Miracle-dz/UniFAR-FTUs huggingface dataset:
TRENDS IN POLITICAL TOLERANCE

Some researchers find a substantial increase in political tolerance since the 1950s, while others find the increase to be "illusory" -the public is more tolerant now of leftists, but has simply found other targets on which to vent its intolerance. Reanalysis and the addition of more extensive trend data from 1940 to 1985 suggest that the shift does seem primarily to reflect increased tolerance of leftists, but that the public has not found other groups to be intolerant of. Measured tolerance has fluctuated greatly over the period, reflecting mainly changes in perceptions of threat from putatively subversive groups, especially domestic Communists. Also, the public's grasp of, and selfinterested concern about, civil liberties seems so minimal that one might argue not that the public is substantially tolerant or intolerant, but that it has no really tangibly measurable "attitude" on the subject one way or the other. Several recent studies have dealt with the American public's tolerance for dissent and nonconformity. Part of the discussion has revolved around the issue of changes in the level of tolerance-specifically, whether there has been an increase it; this quality since the 1950s. The benchmark for these studies has been Samuel A. Stouffer's classic study of civil liberties, published in 1955. Stouffer's survey posed an array of questions about Communists, Socialists, and atheists and sought to assess the public's willingness to allow such people to speak and to hold various kinds of jobs. Twenty years later, comparative data became available. In 1973, Stouffer's 1954 survey was substantially replicated, and, beginning in 1972, several of his central questions have been included on the General Social Survey of the National OpinJOHN MUELLER iS Professor of Political Science at the University of Rochester. The author would like to thank J. Allen Williams, Tom Smith, and Howard Schuman for supplying some of the data used in this paper, Karl Mueller for research assistance, and George E. Marcus and John Zaller for comments on an earlier draft. Public Opinion Quarterly Volume 52:1-25 ? 1988 by the American Association for Public Opinion Research Published by The University of Chicago Press / 0033-362X/88/0052-01/$2.50 This content downloaded from 157.55. 39.27 on Wed, 07 Sep 2016 05:15:06 UTC All use subject to http://about.jstor. org/terms
ENERGY DRINK CONSUMPTION (WITH AND WITHOUT ALCOHOL) AND ITS RELATIONSHIP TO RISKY BEHAVIOR, RISK AWARENESS, AND BEHAVIORAL INTENTION IN COLLEGE STUDENTS

OF THESIS ENERGY DRINK CONSUMPTION (WITH AND WITHOUT ALCOHOL) AND ITS RELATIONSHIP TO RISKY BEHAVIOR, RISK AWARENESS, AND BEHAVIORAL INTENTION IN COLLEGE STUDENTS The purpose of this study was to assess the relationships between energy drink consumption (with and without alcohol) and other risky behaviors; students’ overall awareness level of the risks for consuming energy drinks; and overall behavioral intention to consume energy drinks among college students at the University of Kentucky. A total of 277 students enrolled during the Spring 2012 semester responded to the online survey, 46.5% (n=129) of which were considered energy drink users. Students classified as energy drink users participated in other risky behaviors more often than nonusers (p=0.001). In addition, energy drink users exhibited a higher awareness level for the risks associated with energy drink consumption both with (p=0.000) and without (p=0.000) alcohol. Those who were more aware of the risks associated with energy drink consumption, both with (p=0.001) and without (p=0.001) alcohol, were found to have increased intention to consume energy drinks within the next month compared to those with lower awareness levels. There is a need for future research on this topic, as well as a need for expanded education and intervention programming for college students regarding the risks of energy drink consumption (with and without alcohol).
Unraveling the nature of autism: finding order amid change

In this article, we hypothesize that individuals with autism spectrum disorder (ASD) are born with a deficit in invariance detection, which is a learning process whereby people and animals come to attend the relatively stable patterns or structural regularities in the changing stimulus array. This paper synthesizes a substantial body of research which suggests that a deficit in the domain-general perceptual learning process of invariant detection in ASD can lead to a cascade of consequences in different developmental domains. We will outline how this deficit in invariant detection can cause uncertainty, unpredictability, and a lack of control for individuals with ASD and how varying degrees of impairments in this learning process can account for the heterogeneity of the ASD phenotype. We also describe how differences in neural plasticity in ASD underlie the impairments in perceptual learning. The present account offers an alternative to prior theories and contributes to the challenge of understanding the developmental trajectories that result in the variety of autistic behaviors.
Insight into the Strong Antioxidant Activity of Deinoxanthin, a Unique Carotenoid in Deinococcus Radiodurans

