Miracle-dz/UniFAR-FTUs huggingface dataset:
The subjective well-being of academically gifted students in the Chinese cultural context

This study examined the subjective well-being, as reflected as their satisfaction with life, of academically gifted secondary school students in China, under the framework of the broaden-and-build theory. A total number of 351 academically gifted secondary students participated in the study. Multiple scales were used to measure these academically gifted students’ satisfaction with life and other relevant constructs. The results indicated that girls had significantly higher satisfaction with life than boys. Consistent with the broaden-and-build theory, the study showed that positive emotions and some variables from the broadened mindset and four building resources aspects of the theory were significantly interrelated for these academically gifted students. Based on the hierarchical regression results, positive affect, agency hope, parents’ trust, general health, and social functioning were found to be contributing factors to the subjective well-being, while negative affect, peers alienation, depression, anxiety, were found to be negatively related to their life satisfaction.
Metabolic Syndrome and Associated Factors in Adults of the Amazon Region

Metabolic syndrome (MS) plays a key role in the origin of cardiovascular diseases. Studies on the MS in Brazil are recent, and its epidemiology in more isolated regions such as the Amazon is still unknown. The study aimed to estimate the prevalence of MS and associated factors in adults of the Brazilian Amazon. This study was conducted in 2012–2013. It is a cross-sectional population-based study, involving 787 adults randomly selected from the urban area of four cities in the state of Pará, in the Brazilian Eastern Amazon. The participants underwent anthropometric measurements, laboratory examination, and were questioned about their lifestyle. MS was defined by the Joint Interim Statement criteria, using the multiple logistic regression to investigate the potential association of risk factors with the presence of MS. The overall prevalence of MS was 34.1% (95% CI = 30.8–37.4), increasing linearly with the increasing body mass index and age. From 40–49 years of age, MS was observed in about half of the women (46.0%), while men only experienced a high prevalence in the fifth decade of life (43.3%). The low HDL-c (64.4%) and abdominal obesity (58.9%) were higher in women (p < 0.001), while for men, high blood pressure was significantly higher (p < 0.001). Individuals aged 40–59 years old (odds ratio [OR] = 3.35 [95% CI = 2.30–4.90]), ≥ 60 years old (OR = 5.80 [3.63–9.27]), overweight (OR = 4.17 [2.77–6.29]), and obese (OR = 8.82 [5.56–13.98]) were more likely to have MS. The study population experienced high cardiometabolic risk, requiring government efforts to control MS and related risk factors, especially obesity.
Applied Economics

Happiness research has been a great success by yielding new and important insights. These results are often used in a technocratic manner: Governments should maximize, or at least raise, the subjective well-being of the population measured by the national happiness index. Yet the government has strong incentives and a wide range of possibilities to manipulate this index to its advantage. Happiness policy must be part of the normal democratic process where divergent views are considered and aggregated. In particular, competition between parties is a prerequisite for the insights from happiness research put to the bene ﬁ t of the citizens.
Shallow Semantic Parsing using Support Vector Machines

In this paper, we propose a machine learning al-gorithm for shallow semantic parsing, extending the work of Gildea and Jurafsky (2002), Surdeanu et al. (2003) and others. Our al-gorithm is based on Support Vector Machines which we show give an improvement in performance over earlier classiﬁers. We show performance improvements through a number of new features and measure their ability to generalize to a new test set drawn from the AQUAINT corpus.
Instant Annotations – Applying NLP Methods to the Annotation of Spoken Language Documentation Corpora

Thepaper describes work-in-progress by the Pite Saami, Kola Saami and Izhva Komi language documentation projects, all of which use similar data and technical frameworks and are carried out in Freiburg and in collaboration with Hamburg, Syktyvkar, Tromsø and Uppsala. Our projects work in the endangered language documentation framework and record new spoken language data, digitize available recordings and annotate these multimedia data in order to provide comprehensive language corpora as databases for future research on and for endangered and under-described Uralic speech communities. Applying NLP methods in language documentation – specifically rule-based morphological and syntactic analyzers – helps us to create more systematically annotated corpora, rather than eclectic data collections. We propose a step-by-step approach to reach higherlevel annotations by using and improving truly computational methods. Ultimately, the spoken corpora created by our projects will be useful for scientifically significant quantitative investigations on these languages in the future.
Creating a Comprehensive Agent-oriented Methodology

