Miracle-dz/UniFAR-FTUs huggingface dataset:
The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics

Many econometric models are susceptible to analysis only by asymptotic techniques and there are three principles, based on asymptotic theory, for the construction of tests of parametric hypotheses. These are: (i) the Wald (W) test which relies on the asymptotic normality of parameter estimators, (ii) the maximum likelihood ratio (LR) procedure and (iii) the Lagrange multiplier (LM) method which tests the effect on the first order conditions for a maximum of the likelihood of imposing the hypothesis. In the econometric literature, most attention seems to have been centred on the first two principles. Familiar " t-tests " usually rely on the W principle for their validity while there have been a number of papers advocating and illustrating the use of the LR procedure. However, all three are equivalent in well-behaved problems in the sense that they give statistics with the same asymptotic distribution when the null hypothesis is true and have the same asymptotic power characteristics. Choice of any one principle must therefore be made by reference to other criteria such as small sample properties or computational convenience. In many situations the W test is attractive for this latter reason because it is constructed from the unrestricted estimates of the parameters and their estimated covariance matrix. The LM test is based on estimation with the hypothesis imposed as parametric restrictions so it seems reasonable that a choice between W or LM be based on the relative ease of estimation under the null and alternative hypotheses. Whenever it is easier to estimate the restricted model, the LM test will generally be more useful. It then provides applied researchers with a simple technique for assessing the adequacy of their particular specification. This paper has two aims. The first is to exposit the various forms of the LM statistic and to collect together some of the relevant research reported in the mathematical statistics literature. The second is to illustrate the construction of LM tests by considering a number of particular econometric specifications as examples. It will be found that in many instances the LM statistic can be computed by a regression using the residuals of the fitted model which, because of its simplicity, is itself estimated by OLS. The paper contains five sections. In Section 2, the LM statistic is outlined and some alternative versions of it are discussed. Section 3 gives the derivation of the statistic for
A hybrid neural architecture search for hyperspectral image classification

Convolution neural network (CNN)is widely used in hyperspectral image (HSI) classification. However, the network architecture of CNNs is usually designed manually, which requires careful fine-tuning. Recently, many technologies for neural architecture search (NAS) have been proposed to automatically design networks, further improving the accuracy of HSI classification to a new level. This paper proposes a circular kernel convolution-β-decay regulation NAS-confident learning rate (CK-βNAS-CLR) framework to automatically design the neural network structure for HSI classification. First, this paper constructs a hybrid search space with 12 kinds of operation, which considers the difference between enhanced circular kernel convolution and square kernel convolution in feature acquisition, so as to improve the sensitivity of the network to hyperspectral information features. Then, the β-decay regulation scheme is introduced to enhance the robustness of differential architecture search (DARTS) and reduce the discretization differences in architecture search. Finally, we combined the confidence learning rate strategy to alleviate the problem of performance collapse. The experimental results on public HSI datasets (Indian Pines, Pavia University) show that the proposed NAS method achieves impressive classification performance and effectively improves classification accuracy.
Mindfulness skills and experiential avoidance as therapeutic mechanisms for treatment-resistant depression through mindfulness-based cognitive therapy and lifestyle modification

Background/objective The COVID-19 pandemic and consequent physical distancing has made it difficult to provide care for those with Treatment-Resistant Depression (TRD). As a secondary analysis of a clinical trial, the aim of this study was to explore potential mechanisms through which three online-delivered approaches, added to treatment as usual, improve depressive symptoms in TRD patients. Methods The three approaches included (a) Minimal Lifestyle Intervention (MLI), (b) Mindfulness-Based Cognitive Therapy (MBCT), and (c) Lifestyle Modification Program (LMP). Sixty-six participants with TRD completed assessments pre-post intervention (mindfulness skills [FFMQ]; self-compassion [SCS]; and experiential avoidance [AAQ-II]) and pre-intervention to follow-up (depressive symptoms [BDI-II]). Data were analyzed using within-subjects regression models to test mediation. Results Mindfulness skills mediated the effect of MBCT on depressive symptoms (ab = −4.69, 95% CI = −12.93 to−0.32), whereas the lack of experiential avoidance mediated the effect of LMP on depressive symptoms (ab = −3.22, 95% CI = −7.03 to−0.14). Conclusion Strengthening mindfulness skills and decreasing experiential avoidance may promote recovery in patients with TRD, MBCT, and LMP have demonstrated that they may help increase mindfulness skills and decrease experiential avoidance, respectively. Future work will need to unpick the components of these interventions to help isolate active ingredients and increase optimization.
MOVPE-grown n-InxGa1-xN (x~0.5)/p-Si(111) template as a novel substrate