Deinoxanthin (DX) is a unique carotenoid synthesized by Deinococcus radiodurans, one of the most radioresistant organisms known. In comparison with other carotenoids, DX was proven to exhibit significantly stronger reactive oxygen species (ROS)-scavenging activity, which plays an important role in the radioresistance of D. radiodurans. In this work, to gain deeper insights into the strong antioxidant activity of DX, the parameters characterizing ROS-scavenging potential were calculated by means of quantum chemical calculations. It was found that DX possesses lower lowest triplet excitation energy for its unique structure than other carotenoids, such as β-carotene and zeaxanthin, which endows DX strong potential in the energy transfer-based ROS-scavenging process. Moreover, the H-atom donating potential of DX is similar to zeaxanthin according to the theoretical homolytic O-H bond dissociation enthalpy. Thus, the large number of conjugated double bonds should be crucial for its strong antioxidant activity.
Semantic sense extraction from Wikipedia pages

This paper discusses a modality to access and to organize unstructured contents related to a particular topic coming from the access to Wikipedia pages. The proposed approach is focused on the acquisition of new knowledge from Wikipedia pages and is based on the definition of useful patterns able to extract and identify novel concepts and relations to be added in the knowledge base. We proposes a method that uses information from the wiki page's structure. According to the different part of the page we define different strategies to obtain new concepts or relation between them. We analyze not only structure but text directly to obtain relations and concepts and to extract the type of relations to be incorporated in a domain ontology. The purpose is to use the obtained information in an intelligent tutoring system to improve his capabilities in dialogue management with users.
Cross-correlating WMAP5 with 1.5 million LRGs: a new test for the ISW effect

We present the cross-correlation of the density map of luminous red galaxies (LRGs) and the temperature fluctuation in the cosmic microwave background (CMB) as measured by the fiveyear Wilkinson Microwave Anisotropy Probe observations. The LRG samples were extracted from imaging data of the Sloan Digital Sky Survey (SDSS) Data Release 5 based on two previous spectroscopic redshift surveys, the SDSS LRG and the 2dF–SDSS LRG and QSO (2SLAQ) surveys designed to have average redshifts of z ≈ 0.35 and z ≈ 0.55. In addition, we have added a higher redshift photometric LRG sample based on the selection of the AAOmega LRG redshift survey at z ≈ 0.7. The total LRG sample thus comprises 1.5 million galaxies, sampling a redshift range of 0.2 <z< 0.9 over ≈7600 deg 2 of the sky, probing a total cosmic volume of ≈5.5 h −3 Gpc 3 . First, we find that the new LRG sample at z ≈ 0.7 shows very little positive evidence for the Integrated Sachs–Wolfe (ISW) effect. Indeed, the cross-correlation is negative out to ≈1 ◦ .
Urea Output by L3 Teladorsagia circumcincta and some Properties of Two Urea Producing Enzymes

Background Like several other parasites, Teladorsagia circumcincta secretes or excretes urea, but neither the rate of efflux nor the possible metabolic sources of the urea has been considered. Methods Parasites were maintained by passage through sheep. Urea efflux was measured using phenol/hypochlorite after treatment with urea aminohydrolase. The kinetics of creatine amidinohydrolase and arginine amidinohydrolase were characterised by coupling the reactions with urea aminohydrolase and glutamate dehydrogenase. Results Infective L3 T. circumcincta secreted or excreted urea at 25% of the rate of NH3/NH4 +. The rate of urea efflux was about 84 pmol h−1 (103 larvae)−1 over 4 hours, corresponding to about 11 nmol h−1 mg−1 protein. We could not detect urea aminohydrolase activity, but urea production by both creatine amidinohydrolase and arginine amidinohydrolase could be detected. The apparent K m and V max of creatine amidinohydrolase were 1.1 mM and 48 nmol h−1 mg−1 protein, respectively, and the activity was greatest at pH 8. The apparent K m and V max of arginine amidinohydrolase were 0.7 mM and 62 nmol h−1 mg−1 protein, respectively, and the activity was greatest at pH 7.9. Conclusion The activity of creatine amidinohydrolase and arginine amidinohydrolase was sufficient to account for the rate of urea secretion or excretion.
Coping strategies and perceived effectiveness in fourth through eighth grade victims of bullying

Victimization resulting from bullying affects millions of school children worldwide each year (e.g. Nansel et al., 2001; Sapouna, 2008; Smokowski & Kopasz, 2005). These children face the fear and humiliation of verbal, physical, and relational aggression and as a result, often suffer psychological ill effects (e.g. Kochenderfer-Ladd, & Skinner, 2002). This study examined the coping strategies of victims of bullying using qualitative research methodology. One-hundred-and-two fourth through eighth grade students participated in group interviews discussing the topics of bullying and coping with victimization. A coping model emerged from this study that included the primary categories of problem-focused coping and emotion-focused coping, and eight subcategories, self-defense, stand up to the bully, seeking social support, distancing, internalizing, tension-reduction/externalizing, focus on the positive, and self-blame. Results revealed that problem-focused coping was the type of coping most often used by victims of bullying. Externalizing and seeking social support were the most commonly reported problem-focused coping strategies used by victims. Boys discussed using externalizing strategies with greater frequency than girls, whereas girls reported seeking social support more often than boys. Results also indicated that children generally found their implemented strategies to be ineffective in resolving their problem. Implications and suggestions for future research are discussed.
Object recognition by eliminating distracting information

