Miracle-dz/UniFAR-FTUs huggingface dataset:
Identity and Career Maturity in Kinesiology Students

The purpose of this study was to explore athletic identity, identity foreclosure, and career maturity in a sample of undergraduate college students currently enrolled in kinesiology and physical education classes at a university in the southern United States. Students were provided with an internet link that requested them to complete a demographic survey, the Athletic Identity Measurement Scale (AIMS), the foreclosure subscale of the Extended Objective Measure of Ego Identity Status, and the Attitude Scale (Form A-2) of the Career Maturity Inventory. Examination of the Pearson moment correlations indicated that the higher the sport participation during high school, the greater the athletic identity and identity foreclosure, and lower the career maturity attitudes. ANOVAs were performed to examine differences between males (n = 123) and females (n = 183), kinesiology (n = 181) and non-kinesiology majors (n = 125), and white (n = 144) and non-white students (n = 162) on athletic identity, identity foreclosure, and career maturity. Results showed that males scored significantly higher on athletic identity and identity foreclosure, and significantly lower on career maturity than females. Kinesiology students had scores significantly higher on athletic identity and identity foreclosure, and lower on career maturity. Finally, individuals that identified their ethnicity as White had higher athletic identity, lower identity foreclosure, and significantly higher career maturity than individuals who identified as an ethnicity other than white. Although the relationships in this study are in line with what has been found in previous research, the relationships among this sample of undergraduate students were weak. Future research should replicate the study using a measurable level of sport skill level. Future research should also consider introducing an intervention with a career development program, and track participants' athletic identity, identity foreclosure, and career mature before, during, and after implementation of the program.
Facial Expression Recognition with Pyramid Gabor Features and Complete Kernel Fisher Linear Discriminant Analysis

This paper presents a Facial Expression recognition (FER) approach using Pyramid Gabor features and Complete Fisher Kernel Linear Discriminant Analysis (CKFD). Based on the frequency locality of Gabor filters, we propose new pyramid Gabor features for FER, which can be extracted by two phases: multi-frequency channel decompositions and Gabor filtering with single frequency. Experiments show the proposed Pyramid Gabor features can reach better performance than traditional Gabor features in facial expression analysis and involve much less computations. Based on kernel principal component analysis (KPCA) and Fisher linear discriminant analysis (LDA), a complete Kernel Fisher Linear Disciminant Analysis was presented recently, which can carry out discriminant analysis in "double discriminant subspace". This paper expands the complete KFD to the field of facial expression recognition. We find the irregular information subspace is more dicriminant than the regular information subspace.
Moving beyond the Contingent Valuation versus Choice Experiment Debate: Presentation Effects in Stated Preference

We reexamine the literature comparing contingent valuation (CV) and choice experiments (CEs) in terms of stated preference design features, and empirically investigate an understudied dimension in these comparisons, namely, presentation effects. Structured analysis of the literature suggests that the comparisons of CV and CEs as broad descriptive terms may mask the many design and methodological differences seen in implementations of the approaches. In the empirical component, we find preference disclosure to be unaffected by text and table presentation formats in elicitation tasks, except when only the first task is considered. Implications of our findings for stated preference research are discussed. (JEL Q51)
Self-organizing agent communities for autonomic resource management

The autonomic computing paradigm addresses the operational challenges presented by increasingly complex software systems by proposing that they be composed of many autonomous components, each responsible for the runtime reconfiguration of its own dedicated hardware and software components. Consequently, regulation of the whole software system becomes an emergent property of local adaptation and learning carried out by these autonomous system elements. Designing appropriate local adaptation policies for the components of such systems remains a major challenge. This is particularly true where the system’s scale and dynamism compromise the efficiency of a central executive and/or prevent components from pooling information to achieve a shared, accurate evidence base for their negotiations and decisions. In this paper, we investigate how a self-regulatory system response may arise spontaneously from local interactions between autonomic system elements tasked with adaptively consuming/providing computational resources or services when the demand for such resources is continually changing. We demonstrate that system performance is not maximized when all system components are able to freely share information with one another. Rather, maximum efficiency is achieved when individual components have only limited knowledge of their peers. Under these conditions, the system self-organizes into appropriate community structures. By maintaining information flow at the level of communities, the system is able to remain stable enough to efficiently satisfy service demand in resource-limited environments, and thus minimize any unnecessary reconfiguration whilst remaining sufficiently adaptive to be able to reconfigure when service demand changes.
Face sequence recognition using Grassmann Distances and Grassmann Kernels