This paper proposes for the first time the preparation of n-InGaN/p-Si templates as substrates for InGaN device applications. By using MOVPE, a thick (~0.5 m) InGaN with an intermediate In composition has been successfully grown on Si(111) substrates using an AlN interlayer. By optimizing growth temperature and TMI/(TMI+TEG) molar ratio, InGaN films with In content up to 0.5 are successfully grown. Tensile stress in InGaN films grown at 700°C is estimated to be about half of that for GaN grown at 1100°C and no cracks are found in the InGaN layers. The films grown at a relatively high temperature (700-750°C) show phase separation when their thickness exceeds a critical value. Critical thickness for phase separation is larger for a film grown at a lower temperature with a high In content. For InGaN grown at 600°C with a thickness of 0.8 m, no phase separation is detected by both X-ray diffraction and PL. Such a low temperature-grown InGaN shows a large tilt fluctuation. Ohmic I-V characteristics are obtained between n-InGaN and p-Si and the resistance is markedly decreased with increasing In content in InGaN. The Si pn junction beneath the In0.42Ga0.58N layer behaves well as a solar cell with an InGaN filter. For both n-InGaN/p-Si, the presence of an AlN interlayer between the epilayer and the substrate does not have a significant contribution to the series resistance.
High-performance dielectric elastomer unimorph bending actuators without pre-stretch

Dielectric elastomer (DE) single films for bending actuators are normally used with pre-stretch to increase the performance of the actuation. However, pre-stretch requires a high effort in the production of the actuators. In this work, a simple DE bending actuator in a unimorph configuration with high actuation performance is presented. For the manufacturing of the actuator, a silicone film is coated with conductive carbon nanoparticles in a silicone matrix as electrodes on both sides and laminated with a non-stretchable, but highly bendable and light weight polymer film, which acts as a strain limiting layer. Stiffening bars on the strain limiting layer impede an uncontrolled actuator deformation. The bending angle and the displacement of the actuator tip were measured at variable field strength up to 80 kV/mm. In a single DE layer configuration with an electrode area of 50 mm x 30 mm, a bending angle of 15° and a tip displacement of 7 mm were reached. A mathematical model for the bending actuator was applied to compare experimental and theoretical results and to optimize the relevant parameters. By using thermoplastic polyurethane (TPU) as an alternative elastomer material, a bending angle of 40° and a tip displacement of 18 mm could be achieved with the same actuator dimensions and optimized parameters. The simple unimorph bending actuators are promising tools for sensitive grippers on soft robots.
The Influence of Augmented Reality on Purchase Intention in the Housing Industry

Rapid emergence of technology communication has demanded the business organization to adapt with the most optimal marketing approach to ensure long-term survival in the modern business environment. Research on technologies adaptation in relation to marketing were conducted in various sectors in order to study the marketing communication channels in persuading targeted customers to purchase the selling products or utilize the services offered in the market. In contrast, the advancement of technology was not thoroughly harnessed by the property sector, which lagged in adopting the innovative marketing approach to promote their selling house. Accordingly, this research is aimed at investigating the effect of marketing communication channels via the adoption of AR to encourage the intention to purchase among the targeted house buyers. A total of 130 booklets of survey forms were distributed using the self-administered method along with the prompt material of housing design in AR applications. In return, 100 booklets of survey forms were received. As a result, the findings indicated that AR application as a marketing communication channel has an impact on the intention to purchase among potential house buyers. It is expected that AR technologies would create a new norm with extraordinary modification and advancement to promote housing projects and provide guidance to the developer. This initiative is able to improve their marketing communication, thus attracting house buyers based on their interests.
RBF neural network with optimal selection cluster algorithm and its application

In this paper, the RBF neural network (RBFNN) is used as a modeling framework to solve the identification problem of nonlinear systems. First, it is proposed a kind of optimal selection cluster algorithm. By this algorithm, it is optimally gained the hidden layer node number of RBFNN in terms of input samples. At the same time, the initial parameter values of RBF are obtained. Then, the parameters of RBF are estimated by the gradient algorithm with momentum terms, and the weights of RBFNN are identified by the recursive least square algorithm. The above two algorithms are alternately iterated. By the above hybrid algorithms, it is not only raised identification precision of RBFNN, but also improved the generalization property of the net. The validity of the scheme described is proved.
Nonrandomly Missing Data in Multiple Regression Analysis: An Empirical Comparison of Ten Missing Data Treatments

