with PET scan. The main objective is to combine these two images into a single image which contains all the possible information together. The proposed approach yields better results with a peak signal to noise ratio of 60.6 dB, mean square error of 0.0176, entropy of 4.6 and structural similarity index of 0.8.
Bayesian clustering and product partition models

Summary. We present a decision theoretic formulation of product partition models (PPMs) that allows a formal treatment of different decision problems such as estimation or hypothesis testing and clustering methods simultaneously. A key observation in our construction is the fact that PPMs can be formulated in the context of model selection. The underlying partition structure in these models is closely related to that arising in connection with Dirichlet processes. This allows a straightforward adaptation of some computational strategies—originally devised for nonparametric Bayesian problems—to our framework. The resulting algorithms are more flexible than other competing alternatives that are used for problems involving PPMs. We propose an algorithm that yields Bayes estimates of the quantities of interest and the groups of experimental units. We explore the application of our methods to the detection of outliers in normal and Student t regression models, with clustering structure equivalent to that induced by a Dirichlet process prior. We also discuss the sensitivity of the results considering different prior distributions for the partitions.
Three-dimensional panoramic terrain reconstruction from aerial imagery

Abstract A complete solution for effectively, automatically, and accurately reconstructing the three-dimensional (3-D) panoramic terrain from aerial imagery is presented. With enough premeasured and identified georeferences, we first estimate every camera’s accurate intrinsic and extrinsic parameters by implementing bundle adjustment, which is introduced in detail. Afterward, the adjacent relationship of imagery is acquired from the cameras’ position parameters. In addition, the formulas for corresponding area prediction and image rectification are derived according to the camera parameters. Subsequently, feature-based matching is conducted between adjacent image pairs to provide much more constraints for bundle adjustment. Area-based matching is applied to pairs of horizontal epipolar imagery for dense correspondence to produce dense spatial point cloud. Eventually, the mosaicked digital ortho map and digital elevation model of the whole imaging area are produced automatically by a series of steps including spatial intersection, Tin generation, differential correction, and color blending. Experimental results show that the root mean square (RMS) residual errors of check points in planimetry and altitude are, respectively, 0.039 and 0.170 m, demonstrating the high accuracy of camera orientation. The visualized panoramic 3-D realistic scene validates the feasibility and effectiveness of the proposed solution.
The WRF Single-Moment 6-Class Microphysics Scheme (WSM6)

This study examines the performance of the Weather Research and Forecasting (WRF)-Single-Moment- Microphysics scheme (WSMMPs) with a revised ice-microphysics of the Hong et al. In addition to the simple (WRF Single-Moment 3-class Microphysics scheme; WSM3) and mixed-phase (WRF Single-Moment 5-class Microphysics scheme; WSM5) schemes of the Hong et al., a more complex scheme with the inclusion of graupel as another predictive variable (WRF Single-Moment 6-class Microphysics scheme; WSM6) was developed. The characteristics of the three categories of WSMMPs were examined for an idealized storm case and a heavy rainfall event over Korea. In an idealized thunderstorm simulation, the overall evolutionary features of the storm are not sensitive to the number of hydrometeors in the WSMMPs; however, the evolution of surface precipitation is significantly influenced by the complexity in microphysics. A simulation experiment for a heavy rainfall event indicated that the evolution of the simulated precipitation with the inclusion of graupel (WSM6) is similar to that from the simple (WSM3) and mixed-phase (WSM5) microphysics in a low-resolution grid; however, in a high-resolution grid, the amount of rainfall increases and the peak intensity becomes stronger as the number of hydrometeors increases.
Angular distribution of current to a sphere in a flowing, weakly magnetized plasma with negligible Debye length

The influence of a weak magnetic field (B) on the ion current collected by a spherical electrode is studied by means of the 2v-3d particle in cell code SCEPTIC in the limit of zero Debye length. The ion current dependence on B for low fields, shown to be linear, is compared with analytic expressions valid in the magnetized free-flight limit. In the flow-free regime, expressions for the angular distribution of current at different ion temperatures are provided. The configuration in which the plasma is drifting in the B-direction is investigated as well, and a Mach-probe calibration valid for equal temperature ions and electrons is given.
AXIN2 germline testing in a French cohort validates pathogenic variants as a rare cause of predisposition to colorectal polyposis and cancer

Only a few patients with germline AXIN2 variants and colorectal adenomatous polyposis or cancer have been described, raising questions about the actual contribution of this gene to colorectal cancer (CRC) susceptibility. To assess the clinical relevance for AXIN2 testing in patients suspected of genetic predisposition to CRC, we collected clinical and molecular data from the French Oncogenetics laboratories analyzing AXIN2 in this context. Between 2004 and June 2020, 10 different pathogenic/likely pathogenic AXIN2 variants were identified in 11 unrelated individuals. Eight variants were from a consecutive series of 3322 patients, which represents a frequency of 0.24%. However, loss‐of‐function AXIN2 variants were strongly associated with genetic predisposition to CRC as compared with controls (odds ratio: 11.89, 95% confidence interval: 5.103–28.93). Most of the variants were predicted to produce an AXIN2 protein devoid of the SMAD3‐binding and DIX domains, but preserving the β‐catenin‐binding domain. Ninety‐one percent of the AXIN2 variant carriers who underwent colonoscopy had adenomatous polyposis. Forty percent of the variant carriers developed colorectal or/and other digestive cancer. Multiple tooth agenesis was present in at least 60% of them. Our report provides further evidence for a role of AXIN2 in CRC susceptibility, arguing for AXIN2 testing in patients with colorectal adenomatous polyposis or cancer.
Generalized Approach for Phase Interferometric Measurements of Electromagnetic Field

A new generalized approach for microwave phase interferometric measurement of electromagnetic fleld using an antenna matrix (AM) was developed. The approach allows correction of most signiflcant measurement errors caused by mutual couplings of closely spaced antenna elements. It is based on vector measurements of the AM designed for the frequency 2.60GHz and Matlab simulations.
A Normal Approximation for Binomial, F, Beta, and other Common, Related Tail Probabilities, II

Abstract This paper concerns a new Normal approximation to the beta distribution and its relatives, in particular, the binomial, Pascal, negative binomial, F, t, Poisson, gamma, and chi square distributions. The approximate Normal deviates are expressible in terms of algebraic functions and logarithms, but for desk calculation it is preferable in most cases to use an equivalent expression in terms of a function specially tabulated here. Graphs of the error are provided. They show that the approximation is good even in the extreme tails except for beta distributions which are J or U shaped or nearly so, and they permit correction to obtain still more accuracy. For use beyond the range of the graphs, some standard recursive relations and some classical continued fractions are listed, with some properties of the latter which seem to be partly new. Various Normal approximations are compared, with further graphs. The new approximation appears far more accurate than the others. Everything an ordinary user of th.
Theoretical Model for Nucleate Boiling Heat and Mass Transfer of Binary Mixtures

A model is presented to calculate nucleate boiling heat transfer coefficients of binary mixtures. The model includes the governing physical phenomena, such as the variation of the phase interface curvature, the adhesion pressure between wall and liquid, the interfacial thermal resistance as well as the local variation of composition and liquid-vapor equilibrium. Marangoni convection is considered, too. The theoretical background of these phenomena is described and their implementation is explained. The model is verified by comparing calculated heat transfer coefficients of hydrocarbon mixtures with experimental data