In this paper we show how Grassmann distances and Grassmann kernels can be efficiently used to learn and classify face sequence videos. We propose two new methods, the Grassmann Distance Mutual Subspace Method (GD-MSM) which uses Grassmann distances to define the similarity between subspaces of images, and the Grassmann Kernel Support Vector Machine (GK-SVM), which applies two Grassmann kernels - the projection kernel and the Binet-Cauchy kernel - in a convex optimization scheme, using the Support Vector Machine (SVM) framework. GD-MSM and GK-SVM are compared in a face recognition task with several related methods using a large database of face image sequences from 100 subjects, containing expression changes related to a natural conversation setting. Additionally, we study the effect of combining all available training image sequences into a single subspace per category, in comparison with using multiple smaller subspaces, i.e. representing each category by several different subspaces, where each subspace is formed from image sequences taken under different conditions.
University of Groningen Antibodies against the Envelope Glycoprotein Promote Infectivity of Immature Dengue Virus Serotype 2

Cross-reactive dengue virus (DENV) antibodies directed against the envelope (E) and precursor membrane (prM) proteins are believed to contribute to the development of severe dengue disease by facilitating antibody-dependent enhancement of infection. We and others recently demonstrated that anti-prM antibodies render essentially non-infectious immature DENV infectious in Fcc-receptor-expressing cells. Immature DENV particles are abundantly present in standard (st) virus preparations due to inefficient processing of prM to M during virus maturation. Structural analysis has revealed that the E protein is exposed in immature particles and this prompted us to investigate whether antibodies to E render immature particles infectious. To this end, we analyzed the enhancing properties of 27 anti-E antibodies directed against distinct structural domains. Of these, 23 bound to immature particles, and 15 enhanced infectivity of immature DENV in a furindependent manner. The significance of these findings was subsequently tested in vivo using the well-established West Nile virus (WNV) mouse model. Remarkably, mice injected with immature WNV opsonized with anti-E mAbs or immune serum produced a lethal infection in a dose-dependent manner, whereas in the absence of antibody immature WNV virions caused no morbidity or mortality. Furthermore, enhancement infection studies with standard (st) DENV preparations opsonized with anti-E mAbs in the presence or absence of furin inhibitor revealed that prM-containing particles present within st virus preparations contribute to antibody-dependent enhancement of infection. Taken together, our results support the notion that antibodies against the structural proteins prM and E both can promote pathogenesis by enhancing infectivity of prMcontaining immature and partially mature flavivirus particles. Citation: da Silva Voorham JM, Rodenhuis-Zybert IA, Ayala Nuñez NV, Colpitts TM, van der Ende-Metselaar H, et al. (2012) Antibodies against the Envelope Glycoprotein Promote Infectivity of Immature Dengue Virus Serotype 2. PLoS ONE 7(3): e29957. doi:10.1371/journal.pone. 0029957 Editor: Xia Jin, University of Rochester, United States of America Received October 27, 2011; Accepted December 7, 2011; Published March 14, 2012 Copyright: 2012 da Silva Voorham et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by the Pediatric Dengue Vaccine Initiative, by the Dutch Organization for Scientific research (NWO-Earth and Life Sciences and NWO-zonMW), and by Natinal Institutes of Health grants U01 AI061373 and R01-AI077955 (MSD). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: jolanda.smit@umcg.nl
News finds me perception and democracy: Effects on political knowledge, political interest, and voting

Recent scholarship suggests that overreliance on social networks for news and public affairs is associated with the belief that one no longer needs to actively seek information. Instead, individuals perceive that the “news will find me” (NFM) and detach from the regular habit of traditional news consumption. This study examines effects of the NFM perception on political knowledge, political interest, and electoral participation. Drawing on a nationally representative panel survey from the United States (N = 997), this study finds that the NFM perception is negatively associated with both political knowledge and political interest across two time periods. The NFM perception also leads to negative, indirect effects on voting as the relationship is mediated through lower reported levels of political knowledge and interest in politics. The findings add to current conversations about the ability of personalized information networks to adequately inform and engage the public.
The Ascent of Man: A Theoretical and Empirical Case for Blatant Dehumanization

