Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Correspondence Analysis

Multiple correspondence analysis (MCA) is an extension of correspondence analysis (CA) which allows one to analyze the pattern of relationships of several categorical dependent variables. As such, it can also be seen as a generalization of principal component analysis when the variables to be analyzed are categorical instead of quantitative. Because MCA has been (re)discovered many times, equivalent methods are known under several different names such as optimal scaling, optimal or appropriate scoring, dual scaling, homogeneity analysis, scalogram analysis, and quantification method. Technically MCA is obtained by using a standard correspondence analysis on an indicator matrix (i.e., a matrix whose entries are 0 or 1). The percentages of explained variance need to be corrected, and the correspondence analysis interpretation of interpoint distances needs to be adapted.
Runtime Verification of Domain-Specific Models of Physical Characteristics in Control Software

Control logic of embedded systems is nowadays largely implemented in software. Such control software implements, among others, models of physical characteristics, like heat exchange among system components. Due to evolution of system properties and increasing complexity, faults can be left undetected in these models. Therefore, their accuracy must be verified at runtime. Traditional runtime verification techniques that are based on states and/or events in software execution are inadequate in this case. The behavior suggested by models of physical characteristics cannot be mapped to behavioral properties of software. Moreover, implementation in a general-purpose programming language makes these models hard to locate and verify. This paper presents a novel approach to explicitly specify models of physical characteristics using a domain-specific language, to define monitors for inconsistencies by detecting and exploiting redundancy in these models, and to realize these monitors using an aspect-oriented approach. The approach is applied to two industrial case studies.
CO2 exchange between air and water in an Arctic Alaskan and midlatitude Swiss lake: Importance of convective mixing

[1] CO2 exchange between lake water and the atmosphere was investigated at Toolik Lake (Alaska) and Soppensee (Switzerland) employing the eddy covariance (EC) method. The results obtained from three field campaigns at the two sites indicate the importance of convection in the lake in driving gas flux across the water-air interface. Measurements were performed during short (1-3 day) periods with observed diurnal changes between stratified and convective conditions in the lakes. Over Toolik Lake the EC net CO2 efflux was 114 +/- 33 mg C m(-2) d(-1), which compares well with the 131 +/- 2 mg C m(-2) d(-1) estimated by a boundary layer model (BLM) and the 153 +/- 3 mg C m(-2) d(-1) obtained with a surface renewal model (SRM). Floating chamber measurements, however, indicated a net efflux of 365 +/- 61 mg C m(-2) d(-1), which is more than double the EC fluxes measured at the corresponding times (150 +/- 78 mg C m(-2) d(-1)). The differences between continous (EC, SRM, and BLM) and episodic (chamber) flux determination indicate that the chamber measurements might be biased depending on the chosen sampling interval. Significantly smaller fluxes (p < 0.06) were found during stratified periods (51 +/- 42 mg C m(-2) d(-1)) than were found during convective periods (150 +/- 45 mg C m(-2) d(-1)) by the EC method, but not by the BLM. However, the congruence between average values obtained by the models and EC supports the use of both methods, but EC measurements and the SRM provide more insight into the physical-biological processes affecting gas flux. Over Soppensee, the daily net efflux from the lake was 289 +/- 153 mg C m(-2) d(-1) during the measuring period. Flux differences were significant (p < 0.002) between stratified periods (240 +/- 82 mg C m(-2) d(-1)) and periods with penetrative convection (1117 +/- 236 mg C m(-2) d(-1)) but insignificant if convection in the lake was weak and nonpenetrative. Our data indicate the importance of periods of heat loss and convective mixing to the process of gas exchange across the water surface, and calculations of gas transfer velocity using the surface renewal model support our observations. Future studies should employ the EC method in order to obtain essential data for process-scale investigations. Measurements should be extended to cover the full season from thaw to freeze, thereby integrating data over stratified and convective periods. Thus the statistical confidence in the seasonal budgets of CO2 and other trace gases that are exchanged across lake surfaces could be increased considerably.
Why so Few Women on Boards of Directors? Empirical Evidence from Danish Companies 1997-2007

This paper analyzes the determinants of women on the boards of directors based on a panel sample of all Danish companies in the private sector with more than 50 employees. The share of women on the boards of directors was 12 percent in 2007 and has only slowly increased during the period 1997-2007. We test three hypotheses on female board representation which we denote the female-led hypothesis, the tokenism hypothesis, and the pipeline hypothesis, respectively.Based on fixed effects estimation we find that the female-led hypothesis cannot be supported. Firms with a female chairman of the board of directors tend to have significantly fewer other non-staff board members. We find clear evidence of a tokenism behavior in Danish companies. Having one non-staff woman on the board is negatively related to the chance of hiring another woman for the board of directors. Finally, the pipeline hypothesis is partly confirmed. The share of women among the group of CEOs and VPs from other firms in the industry is positively related to having a women on the board.
Where Are You Talking From?: Estimating the Location of tweets Using Recurrent Neural Networks