In an electronic pattern generator characters to be recorded are displayed on the screen of a cathode ray tube and then exposed on a recording medium mounted on a carrier, which is displacable in a plane parallel to the plane of the screen. The characters are displayed from video-data recallable from a store. The recalled video-data are transformed into a first video signal for the brightness control of the electron beam of the cathode ray tube into first deflection signals for the beam and into displacement signals for the carrier. For controlling the make-up and composing process the characters are displayed identically on a further screen of a soft copy terminal independently of the display on the screen of the cathode ray tube. By identical display it is understood, that the various type fonts and sizes of characters are not displayed in an unitary fashion, but in the original font of writing, as they will appear later in the typographical composition on the recording medium. By identical display it is further understood, that also the appropriate displacement motions of the carrier are taken into account when displaying the soft copy for control purpose. The identical display is achieved by storing the first video signal and transforming it into a second video signal for the soft copy terminal with the help of different read-in and read-out signals for the storage means and different second deflection signals for the screen of the soft copy terminal.
The impact of genetic heterogeneity on biomarker development in kidney cancer assessed by multiregional sampling

Primary clear cell renal cell carcinoma (ccRCC) genetic heterogeneity may lead to an underestimation of the mutational burden detected from a single site evaluation. We sought to characterize the extent of clonal branching involving key tumor suppressor mutations in primary ccRCC and determine if genetic heterogeneity could limit the mutation profiling from a single region assessment. Ex vivo core needle biopsies were obtained from three to five different regions of resected renal tumors at a single institution from 2012 to 2013. DNA was extracted and targeted sequencing was performed on five genes associated with ccRCC (von‐Hippel Lindau [VHL], PBRM1, SETD2, BAP1, and KDM5C). We constructed phylogenetic trees by inferring clonal evolution based on the mutations present within each core and estimated the predictive power of detecting a mutation for each successive tumor region sampled. We obtained 47 ex vivo biopsy cores from 14 primary ccRCC's (median tumor size 4.5 cm, IQR 4.0–5.9 cm). Branching patterns of various complexities were observed in tumors with three or more mutations. A VHL mutation was detected in nine tumors (64%), each time being present ubiquitously throughout the tumor. Other genes had various degrees of regional mutational variation. Based on the mutations' prevalence we estimated that three different tumor regions should be sampled to detect mutations in PBRM1, SETD2, BAP1, and/or KDM5C with 90% certainty. The mutational burden of renal tumors varies by region sampled. Single site assessment of key tumor suppressor mutations in primary ccRCC may not adequately capture the genetic predictors of tumor behavior.
The Effect of Musical Aptitude: Perceiving and Producing Mandarin Lexical Tones By Non-Native Speakers of Chinese

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Integrating Deep and Shallow Models for Multi-Modal Depression Analysis—Hybrid Architectures

At present, although great progress has been made in automatic depression assessment, most of the recent works only concern the audio and video paralinguistic information, rather than the linguistic information from the spoken content. In this work, we argue that beside developing good audio and video features, to build reliable depression detection systems, text-based content features are also of importance to analyse depression-related textual indicators. Furthermore, to improve the performance of automatic depression assessment systems, powerful models, capable of modelling the characteristics of depression embedded in the audio, visual and text descriptors, are also required. This paper proposes new text and video features and hybridizes deep and shallow models for depression estimation and classification from audio, video and text descriptors. The proposed hybrid framework consists of three main parts: 1) A Deep Convolutional Neural Network (DCNN) and Deep Neural Network (DNN) based audio-visual multi-modal depression recognition model for estimating the Patient Health Questionnaire depression scale (PHQ-8); 2) A Paragraph Vector (PV) and Support Vector Machine (SVM) based model for inferring the physical and mental conditions of the individual from the transcripts of the interview; 3) A Random Forest (RF) model for depression classification from the estimated PHQ-8 score and the inferred conditions of the individual. In the PV-SVM model, PV embedding is used to obtain fixed-length feature vectors from transcripts of the answers to the questions associated with psychoanalytic aspects of depression, which are subsequently fed into the SVM classifiers for detecting the presence/absence of the considered psychoanalytic symptoms. To our best knowledge, this approach is the first attempt to apply PV for depression analysis. Besides, we propose a new visual descriptor - Histogram of Displacement Range (HDR) to characterize the displacement and velocity of the facial landmarks in the video segment. Experiments have been carried out on the Audio Visual Emotion Challenge (AVEC2016) depression dataset, they demonstrate that: 1) The proposed hybrid framework effectively improves the accuracies of both depression estimation and depression classification, with an average F1 measure up to 0.746, which is higher than the best result (0.724) of the depression sub-challenge of AVEC2016. 2) HDR obtains better depression recognition performance than Bag-of-Words (BoW) and Motion History Histogram (MHH) features.
Review Article: The New Political Economy of Skill Formation