Dehumanization is a central concept in the study of intergroup relations. Yet although theoretical and methodological advances in subtle, “everyday” dehumanization have progressed rapidly, blatant dehumanization remains understudied. The present research attempts to refocus theoretical and empirical attention on blatant dehumanization, examining when and why it provides explanatory power beyond subtle dehumanization. To accomplish this, we introduce and validate a blatant measure of dehumanization based on the popular depiction of evolutionary progress in the “Ascent of Man. ” We compare blatant dehumanization to established conceptualizations of subtle and implicit dehumanization, including infrahumanization, perceptions of human nature and human uniqueness, and implicit associations between ingroup–outgroup and human–animal concepts. Across 7 studies conducted in 3 countries, we demonstrate that blatant dehumanization is (a) more strongly associated with individual differences in support for hierarchy than subtle or implicit dehumanization, (b) uniquely predictive of numerous consequential attitudes and behaviors toward multiple outgroup targets, (c) predictive above prejudice, and (d) reliable over time. Finally, we show that blatant—but not subtle—dehumanization spikes immediately after incidents of real intergroup violence and strongly predicts support for aggressive actions like torture and retaliatory violence (after the Boston Marathon bombings and Woolwich attacks in England). This research extends theory on the role of dehumanization in intergroup relations and intergroup conflict and provides an intuitive, validated empirical tool to reliably measure blatant dehumanization.
The psychometric properties of the communicative competence scale for individuals with Aphasia using speech-generating devices

ABSTRACT Background: Persons with aphasia (PWA) use augmentative and alternative communication (AAC) systems to access and create symbol-based messages. The purpose of this study was to (a) investigate the psychometric properties of the newly developed Communicative Competence Scale (CCS), and (b) to determine if any factors, or subcategories, exist within each scale of the scale. The CCS aims to assess the competence of PWA using AAC. Aims: Respondents were 55 undergraduate and graduate speech-language pathology students. The students were recruited to test the statistical properties of the CCS and Conversational Skills Rating Scale (CSRS). Prior to completing both instruments, respondents watched two videos of a PWA communicating with a speech-generating device. The messages were displayed using a grid display in one video and a scene display in the other. Main contribution: Concurrent validity was established between the CCS and the CSRS, and a moderate correlation was found. Reliability Coefficients revealed that the CCS and the CSRS had acceptable levels of internal consistency. Factor analysis revealed factor loadings or subordinate categories within the CCS and CSRS that were consistent with previous research. Conclusions: The CCS was found to be an internally consistent, reliable, and valid measure of perceived communicative competence of PWA using aided AAC. Clinical implications are discussed.
Modeling of thermally induced skew variations in clock distribution network

Clock distribution network is sensitive to large thermal gradients on the die as the performance of both clock buffers and interconnects are affected by temperature. A robust clock network design relies on the accurate analysis of clock skew subject to temperature variations. In this work, we address the problem of thermally induced clock skew modeling in nanometer CMOS technologies. The complex thermal behavior of both buffers and interconnects are taken into account. In addition, our characterization of the temperature effect on buffers and interconnects provides valuable insight to designers about the potential impact of thermal variations on clock networks. The use of industrial standard data format in the interface allows our tool to be easily integrated into existing design flows.
Left ventricular geometry and severe left ventricular hypertrophy in children and adolescents with essential hypertension.

BACKGROUND Left ventricular (LV) hypertrophy has been established as an independent risk factor for cardiovascular disease in adults. Recent research has refined this relationship by determining a cutpoint of 51 g/m(2.7) for LV mass index indicative of increased risk and defining LV geometric patterns that are associated with increased risk. The purpose of this study was to evaluate severe LV hypertrophy and LV geometry in children and adolescents with essential hypertension. METHODS AND RESULTS A cross-sectional study of young patients (n=130) with persistent blood pressure elevation above the 90th percentile was conducted. Nineteen patients (14%) had LV mass greater than the 99th percentile; 11 of these were also above the adult cutpoint of 51 g/m(2.7). Males, subjects with greater body mass index, and those who had lower heart rate at maximum exercise were at significantly (P<.05) higher risk of severe LV hypertrophy. In addition, 22 patients (17%) had concentric LV hypertrophy, a geometric pattern that is associated with increased risk of cardiovascular disease in adults. Seven patients had LV mass index above the cutpoint and concentric hypertrophy. No consistent significant determinants of LV geometry were identified in these children and adolescents with hypertension. CONCLUSIONS Severe LV hypertrophy and abnormal LV geometry are relatively prevalent in young patients with essential hypertension. These findings suggest that these patients may be at risk for future cardiovascular disease and underscore the importance of recognition and treatment of blood pressure elevation in children and adolescents. Weight loss is an important component of therapy in young patients with essential hypertension who are overweight.
Title Stata.com Teffects Nnmatch — Nearest-neighbor Matching

