Miracle-dz/UniFAR-FTUs huggingface dataset:
A combined model of uncertainty management theory and the group engagement model of identity

ii ACKNOWLEDGEMENTS . iii TABLE OF CONTENTS . v LIST OF TABLES . viii LIST OF FIGURES . x CHAPTER 1: INTRODUCTION . 1 CHAPTER 2: LITERATURE REVIEW AND RESEARCH HYPOTHESES . 6 The History of the Fair Process Effect . 6 Distributive Justice . 6 Procedural Justice . 7 Interactional Justice . 9 Fair Process Effect Relationships . 11 Task Performance . 11 Organizational Citizenship Behavior . 11 Job Engagement . 13 The Fair Process Effect: The Group Engagement Model . 14 GEM and Justice . 18 GEM and Economic Evaluations . 19
Implementation and randomized controlled trial evaluation of universal postnatal nurse home visiting.

OBJECTIVES We evaluated whether a brief, universal, postnatal nurse home-visiting intervention can be implemented with high penetration and fidelity, prevent emergency health care services, and promote positive parenting by infant age 6 months. METHODS Durham Connects is a manualized 4- to 7-session program to assess family needs and connect parents with community resources to improve infant health and well-being. All 4777 resident births in Durham, North Carolina, between July 1, 2009, and December 31, 2010, were randomly assigned to intervention and control conditions. A random, representative subset of 549 families received blinded interviews for impact evaluation. RESULTS Of all families, 80% initiated participation; adherence was 84%. Hospital records indicated that Durham Connects infants had 59% fewer infant emergency medical care episodes than did control infants. Durham Connects mothers reported fewer infant emergency care episodes and more community connections, more positive parenting behaviors, participation in higher quality out-of-home child care, and lower rates of anxiety than control mothers. Blinded observers reported higher quality home environments for Durham Connects than for control families. CONCLUSIONS A brief universal home-visiting program implemented with high penetration and fidelity can lower costly emergency medical care and improve family outcomes.
Galaxy Zoo Green Peas: discovery of a class of compact extremely star-forming galaxies

We investigate a class of rapidly growing emission line galaxies, known as “Green Peas,” first noted by volunteers in the Galaxy Zoo project because of their peculiar bright green colour and small size, unresolved in SDSS imaging. Their appearance is due to very strong optical emission lines, namely [O III] �5007 ˚ A, with an unusually large equivalent width of up to �1000 ˚ A. We discuss a well-defined sample of 251 colour-selected ob jects, most of which are strongly star forming, although there are some AGN interlopers including 8 newly discovered Narrow Line Seyfert 1 galaxies. The star-forming Peas are low mass galaxies (M� 10 8.5 10 10 M⊙) with high star formation rates (� 10 M⊙yr −1 ), low metallicities (log[O/H] + 12 �8.7) and low reddening (E(B V) 6 0.25) and they reside in low density environments. They have some of the highest specific star for mation rates (up to � 10 −8 yr −1 ) seen in the local Universe, yielding doubling times for their stellar mass of hundreds of Myrs. The few star-forming Peas with HST imaging appear to have several clumps of bright star-forming regions and low surface density features that may indicate recent or ongoing mergers. The Peas are similar in size, mass, luminosity and metallicity to Luminous Blue Compact Galaxies. They are also similar to high redshift UV-luminous galaxies, e.g., Lymanbreak galaxies and Lyman-� emitters, and therefore provide a local laboratory with which to study the extreme star formation processes that occur in high-redshift galaxies. Studying starbursting galaxies as a function of redshift is essential to u nderstanding the build up of stellar mass in the Universe.
Anonymity as a Major Generator of Self-Disclosure in Online Groups

Among the variables explored in the field of group therapy, selfdisclosure has been found to be a major factor in enhancing the bonds of trust between group members, legitimizing group membership, and strengthening group identity (Kirschner, Dies, & Brown, 1978). Research has shown that the willingness to disclose information is significantly higher in computer-mediated communication than in face-to-face-settings, and anonymity plays a significant role in this process (Joinson, 2001). The role anonymity plays in online selfdisclosure was addressed by Suler (among others), who suggested that, “When people have the opportunity to separate their actions online from their in-person lifestyle and identity, they feel less vulnerable about self-disclosing and acting out” (Suler, 2004, p. 322). Suler noted that when people are not held accountable in the offline world International Journal of Group Psychotherapy, 67: 591–596, 2017 Copyright © The American Group Psychotherapy Association, Inc. ISSN: 0020-7284 print/1943-2836 online DOI: https://doi.org/10.1080/00207284.2017. 1314730
A dinâmica socioeconômica das comunidades rurais amazônicas: o caso da Comunidade de Nossa Senhora do Rosário - Parintins - AM.