More than any other period in history, our present times may be aptly labeled the Age of Human Capital. Economists from Gary Becker to Robert Barro, Daron Acemoglu and Paul Romer have driven home the message that the effect of education in improving the economic wellbeing of persons and nations is strong, and has been increasing over the last decades (Vanhuysse and Sabbagh, 2004). Policies aimed at boosting human capital contribute to wealth in at least two ways: by increasing (future) workers' ability to invent, adopt, or diffuse new ideas and technologies, and by raising workers' output per unit of time. Both books under review underscore the key role of skills and human capital in contemporary economies. Written by leading theorists in economics and political economy, these ambitious books present wide-ranging theoretical arguments grounded in extensive empirical evidence.
Bias hypotheses under scrutiny: investigating the validity of student assessment of university teaching by means of external observer ratings

Abstract To advance the discussion on the validity of student evaluations of university teaching, student ratings of two teaching dimensions – student involvement and rapport – were compared with corresponding observer ratings. Seven potential bias variables were tested with regard to their impact on the students’ teaching assessment: three teacher characteristics (first impression, enthusiasm, humour) and four student characteristics (prior interest, expected grades, study experience, class attendance). Bias was defined as an impediment of the students’ assessment of teaching on course level. By means of bivariate correlations with course averages and two-level latent moderated structural equations, data of 1,716 students in 80 courses were analysed. Results showed that all three teacher characteristics were genuinely connected to rapport, and even explained variance of the student-rated variable when controlling for observer-rated rapport. The assessment of student involvement was not modified by the teacher characteristics except for teacher enthusiasm, which affected the student evaluation when controlling for observed involvement and, moreover, moderated the relation between the observed and the student-rated variable. For the examined student characteristics, no biasing effects were found – neither on rapport nor on student involvement.
Low‐degree mantle convection with strongly temperature‐ and depth‐dependent viscosity in a three‐dimensional spherical shell

[1] A series of numerical simulations of thermal convection of Boussinesq fluid with infinite Prandtl number, with Rayleigh number 10 7 , and with the strongly temperature-and depth-dependent viscosity in a three-dimensional spherical shell is carried out to study the mantle convection of single-plate terrestrial planets like Venus or Mars without an Earth-like plate tectonics. The strongly temperature-dependent viscosity (the viscosity contrast across the shell is > 10 5 ) makes the convection under stagnant lid short-wavelength structures. Numerous, cylindrical upwelling plumes are developed because of the secondary downwelling plumes arising from the bottom of lid. This convection pattern is inconsistent with that inferred from the geodesic observation of the Venus or Mars. Additional effect of the stratified viscosity at the upper/lower mantle (the viscosity contrast is varied from 30 to 300) are investigated. It is found that the combination of the strongly temperature- and depth-dependent viscosity causes long-wavelength structures of convection in which the spherical harmonic degree l is dominant at 1-4. The geoid anomaly calculated by the simulated convections shows a long-wavelength structure, which is compared with observations. The degree-one (l = 1) convection like the Martian mantle is realized in the wide range of viscosity contrast from 30 to 100 when the viscosity is continuously increased with depth at the lower mantle.
What is business school research for? Academic and stakeholder perspectives, politics and relationality

ABSTRACT Through an empirically grounded analysis of contested interpretations of impact held by business school academics and wider stakeholders, we provide a political and relational understanding of research impact whereby impacts are realized through situated, relational and ongoing interactions. Our qualitative study of three UK business and management schools, comprising interviews with around 70 researchers, academic leaders and stakeholders explores conceptualizations of impact, examines the processes through which impact is achieved and considers the influence of the Research Excellence Framework (REF). The inclusion of academic leaders and business stakeholders adds value to studies that have focused primarily on academic researchers. We present a nuanced picture of the ways in which academics and business stakeholders conceptualize impact and navigate the complexity of roles and relationships in this arena. Implications for practice include the desirability of embracing different understandings of impact, creating space for generative dialogue and incorporating impact more explicitly in teaching and learning.
Face Recognition in Different Subspaces: A Comparative Study