Object appearance is controlled by a multitude of factors. Some of these factors (such as illumination) are irrelevant to object identity and hamper recognition. In this paper, we propose an approach to object recognition that is based on learning to eliminate these irrelevant, distracting factors. This approach is complementary to most previous schemes, which attempt to learn the relevant factors. The proposed approach has two main advantages. First, we show that major distracting factors can be learned with very limited training data. As a result, the proposed scheme requires much less training data than the previous schemes. Second, we show that the problem of estimating distracting factors can be formulated in a general learning framework. As a result, arbitrary distracting factors can be learned systematically and automatically from training data. This single framework can successfully learn factors as diverse as illumination (for faces) and font style (for characters).
Extracting Multiword Terms from Document Collections

Multiword terms (MWTs) are relevant strings of words in text collections. Once they are automatically extracted, they may be used by an Information Retrieval system, suggesting its users possible conceptual interesting refinements of their information needs. As a matter of fact, these multiword terms point to relevant information, often corresponding to topics and subtopics in the text collection, and maybe quite useful specially for highly refining generic queries. In this paper, we introduce the LocalMaxs algorithm, for automatically extracting multiword terms. This algorithm requires neither empirically suggested thresholds nor complex linguistic filters nor language specific morpho-syntactic rules. These features make this algorithm a suitable approach to extract MWTs from text collections written in any language. Moreover, by introducing the Fair Dispersion Point Normalization concept, we can deal with arbitrarily long MWTs and can compare the results obtained by using different word association measures for MWTs selection. We also introduce our own association measure, the SCP, to work with the LocalMaxs algorithm, and assess the results obtained by comparing it with related statistics-based measures (Specific Mutual Information, Dice, Loglike and  coefficients) used in experiments on a text collection. An Information Retrieval application using our approach is also presented.
Multi-source Hierarchical Prediction Consolidation

In big data applications such as healthcare data mining, due to privacy concerns, it is necessary to collect predictions from multiple information sources for the same instance, with raw features being discarded or withheld when aggregating multiple predictions. Besides, crowd-sourced labels need to be aggregated to estimate the ground truth of the data. Due to the imperfection caused by predictive models or human crowdsourcing workers, noisy and conflicting information is ubiquitous and inevitable. Although state-of-the-art aggregation methods have been proposed to handle label spaces with flat structures, as the label space is becoming more and more complicated, aggregation under a label hierarchical structure becomes necessary but has been largely ignored. These label hierarchies can be quite informative as they are usually created by domain experts to make sense of highly complex label correlations such as protein functionality interactions or disease relationships. We propose a novel multi-source hierarchical prediction consolidation method to effectively exploits the complicated hierarchical label structures to resolve the noisy and conflicting information that inherently originates from multiple imperfect sources. We formulate the problem as an optimization problem with a closed-form solution. The consolidation result is inferred in a totally unsupervised, iterative fashion. Experimental results on both synthetic and real-world data sets show the effectiveness of the proposed method over existing alternatives.
The Role of Collective Efficacy in Adventure Racing Teams

This study examines the relationship between collective efficacy and performance in a single competition of adventure racing. Adventure racing is a team-based sport that requires the multidisciplinary tasks of trekking, mountain biking, canoeing, and climbing to navigate through a preplanned racecourse. Seventeen teams competing in an adventure race completed measures of prior performance, preparation effort, and a collective efficacy assessing perceptions of their team's functioning in six performance areas. Three in-race measures of collective efficacy and environmental factors-conditions are taken at various checkpoints. A correlational analysis indicates a positive relationship between preparation effort and initial perceptions of collective efficacy. A repeated measures analysis reveals the dynamic nature of collective efficacy and the reciprocal relationship between efficacy and performance. The results are consistent with D. L. Feltz and C. D. Lirgg's (1998) examination of collegiate teams and A. Bandura's (2000) contention that collective efficacy fosters a sense of motivational investment and an increased sense of staying power.
Quasi Current Mode Control for the Phase-Shifted Series Resonant Converter

A novel indirect current mode control is applied in the phase-shifted series resonant converter system. The current is generated from resonant tank vector and the resonant current is regulated indirectly through quasi current mode control and thus the dynamic performance of the converter system is improved. Only single voltage feedback is required for the system. The proposed system consists of two control loops with one inner resonant vector and one outer voltage loop. Analysis and practical experiments are carried out and the results show the better performance compared with that of the conventional control.
Defaults and Donation Decisions