O presente trabalho visa contribuir na construcao de parâmetros que sirvam de base para a introducao de tecnologias adequadas em comunidades rurais amazonicas, considerando a estrutura social e agraria, bem como a producao e a organizacao, com a finalidade de compreender melhor as realidades locais.
Agnostic Active Learning Without Constraints

We present and analyze an agnostic active learning algorithm that works without keeping a version space. This is unlike all previous approaches where a restricted set of candidate hypotheses is maintained throughout learning, and only hypotheses from this set are ever returned. By avoiding this version space approach, our algorithm sheds the computational burden and brittleness associated with maintaining version spaces, yet still allows for substantial improvements over supervised learning for classification.
Effects of Social Skills Training on Social Interactions of Children with Epilepsy

Background: Children with epilepsy have behavioral and social interactions problems more often than their peers. The purpose of this study was to investigate the eﬀect of social skills training on the social interaction of children with epilepsy. Methods: This was a 1 group quasi-experimental study with pretest and posttest design. Through convenience sampling, 40 chil-drenbetweentheagesof 6and12years(mean=11.8years,SD=1.8)withepilepsywereselectedfromtheIranianepilepsyassociation fromAugusttoDecember2014. Thesocialskillsratingsystemscalewasusedtocollectdata. Socialskillstrainingin4sessions’ were conducted for the group. Training consisted of 1 session per week. Paired-samples t-test was used for analyzing the pre- and post-results of intervention. The signiﬁcance level was considered less than 0.05. Results: The mean score of the social interaction of children with epilepsy was increased after social skills training intervention. This diﬀerence between before and after intervention was statistically signiﬁcant (P < 0.001). Therefore, social skills training had a positive inﬂuence on the social interaction of children with epilepsy. This positive change was observed in all subscale of social interactions such as cooperation, decisiveness, responsibility, and self-control. Conclusions: Theresultsshowedthatsocialskillstrainingduetoincreasesocialinteractioncouldimprovethesocialfunctionand maybe reduce high-risk behaviors in children with epilepsy.
How to Explain Individual Classification Decisions

After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data. Thus, we get an answer to the question what is the most likely label of a given unseen data point. However, most methods will provide no answer why the model predicted a particular label for a single instance and what features were most influential for that particular instance. The only method that is currently able to provide such explanations are decision trees. This paper proposes a procedure which (based on a set of assumptions) allows to explain the decisions of any classification method.
Speciational view of macroevolution: Are micro and macroevolution decoupled?

We introduce a simple computational model that, with microscopic dynamics driven by natural selection and mutation alone, allows the description of true speciation events. A statistical analysis of the evolutionary tree so generated captures realistic features showing power laws for frequency distributions in time and size. Finally, some possible interpretations of the absence of punctuated dynamics with mass extinctions are worked out.
Central limit theorem for the on-line nearest-neighbour graph

The on-line nearest-neighbour graph on a sequence of uniform random points in (0, 1)d (d ∈ N) joins each point after the first to its nearest neighbour amongst its predecessors. For the total power-weighted edge length of this graph, with weight exponent α ∈ (0, d/2), we prove a central limit theorem (in the large-sample limit), including an expression for the limiting variance. In contrast, we give a convergence result (with no scaling) for α > d/2. Both these results extend previous work. We also make some progress in the critical case α = d/2.
Finger-Knuckle-Print Region of Interest Segmentation Using Gradient Field Orientation & Coherence

Finger-knuckle-print is one of the emerging biometric traits. The region of interest is the area where the maximum information is centered, for a finger knuckle it is the area surrounding the knuckle region. A good system needs this region of interest as input for the feature vector extraction. In this method we present a novel approach for segmentation of Region of interest (ROI) of a finger-knuckle-print using gradient field orientation & its local field strength. This approach is fast and gives good results in case of shift in finger-knuckle-placement (translational shift).
The Role of Forecast Patterns in Conveying Analysts ’ Predictive Ability ∗