This study investigated the effectiveness of ten missing data treatments within the context of a two-predictor multiple regression analysis with nonrandomly missing data. Five distinct types of missing data treatments were examined: deletion (both listwise and pairwise methods), deterministic imputation (with imputations based on the sample mean, simple regression and multiple regression), stochastic imputation (mean, simple regression and multiple regression), maximum likelihood estimation (ML) and multiple imputation (MI). Design factors included in the study were sample size, total proportion of missing data, and the proportion of missing data occurring in the upper stratum of each predictor. The success of each method was evaluated based on the sample estimate of R 2 and each standardized regression coefficient. Results suggest that the stochastic multiple regression imputation procedure evidenced the best performance in providing unbiased estimates of the parameters of interest. Deterministic imputation approaches and the stochastic mean imputation approach resulted in large amounts of bias in the estimates. t is not uncommon for missing data to occur on one or more variables within an empirical investigation. Missing data may adversely affect data analyses, interpretations and conclusions. Collins, Schafer, and Kam (2001) indicate that missing data may potentially bias parameter estimates, inflate Type I and Type II error rates, and influence the performance of confidence bands. Further, because a loss of data is almost always associated with a loss of information, concerns arise with regard to reductions of statistical power. Unfortunately, researchers’ recommendations for managing missing data are not in complete agreement resulting in conceptual difficulties and computational challenges (Guertin, 1968; Beale & Little, 1975; Gleason & Staelin, 1975; Frane, 1976; Kim & Curry, 1977; Santos 1981; Basilevsky, Sabourin, Hum, & Anderson, 1985; Raymond & Roberts, 1987; Schafer & Graham, 2002). Many studies that have examined missing data treatments are not comparable due to the various methods used, the stratification categories (number of variables, sample size, proportion of missing data, and degree of multicollinearity), and the criteria that measure effectiveness of the missing data treatment (Anderson, Basilevsky, & Hum, 1983). Further, Schafer and Graham (2002) argue that the treatment of missing values cannot be properly evaluated apart from the modeling, estimation, or testing procedure in which it is rooted. Before proceeding with an examination of the extant literature, a consideration of some missing data terminology is warranted.
Cardiac Hamp mRNA Is Predominantly Expressed in the Right Atrium and Does Not Respond to Iron

Hepcidin is a liver-derived hormone that controls systemic iron traffic. It is also expressed in the heart, where it acts locally. We utilized cell and mouse models to study the regulation, expression, and function of cardiac hepcidin. Hepcidin-encoding Hamp mRNA was induced upon differentiation of C2C12 cells to a cardiomyocyte-like phenotype and was not further stimulated by BMP6, BMP2, or IL-6, the major inducers of hepatic hepcidin. The mRNAs encoding hepcidin and its upstream regulator hemojuvelin (Hjv) are primarily expressed in the atria of the heart, with ~20-fold higher Hamp mRNA levels in the right vs. left atrium and negligible expression in the ventricles and apex. Hjv−/− mice, a model of hemochromatosis due to suppression of liver hepcidin, exhibit only modest cardiac Hamp deficiency and minor cardiac dysfunction. Dietary iron manipulations did not significantly affect cardiac Hamp mRNA in the atria of wild-type or Hjv−/− mice. Two weeks following myocardial infarction, Hamp was robustly induced in the liver and heart apex but not atria, possibly in response to inflammation. We conclude that cardiac Hamp is predominantly expressed in the right atrium and is partially regulated by Hjv; however, it does not respond to iron and other inducers of hepatic hepcidin.
MOLECULAR SIMILARITY SEARCHING USING MEANING-FACTOR REDUCED GRAPH

Molecular similarity searching from molecular SMILES format using score base on reduced graphs with meaning factor has been proposed. The effectiveness of the method at is compared with searching using Tanimoto score. The searches have been carried out to find the potential drugs or molecules that form interactions by docking with Cov-Proteinase receptor which is SARS virus protein. Since the new proposed method yield almost higher scores than those obtained from Tanimoto method, the structurally diverse sets of active molecules are more retrieved.
Some Remarks on Schwarz–Christoffel Transformations from the Unit Disk to a Regular Polygon and their Numerical Computation

In this article we give a description of some particular aspects of Schwarz–Christoffel transformations either from a theoretical point of view or related to numerical approach. Furthermore some interesting geometrical properties of these transforms are shown and displayed by means of a graphical interface. In particular, an approximation of the identity in the unit disk of by means of a sequence of Schwarz–Christoffel transformations is exhibited.
Some Intuitionistic Topological Notions of Intuitionistic Region, Possible Application to GIS Topological Rules

In Geographical information systems (GIS) there is a need to model spatial regions with intuitionistic boundary. In this paper, we generalize the topological ideals spaces to the notion of intuitionistic set; we construct the basic fundamental concepts and properties of an intuitionistic spatial region. In addition, we introduce the notion of ideals on intuitionistic set which is considered as a generalization of ideals studies in (4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16). The important topological intuitionistic ideal has been given. The concept of intuitionistic local function is also introduced for a intuitionistic topological space. These concepts are discussed with a view to find new intuitionistic topology from the original one. The basic structure, especially a basis for such generated intuitionistic topologies and several relations between different topological intuitionistic ideals are also studied here. Possible application to GIS topology rules are touched upon.
Improvement of Medium-Range Forecasts Using the Analog-Dynamical Method