Face recognition is one of the most successful applications of image analysis and understanding and has gained much attention in recent years. Among many approaches to the problem of face recognition, appearance-based subspace analysis still gives the most promising results. In this paper we study the three most popular appearance-based face recognition projection methods (PCA, LDA and ICA). All methods are tested in equal working conditions regarding preprocessing and algorithm implementation on the FERET data set with its standard tests. We also compare the ICA method with its whitening preprocess and find out that there is no significant difference between them. When we compare different projection with different metrics we found out that the LDA+COS combination is the most promising for all tasks. The L1 metric gives the best results in combination with PCA and ICA1, and COS is superior to any other metric when used with LDA and ICA2. Our results are compared to other studies and some discrepancies are pointed out
Does Religion Buffer the Effects of Discrimination on Distress for Religious Minorities? The Case of Arab Americans

Religiosity is well documented as a coping resource that protects against the effects of discrimination on distress, but little is known about the utility of religious minorities’ religiosity. This study investigates if religious resources buffer the effect of discrimination on distress for Arab Americans and if that relationship differs based on religious minority status. Following the contours of the stress process model, I leverage data from the 2003 Detroit Arab American Survey (DAAS; N = 977) to test the relationship between distress, discrimination, and three measures of religiosity: frequency of religious service attendance, God salience, and salient religious practices. The results indicate that for the overall sample, only salient religious practices buffered discrimination. However, when religious affiliation was taken into account, the results indicate that both God salience and salient religious practices significantly buffer discrimination for Muslim respondents only. I conclude with theoretical contributions, study limitations, and future research avenues.
Semantically-based recommendation by using semantic clusters of users' viewing history

Collaborative filtering-based recommender systems assume that if two users have shown similar interest on the same set of contents, they may show a similar interest-pattern in choosing future contents. However, users who have certain tastes on one specific category of contents may behave differently in choosing contents from other categories. Moreover, the collaborative filtering approaches may not work efficiently with sparse data sets, where there are a small number of contents or a limited number of users in the content categories. In this paper, to deal with these problems, we propose a novel approach of recommending contents across different categories by considering both the semantic information of contents and user interests. The proposed approach uses Linked Data as the source to find the appropriate semantics of the contents extracted from users' viewing history. The semantic concepts retrieved for the contents are then grouped together into semantic clusters based on their similarity and relevance. Our approach recommends potentially interesting contents to general users based on the content-consumption trends monitored from leading user groups who most proactively and frequently consume contents. We demonstrate the effectiveness of our approach by conducting an experiment with the real-world mobile IPTV service data obtained from one of the popular IPTV service providers in South Korea.
A Combined RESTART - Cross Entropy Method for Rare Event Estimation with Applications to ATM Networks

We present a fast algorithm for the efficient estimation of rare-event (buffer overflow) probabilities in queueing networks. Our algorithm presents a combined version of the two well known methods, called RESTSART/splitting and cross-entropy , where in the latter the optimal change of measure (importance sampling) is determined adaptively during the simulation, using the cross-entropy method. For queueing models we consider two well known importance sampling policies: the so-called fixed (stable) queueing policy and the so-called switching (unstable) queueing policy and compare their efficiencies. In particular, we show that for the switching policy the combined algorithm is typically much faster than its counterpart, the combined algorithm with fixed policy. We also show that the switching policy combined algorithm reduces, in fact, to a degenerated case presenting a simple single-level cross-entropy algorithm. Simulation results for queueing models of ATM-type are presented. Our numerical results demonstrate high efficiency of the proposed method as compared to the original RESTART/splitting.
Evaluating extensions to coherent mortality forecasting models

Coherent models were developed recently to forecast the mortality of two or more sub-populations simultaneously and to ensure long-term non-divergent mortality forecasts of sub-populations. This paper evaluates the forecast accuracy of two recently-published coherent mortality models, the Poisson common factor and the product-ratio functional models. These models are compared to each other and the corresponding independent models, as well as the original Lee–Carter model. All models are applied to age-gender-speciﬁc mortality data for Australia and Malaysia and age-gender-ethnicity-speciﬁc data for Malaysia. The out-of-sample forecast error of log death rates, male-to-female death rate ratios and life expectancy at birth from each model are compared and examined across groups. The results show that, in terms of overall accuracy, the forecasts of both coherent models are consistently more accurate than those of the independent models for Australia and for Malaysia, but the relative performance differs by forecast horizon. Although the product-ratio functional model outperforms the Poisson common factor model for Australia, the Poisson common factor is more accurate for Malaysia. For the ethnic groups application, ethnic-coherence gives better results than gender-coherence. The results provide evidence that coherent models are preferable to independent models for forecasting sub-populations’ mortality.