Syntax Menu Description Options Remarks and examples Stored results Methods and formulas References Also see Syntax teffects nnmatch (ovar omvarlist) (tvar) if in weight , stat options stat Description Stat ate estimate average treatment effect in population; the default atet estimate average treatment effect on the treated options Description Model nneighbor(#) specify number of matches per observation; default is nneighbor(1) biasadj(varlist) correct for large-sample bias using specified variables ematch(varlist) match exactly on specified variables SE/Robust vce(vcetype) vcetype may be vce(robust , nn(#)); use robust Abadie–Imbens standard errors with # matches vce(iid); use default Abadie–Imbens standard errors Reporting level(#) set confidence level; default is level(95) dmvariables display names of matching variables display options control column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling Advanced caliper(#) specify the maximum distance for which two observations are potential neighbors dtolerance(#) set maximum distance between individuals considered equal osample(newvar) newvar identifies observations that violate the overlap assumption control(# | label) specify the level of tvar that is the control tlevel(# | label) specify the level of tvar that is the treatment generate(stub) generate variables containing the observation numbers of the nearest neighbors metric(metric) select distance metric for covariates coeflegend display legend instead of statistics
Max-min eigenvalue problems, primal-dual Interior point algorithms, and Trust region subproblemst

Two Primal-dual interior point algorithms are presented for the problem of maximizing the smallest eigenvalue of a symmetric matrix over diagonal perturbations. These algorithms prove to be simple, robust, and efficient. Both algorithms are based on transforming the problem to one with constraints over the cone of positive semidefinite matrices, i.e. Lowner order constraints. One of the algorithms does this transformation through an intermediate transformation to a trust region subproblem. This allows the removal of a dense row
Research on Node Trusted Degree Based Security Routing Technology

In response to the urgent needs of current users of network security services, we propose a security routing technology which is based on node trusted degree. By collecting and integrating the evidence of identity and behavior, such as the security, reliability and availability, we build the node trusted measurement model. Based on this model, the safe transmission and diffusion of information is guaranteed by the OSPF routing protocol and CPK security authentication mechanism, and we can generate the security optimal path under the node's trusted degree and bandwidth constraints. Simulation results show that the improved security routing protocol has better effectiveness and dynamic adaptive capacity when the network is attacked.
An Ant Odor Analysis Approach to the Ant Colony Optimization Algorithm for Data-Aggregation in Wireless Sensor Networks

In this paper we present an ant odor analysis approach to the ant colony optimization (ACO) algorithm for data aggregation in wireless sensor networks. We provide a modification to the existing ACO data aggregation scheme by accounting for multiple sinks and sources in an energy-constrained wireless sensor network. By forming families of ants (FoA), an intrinsic way is provided to explore the search space for routing and for determining optimal data aggregation. An ant odor identification model (AOIM) serves to determine and increase the correlation between data at the various nodes by trying to include hitherto different data under the same family (FoA). Simulation results show that the energy-efficiency achieved is high with less associated costs even for a larger number of sources and sinks
Instance Based Sparse Classifier Fusion for Speaker Verification

This paper focuses on the problem of ensemble classification for text-independent speaker verification. Ensemble classification is an efficient method to improve the performance of the classification system. This method gains the advantage of a set of expert classifiers. A speaker verification system gets an input utterance and an identity claim, then verifies the claim in terms of a matching score. This score determines the resemblance of the input utterance and preenrolled target speakers. Since there is a variety of information in a speech signal, state-of-the-art speaker verification systems use a set of complementary classifiers to provide a reliable decision about the verification. Such a system receives some scores as input and takes a binary decision: accept or reject the claimed identity. Most of the recent studies on the classifier fusion for speaker verification used a weighted linear combination of the base classifiers. The corresponding weights are estimated using logistic regression. Additional researches have been performed on ensemble classification by adding different regularization terms to the logistic regression formulae. However, there are missing points in this type of ensemble classification, which are the correlation of the base classifiers and the superiority of some base classifiers for each test instance. We address both problems, by an instance based classifier ensemble selection and weight determination method. Our extensive studies on NIST 2004 speaker recognition evaluation (SRE) corpus in terms of EER, minDCF and minCLLR show the effectiveness of the proposed method.