AbstractThis study further develops the analog-dynamical method and applies it to medium-range weather forecasts. By regarding the forecast field as a small disturbance superimposed on historical analog fields, historical analog errors can be used to estimate and correct forecast errors. This method is applied to 10-day forecasts from the Global and Regional Assimilation and Prediction System (GRAPES). Both the distribution of atmospheric circulation and the pattern of sea surface temperature (SST) are considered in choosing the analog samples from a historical dataset for 2001–10 based on NCEP Final (FNL) data. The results demonstrate that the analog-dynamical method greatly reduces forecast errors and extends the period of validity of the global 500-hPa height field by 0.8 days, which is superior to results obtained using systematic correction. The correction effect at 500 hPa is increasingly significant when the lead time increases. Although the analogs are selected using 500-hPa height fields, the for.
Perspectives of informal street waste pickers in Loja-Ecuador

Waste pickers contribute to country environmental and sustainability goals, and yet they are a marginalised and precarious group that requires further research. Using a case study design of informal waste pickers in Loja-Ecuador, representatives from waste picker associations, recycling warehouses, and public agencies were interviewed about the dynamics of informal recycling in this city. In addition, street level waste pickers were interviewed to understand their difficulties. The data obtained were complemented by participant observation of the recycling work on the street. The results show that in the city of Loja, two types of waste pickers coexist —associated and non-associated—, those who are associated obtain higher profits although the main perceived benefit is to carry out the activity under the authority’s consent. Likewise, it was found that waste recovery is the main income source for most of the families engaged in this activity, however, the income generated varies by gender. Furthermore, the majority of waste pickers are women who, along with children and the elderly, are disadvantaged as compared with their male counterparts in performing this activity. Competition for recyclable material among waste pickers was cited as the main problem. The findings provide the basis for constructing an inclusive urban development process that considers the work of waste pickers within the framework of a popular and solidarity economy.
Runnig head: SELF-CONTROL DEMANDS AND PSYCHOLOGICAL DETACHMENT 1

31 In the present article, we investigate psychological detachment as a moderator of the positive 32 relationship of self-control demands (SCDs) and indicators of psychological strain. Based on 33 the propositions that a) SCDs are a source of work stress, which draws on and depletes 34 limited regulatory resources, and b) psychological detachment facilitates the recovery of that 35 resource, we expected that psychological detachment attenuates the positive relationships 36 between SCDs and psychological strain (ego depletion, need for recovery, emotional 37 exhaustion and depersonalization). We tested our prediction in two different studies with 38 hierarchical moderated regression analyses. Results of the first study (N= 445) provided 39 strong support for our prediction that psychological detachment buffers the adverse impact of 40 SCDs on strain. In the second study (N=426), we replicated our initial findings and tested the 41 theoretical assertion that psychological detachment is more effective in buffering those 42 stressors that deplete limited regulatory resources (SCDs) in contrast to stressors (job 43 ambiguity), which are considered to cause strain through other mechanisms. Contrastive 44 comparisons of the differential interaction patterns of psychological detachment with 45 stressors that induce self-control efforts and job ambiguity, supported our prediction that 46 psychological detachment is more effective in attenuating the adverse effects of SCDs on 47 psychological strain. 48
Data Flush

Data perturbation is a technique for generating synthetic data by adding “noise” to raw data, which has an array of applications in science and engineering, primarily in data security and privacy. One challenge for data perturbation is that it usually produces synthetic data resulting in information loss at the expense of privacy protection. The information loss, in turn, renders the accuracy loss for any statistical or machine learning method based on the synthetic data, weakening downstream analysis and deteriorating in machine learning. In this article, we introduce and advocate a fundamental principle of data perturbation, which requires the preservation of the distribution of raw data. To achieve this, we propose a new scheme, named data flush, which ascertains the validity of the downstream analysis and maintains the predictive accuracy of a learning task. It perturbs data nonlinearly while accommodating the requirement of strict privacy protection, for instance, differential privacy. We highlight multiple facets of data flush through examples.
Neural networks for estimating articulatory positions from speech

This talk describes an application of neural networks for estimating positions of various articulators (such as the tongue, the lips, etc.) in the vocal tract from the speech signal. In general, a neural network consists of a large number of interconnected computational elements. The networks that will be discussed in this paper include an input layer of nodes connected directly or through an intermediate layer of hidden nodes to an output layer. Iterative gradient search procedures are often used for determining the unknown parameters of the neural network, but these procedures are very slow for training neural networks with a large number of hidden nodes. For estimating articulator positions, it was found that the weights in the first layer could be set to fixed random values during the training procedure without degrading the performance of the network. The random fixed weights in the first layer permit the use of a fast noniterative procedure for determining the unknown parameters of the second layer.