We investigate whether and how forecasting patterns convey information about an analyst’s predictive ability. Our model establishes an equilibrium strategy where the analyst issues a forecast only if the realized value of his private signal exceeds a threshold. In this equilibrium, higher-ability analysts choose higher thresholds than lower-ability analysts, and investors interpret all forecasts correctly. An immediate implication of this equilibrium is that, because higher-ability analysts choose higher thresholds, they issue fewer forecasts than lower-ability analysts. Using a large sample of analysts’ forecasts and controlling for other factors known to affect forecast frequency, we find empirical evidence consistent with this prediction. ∗We have benefited from helpful comments by an anonymous reviewer, Moshe Bareket, Robert Bushman, Jennifer Francis, John Graham, Thomas Hemmer, Michael Johannes, Chandra Kanodia, Ernst Maug, Michael Mikhail, Katherine Schipper, Michael Smith, Phillip Stocken, Mohan Venkatachalam, and the workshop participants at the College of William and Mary, Columbia, 2001 Duke/UNC Fall Camp, INSEAD, Humboldt University at Berlin, University of Minnesota, 2002 Eastern Finance Asssociation Annual Meeting, and 2002 American Accounting Association Annual Meeting. We receive financial support from the Fuqua School of Business at Duke University and Columbia Business School at Columbia University. All errors remain ours. †Corresponding author. The Fuqua School of Business, Duke University, Durham, NC 27708-0120, email address: qc2@duke.edu. ‡Columbia Business School, Finance and Economics Division, 3022 Broadway, New York, NY 10027-6902, email address: wj2006@columbia.edu.
Summary of Results from the Mars Phoenix Lander's Thermal Evolved Gas Analyzer

ANALYZER. B. Sutter, D.W. Ming, W. V. Boynton P B. Niles, J. Hoffman, H. V. Lauer, and D. C. Golden Jacobs/ESCG, Houston TX, 77058, brad.sutter-2@nasa.gov. NASA Johnson Space Center, Houston, TX 77058. University of Arizona, Tucson, AZ 85721 4 Department of Physics, University of Texas at Dallas, Richardson, TX 75083 ESCG/Barrios Tech., Houston, TX 77058. ESCG/Hamilton Sundstrand, Houston, TX 77058.
Intergroup conflict and barriers to common ground: A self‐affirmation perspective

Funding information National Science Foundation, Grant/Award Number: National Science Foundation RAPID Grant BCS‐1516476 Abstract Psychological barriers to conflict resolution stem, in part, from defensive responses to feelings of self‐threat. Self‐affirmation theory proposes that affirmations of global self‐worth—often achieved by writing or reflecting on core values—can broaden individuals' perspectives and potentially reduce biases in their intergroup judgments. In this paper, we review the extant literature on the use of self‐affirmation to potentially reduce intergroup biases in order to shed light on the role of self‐threat in perpetuating conflict. Self‐affirmation has been shown to impact 3 key aspects of intergroup conflict: (a) the strength with which conflict‐supporting beliefs are held, (b) the biased processing of conflict‐relevant information, and (c) the resistance to seeing common ground in negotiations. Discussion centers on the limits as well as the potential of self‐affirmation to promote openness and conflict resolution.
The FOOM Method – Modeling Software Product Lines in Industrial Settings

This paper presents FOOM – Feature-based Object Oriented Modeling and its application to a family of sonar systems. FOOM is a synthesis of the FODA (Feature Oriented Domain Analysis) and Horseshoe models. The Unified Software Development Process (USP) is used as a template for describing the architectural transformation. FOOM focuses on identifying user-driven features throughout a product line’s architecture, organizing the architectural assets to lend them to substantial re-use, and instantiating multiple products from a single architecture. The paper introduces the notion of a feature class and feature listcontracts between the product line and products. The application of FOOM to sonar system is done systematically starting with the identification of a suitable base product, then architecture extraction is done; domain analysis is then carried out in order to identify the current and future members of the product family, and finally, product line architecture is developed.
New Perspectives in the Exploration of Korsakoff’s Syndrome: The Usefulness of Neurophysiological Markers