How can we estimate the location of user-generated content using textual data without location-specific information to understand urban space? Understanding urban space is important to tackle the issues that cities face, e.g. disasters, traffic congestion. Although event information reported with location data on microblog are very informational, many users post them without their locations because of the privacy concerns. To address this issue, some studies have attempted to estimate the location where the users post their tweets by analyzing the text. While those works have introduced various techniques for effective estimation, they have taken a lot of effort to do so. In this paper, we propose an approach that can estimate the location without those efforts. To achieve this goal, we adopt bidirectional Long-Short Term Memory (BLSTM). In our experiment, we use the geotagged tweets that were posted in Japan and treat location estimation as a multi-class classification problem where the location of tweets should be classified into administrative districts. As a result, we show that our proposed method can classify the location of tweets with higher accuracy than baseline methods.
Exploratory Factor Analysis Theory and

1. INTRODUCTION Many scientific studies are featured by the fact that " numerous variables are used to characterize objects " (Rietveld & Van Hout 1993: 251). Examples are studies in which questionnaires are used that consist of a lot of questions (variables), and studies in which mental ability is tested via several subtests, like verbal skills tests, logical reasoning ability tests, etcetera (Darlington 2004). Because of these big numbers of variables that are into play, the study can become rather complicated. Besides, it could well be that some of the variables measure different aspects of a same underlying variable. For situations such as these, (exploratory 1) factor analysis has been invented 2. Factor analysis attempts to bring intercorrelated variables together under more general, underlying variables. More specifically, the goal of factor analysis is to reduce " the dimensionality of the original space and to give an interpretation to the new space, spanned by a reduced number of new dimensions which are supposed to underlie the old ones " (Rietveld & Van Hout 1993: 254), or to explain the variance in the observed variables in terms of underlying latent factors " (Habing 2003: 2) Thus, factor analysis offers not only the possibility of gaining a clear view of the data, but also the possibility of using the output in subsequent analyses (Field 2000; Rietveld & Van Hout 1993). In this paper an example will be given of the use of factor analysis. This will be done by carrying out a factor analysis on data from a study in the field of applied linguistics, using SPSS for Windows. For this to be understandable, however, it is necessary to discuss the theory behind factor analysis. As the goal of this paper is to show and explain the use of factor analysis in SPSS, the theoretical aspects of factor analysis will here be discussed from a practical, applied perspective. Moreover, already plenty theoretical treatments of factor analysis exist that offer excellent and comprehensive overviews of this technique (see for instance Field 2000: 470; Rietveld & Van Hout 1993: 293-295). It would therefore be useless to come up with another purely theoretical discussion. The point of departure in this 'theoretical' discussion of factor analysis is a flow diagram from Rietveld & Van Hout (1993: 291) that offers an overview of the steps in factor analysis. Treating the theory in such a stepwise and practical fashion …
Saving Free Trade: A Pragmatic Approach

American Supporters of free trade are on the defensive. Record U.S. trade deficits are fueling demands from industry, Congress, and the public for tariffs, import quotas, and other protectionist measures that could reverse Americas long-standing commitment to open markets and sacrifice much of the economic progress experienced in recent years. In Saving Free Trade: A Pragmatic Approach, Robert Z. Lawrence and Robert E. Litan analyze both the allure of protectionism and the problems associated with free trade, proposing reasonable, cost-effective ways of helping industries, workers, and communities battered by intense import competition. The book focuses on the escape clause of the U.S. Trade Act of 1974, meant to provide domestic industries temporary shelter from severe import competition, and the trade adjustment assistance program, designed to provide direct aid to companies, workers, and communities injured by imports. The authors analyze the assumptions and implication of the many current congressional attempts to amend the provisions of the escape clause and the assistance program. They then set forth their own proposals, including new definitions of import injuries, modifications of provisions for providing relief for beleaguered companies, new standards for compensating and retaining displaced workers, and a plan for insuring communities against severe losses to their tax bases if local industries fail because they can no longer compete. Saving Free Trade provides a detailed but nontechnical introduction to the complex implications of amending trade policy and shrewd, innovative proposals for improving Americas ability to adapt to rapid changes in world markets.
Autorregresive Conditional Volatility, Skewness and Kurtosis