The well-documented shortage of donated organs suggests that greater effort should be made to increase the number of individuals who decide to become potential donors. We examine the role of one factor: the no-action default for agreement. We first argue that such decisions are constructed in response to the question, and therefore influenced by the form of the question. We then describe research that shows that presumed consent increases agreement to be a donor, and compare countries with opt-in (explicit consent) and opt-out (presumed consent) defaults. Our analysis shows that opt-in countries have much higher rates of apparent agreement with donation, and a statistically significant higher rate of donations, even with appropriate statistical controls. We close by discussing the costs and benefits associated with both defaults as well as mandated choice.
Science Communication in Teacher Personal Pronouns

In this study, I explore how personal pronouns used by elementary teachers during science inquiry discussions communicate science and frame teacher–student–science relations. A semiotic framework is adopted wherein teacher pronominal choices are viewed as symbolically expressing cognitive meanings (scientific thinking, forms of expression, and concepts) and indexically communicating social meanings (hidden messages about social and personal aspects of science—human agency, science membership, and gender). Through the construction of interactional maps and micro‐ethnographic analysis of classroom video‐recordings, I focus specifically on participant examples (oral descriptions of actual or hypothetical situations wherein the teacher presents herself and/or her students as characters to illustrate topics under discussion). This analysis revealed that the teacher use of the generalised you communicated to the students how to mean scientifically (i.e. to speak like a scientist), while I communicated scientific ways of thinking and reasoning. Furthermore, teacher pronouns communicated the social nature of science (NOS) (e.g. science as a human enterprise) as well as multiple teacher–student–science relational frames that were inclusive of some students (mainly boys) but excluded girls (i.e. positioned them as science outsiders). Exclusive use of he was taken as indicative of a gender bias. It is argued that science teachers should become more aware of the range of personal pronouns available for science instruction, their advantages and constraints for science discussions, their potential as instructional tools for humanising and personalising impersonal science curricula as well as the risk of ‘NOS’ miscommunication.
Sequential Monte Carlo without likelihoods

Recent new methods in Bayesian simulation have provided ways of evaluating posterior distributions in the presence of analytically or computationally intractable likelihood functions. Despite representing a substantial methodological advance, existing methods based on rejection sampling or Markov chain Monte Carlo can be highly inefficient and accordingly require far more iterations than may be practical to implement. Here we propose a sequential Monte Carlo sampler that convincingly overcomes these inefficiencies. We demonstrate its implementation through an epidemiological study of the transmission rate of tuberculosis.
Between Online Heaven and Cyberhell

The diffusion of the internet in Germany was accompanied by strong attention from the traditional mass media who considered the new medium extensively in their coverage. Their media framing of the internet is analyzed following the four dimensions of framing identified in earlier research on media issues. Central to this process are the argumentation patterns used in this research which were defined with regard to a qualitative analysis of the multimedia discourse. Results of a quantitative content analysis of German news magazine coverage (1995-8) indicate that these media had a strong tendency towards a favorable assessment of the internet. The euphoric and economically optimistic argumentation patterns were most important, and outcomes of the development were evaluated as supporting the emancipation of the individual. However, this particular way of framing the internet issue is only partly reflected in the perceptions of users.
A Simple and Strongly-Local Flow-Based Method for Cut Improvement

Many graph-based learning problems can be cast as finding a good set of vertices nearby a seed set, and a powerful methodology for these problems is based on maximum flows. We introduce and analyze a new method for locally-biased graph-based learning called SimpleLocal, which finds good conductance cuts near a set of seed vertices. An important feature of our algorithm is that it is strongly-local, meaning it does not need to explore the entire graph to find cuts that are locally optimal. This method solves the same objective as existing strongly-local flow-based methods, but it enables a simple implementation. We also show how it achieves localization through an implicit L1-norm penalty term. As a flow-based method, our algorithm exhibits several ad- vantages in terms of cut optimality and accurate identification of target regions in a graph. We demonstrate the power of SimpleLocal by solving problems on a 467 million edge graph based on an MRI scan.
A healthy start: promoting mental health and well-being in the early primary school years

Mental health problems in children represent a significant international health concern, with up to one in five children using mental health services during the course of any given year. Identifying the processes of what prevents social, emotional and behaviour difficulties (SEBD) and promotes healthy development from an early age can make a significant contribution to the promotion of positive mental health in children. This article describes a longitudinal study which sought to identify the risk and promotive factors as young children move from the early to junior years in primary school. Multilevel analysis was used to identify the individual, classroom, school, home and community factors that predict change in SEBD and in prosocial behaviour in the early school years. It also calculated the cumulative effect of the various risk and promotive factors on the pupils’ well-being and mental health. The article presents the windows of vulnerability and opportunity for young children’s healthy development, proposing a trajectory for healthy development in early and middle childhood.