This paper proposes a GARCH-type model allowing for time-varying volatility, skewness and kurtosis. The model is estimated assuming a Gram-Charlier series expansion of the normal density function for the error term, which is easier to estimate than the non-central t distribution proposed by Harvey and Siddique (1999). Moreover, this approach accounts for time-varying skewness and kurtosis while the approach by Harvey and Siddique (1999) only accounts for nonnormal skewness. We apply this method to daily returns of a variety of stock indices and exchange rates. Our results indicate a significant presence of conditional skewness and kurtosis. It is also found that specifications allowing for time-varying skewness and kurtosis outperform specifications with constant third and fourth moments.
Decision Level Fusion of Colour Histogram Based Classifiers for Clustering of Mouth Area Images

It is well known that in many situations combining diverse classifiers can improve the performance of a classification system. In this paper, a new histogram based lip segmentation technique is proposed considering local kernel histograms in different illumination invariant colour spaces. The histogram is computed in local areas using two Gaussian kernels; one in the colour space and the other in the spatial domain. Using the estimated histogram, the posterior probability associated to non-lip class is then computed for each pixel. This process is performed considering different colour spaces. A weighted averaging method is then used for fusing the posterior probability values. As the result a new score is obtained which is used for labeling the pixels as lip or non-lip. The advantage of the proposed method is that the segmentation process is totally unsupervised. So, the method is robust against different variations such as variation in lip shape, skin colour, facial hair, illumination, etc. Moreover, an improved performance is achieved by fusing colour information.
An interactive simulation programming system which converses in English

In this paper an experimental system for producing simulation programs through natural language dialogue with a computer is described. With the current version of this system, which operates under CP/CMS on an IBM 360/67, a queuing problem may be stated to the computer in English. The system checks the problem description for completeness, and if necessary, asks questions which may be answered in English. Then it can produce an English description of the problem as it “sees” it and a GPSS program for performing the simulation. The user may then modify the problem through further dialogue to produce additional programs, as desired. A complete sample problem is included in the paper.
Consistency conditions and trace anomalies in six dimensions

Conformally invariant quantum field theories develop trace anomalies when defined on curved backgrounds. We study again the problem of identifying all possible trace anomalies in d = 6 by studying the consistency conditions to derive their ten independent solutions. It is known that only four of these solutions represent true anomalies, classified as one type A anomaly, given by the topological Euler density, and three type B anomalies, made up by three independent Weyl invariants. However, we also present the explicit expressions of the remaining six trivial anomalies, namely those that can be obtained by the Weyl variation of local functionals. The knowledge of the latter is in general necessary to disentangle the universal coefficients of the type A and B anomalies from calculations performed on concrete models.
PCA fused NN approach for drill wear prediction in drilling mild steel specimen

The present paper describes use of principal components for drill wear prediction. It also makes a comparative analysis in using large sensor based technique in predicting drill wear. In order to reduce the redundancy of the network, principal component has been fused with artificial neural network (ANN) for prediction of drill wear. Large numbers of experiments have been conducted and sensor signals have been acquired using data acquisition system. Cutting force, torque, vibrations along with other process parameters such as spindle speed, feed rate, drill diameter, chip thickness and surface roughness have been used as indicative parameters for characterizing the progressive wear of drill. Principal component of these input parameters has been derived thereafter and has been used to predict the flank wear using BPNN
Performance of coherent UWB Rake receivers with channel estimators

In this paper, the performances of DS-UWB and TH-UWB for a single-user link are compared for a data rate of 100 Mbps using the newly proposed, realistic UWB channel model from IEEE P802.15. The performance of suboptimal, fractionally spaced (FS), coherent Rake receivers with a pulse-matched filter is numerically evaluated with two channel estimation algorithms. DS-UWB and TH-UWB with antipodal modulation perform basically the same. The FS-Rake receiver performs much better than chip- and symbol-spaced Rake receivers. The best evaluated channel estimation algorithm gives only a fractional loss compared with the perfectly estimated channel. However, since TH-UWB is more difficult to synchronize, the conclusion is that DS-UWB is more suitable for high-speed indoor links.
The Effects of Child Support Payments on Developmental Outcomes for Elementary School-Age Children

Past research suggests that increasing the incomes of single mothers will bring integenerational benefits. However, some sources of income may be more beneficial to children than others. This paper evaluates the effects of child support payments from absent fathers on children's achievement test scores and home environments, using three methods to control for heterogeneity among families. The results provide evidence that increased child support payments may improve the academic achievement of elementary school-age children even more than income from other sources. While overall family income appears to affect levels of cognitive stimulation available in children's homes, child support does not have larger effects than other sources of income. These findings suggest that increasing the financial contributions of absent fathers through improved child support enforcement or other interventions may be a particularly beneficial income support strategy for children in single mother families.
A survey on area coverage in wireless sensor networks

In recent years, Wireless Sensor Networks (WSNs) have gained much attention because of its varying applications from catastrophic region to industrial and household region. In few applications, sensors are deployed in extreme environmental conditions. Hence, node access is not possible in that scenario. Therefore, a large number of sensor nodes are deployed in the target field so that node replacement problem is eliminated. Also, coverage is a very important parameter because it measures how effectively a target field is monitored by the sensor network. This paper focuses on the coverage issue in wireless sensor networks. Initially, three different types of coverage issues are discussed. Then, full coverage issue is examined by considering different points such as node type, deployment type, relation of communication range to sensing range, strategy used to detect full coverage and positioning based/independent algorithms. Some applications of wireless sensor networks are given. Finally, research challenges in the field of area coverage are discussed.
Optimal vector control for wound rotor salient pole synchronous motors over full speed range

This contribution introduces an optimal control for wound rotor salient pole synchronous motor intended for a new mining machine drive. The optimum considers demand for maximum torque of the motor. Hence, the main idea is to find optimal values of stator flux current Isd and rotor field excitation current If to achieve maximum available torque of the machine. This contribution describes proposed control. The theoretical conclusions are verified by simulation results and extensive experiments made on developed down-scale prototype of the investigated drive.
Binning: Converting Numerical Classification into Text Classification

Consider a supervised learning problem in which examples contain both numericaland text-valued features. One common approach to this problem would be to treat the presence or absence of a word as a Boolean feature, which when combined with the other numerical features enables the application of a range of traditional feature-vector-based learning methods. This paper presents an alternative approach, in which numerical features are converted into “bag of word” features, enabling instead the use of a range of existing text-classification methods. Our approach creates a set of bins for each feature into which its observed values can fall. Two tokens are defined for each bin endpoint, representing which side of a bin’s endpoint a feature value lies. A numerical feature is then assigned the bag of tokens appropriate for its value. Not only does this approach now make it possible to apply text-classification methods to problems involving both numerical and text-valued features, even problems that contain solely numerical features can be converted using this representation so that text-classification methods can be applied. We therefore evaluate our approach both on a range of real-world datasets taken from the UCI Repository that solely involve numerical features, as well as on additional datasets that contain both numericaland text-valued features. Our results show that the performance of the text-classification methods using the binning representation often meets or exceeds that of traditional supervised learning methods (C4.5, k-NN, NBC, and Ripper), even on existing numericalfeature-only datasets from the UCI Repository, suggesting that text-classification methods, coupled with binning, can serve as a credible learning approach for traditional supervised learning
A Semiparametric Estimator of Random Eeects

The use of a linear estimator to estimate random eeects in a Mixed Model is not necessarily optimal if the prior distribution is non-normal. Either a frequentist or Bayesian approach leads to the Best Linear Unbiased Predictor (BLUP), but an empirical Bayes approach produces a multivariate, non-linear, single-pass kernel-based estimator (the General Empirical Bayes or GEB es-timator) that allows relaxed distributional assumptions on the parameters. A suitable loss function is motivated to allow selection of a bandwidth. This loss function requires a technical modii-cation to the familiar kernel form, and this modiied estimator is shown to be asymptotically optimal. 2 3 The GEB estimator is implemented using Splus software. We introduce a class of densities which allow a rotation to independence , and restrict attention to these densities. Simulated longitudinal studies allow comparison of the GEB estimator with the BLUP using three diierent classes of priors. The GEB is found to outperform the BLUP when the prior is Cauchy and for some normal mixtures.
Resistance to β-Lactam Antibiotics Conferred by Point Mutations in Penicillin-Binding Proteins PBP3, PBP4 and PBP6 in Salmonella enterica

Penicillin-binding proteins (PBPs) are enzymes responsible for the polymerization of the glycan strand and the cross-linking between glycan chains as well as the target proteins for β-lactam antibiotics. Mutational alterations in PBPs can confer resistance either by reducing binding of the antibiotic to the active site or by evolving a β-lactamase activity that degrades the antibiotic. As no systematic studies have been performed to examine the potential of all PBPs present in one bacterial species to evolve increased resistance against β-lactam antibiotics, we explored the ability of fifteen different defined or putative PBPs in Salmonella enterica to acquire increased resistance against penicillin G. We could after mutagenesis and selection in presence of penicillin G isolate mutants with amino-acid substitutions in the PBPs, FtsI, DacB and DacC (corresponding to PBP3, PBP4 and PBP6) with increased resistance against β-lactam antibiotics. Our results suggest that: (i) most evolved PBPs became ‘generalists” with increased resistance against several different classes of β-lactam antibiotics, (ii) synergistic interactions between mutations conferring antibiotic resistance are common and (iii) the mechanism of resistance of these mutants could be to make the active site more accessible for water allowing hydrolysis or less binding to β-lactam antibiotics.