This perspective aims at underlining the usefulness of event-related potentials (ERP) to better understand the brain correlates of Korsakoff’s syndrome (KS), a neuropsychiatric disease characterized by severe memory impairment and most frequently resulting as a neurological complication of alcohol-dependence (AD). While ERP have been broadly used in AD, it has up to now been very little applied in KS or in the comparison of KS and AD. Within the framework of dual-process models, an influential theory postulating that addictive states result from an imbalance between under-activated reflective system and over-activated automatic-affective one, this paper proposes: (1) a brief synthesis of the main results of ERP studies in AD and KS, and (2) new research avenues using ERP to identify the electrophysiological correlates of cognitive and emotional dysfunction in KS. These experimental perspectives aim at exploring the continuity hypothesis, which postulates a gradient of impairments from AD to KS. We conclude on the possibility of developing neuropsychological strategies with electrophysiological follow-up to ensure KS diagnosis and test the efficacy of patient’s neurocognitive rehabilitation.
Enhancing teaching excellence through team-based learning

Abstract Through the lens of implementing team-based learning (TBL), this paper unpacks elements of ‘excellence’ and investigates the influence of individual belief systems, the learning environment and institutional context in influencing perceptions of ‘excellence’. We analyse the adoption of a student-centred approach to teaching and explore the implications for innovative approaches to teaching in a TEF environment. An outline of our findings in relation to TBL is discussed, and a conceptual framework of perceptions of excellence is presented. In recognition of the dichotomy between novice and expert learners, approaches to enhancing teaching excellence are discussed. We conclude with a mechanism by which metric-driven evaluation methods are likely to hinder pedagogical innovations and moves to enhance teaching excellence.
Does Migration Make You Happy? The Influence of Migration on Subjective Well-Being

In the field of neoclassical economics, migrants are expected to move to improve their economic situation, but what are the effects of moving on the subjective well-being (SWB) of migrants? Using longitudinal data from the German Socio-Economic Panel Study (SOEP) (1990-2007), I investigate the influence of migration from Eastern to Western Germany on SWB. The hypotheses in this study are derived from neoclassical economics and from the psychology literature. Following the rational choice framework, I expect that migration improves SWB in the long term. Fixed-effects models distinguish between the effects of unobserved heterogeneity, such as varying personality traits, and migration on SWB. The results reveal that migration has a positive, longterm effect on SWB. In addition, the favorable labor market conditions in Western Germany account for the increasing SWB that is reported by male migrants but does not account for that reported by female migrants.
Testing and the Method of Sieves

This paper develops test statistics based on scores for the specification of regression in nonparametric and semiparametric contexts. We study how different types of test statistics focus power on different directions of departure from the null hypothesis. We consider index models as basic examples, and utilize sieves for nonparametric approximation. We examine various goodness-of-fit statistics, including Cramer-von Mises and Kolmogorov-Smirnov forms. For a "box-style" sieve approximation, we establish limiting distributions of these statistics. We develop a bootstrap resampling method for estimating critical values for the test statistics, and illustrate their performance with a Monte Carlo simulation.
Fast Differentially Private Matrix Factorization

Differentially private collaborative filtering is a challenging task, both in terms of accuracy and speed. We present a simple algorithm that is provably differentially private, while offering good performance, using a novel connection of differential privacy to Bayesian posterior sampling via Stochastic Gradient Langevin Dynamics. Due to its simplicity the algorithm lends itself to efficient implementation. By careful systems design and by exploiting the power law behavior of the data to maximize CPU cache bandwidth we are able to generate 1024 dimensional models at a rate of 8.5 million recommendations per second on a single PC.
Creating a model of conceptual change

This thesis investigates promoting student understanding of the Particulate Nature of Matter (PNM) through Inquiry Based Learning (IBL), visualization and modelling. A Practitioner Action Research methodology was employed and student performance at junior second level school was measured using diagnostic and summative testing. A module on this topic was prepared which included a student workbook and a teaching manual. Student and teacher reflections on the learning issues experienced were obtained through interviews and repertory grid analysis based on Kellyian Personal Construct Psychology (PCP) principles. Summative results show a better comprehension of PNM by the intervention group as opposed to their control group peers. Repertory grid analysis was used to highlight and rate aspects of students’ affective and cognitive learning experiences. Furthermore, in conjunction with PCP, student drawings provided as exam answers when they were asked to think critically within a novel problem scenario were investigated. This exploratory approach enabled the systematic metering of student comprehension of chemistry constructs and served to detect the learning gaps in their construct hierarchy. This work represents a considered approach towards the development of an understanding of student answers including alternative conceptions. It integrates PCP with research on explanations and representations of ideas. It attempts to create an assessment instrument that can be used to visualise and comprehend a student’s chemistry drawings in a more holistic way than a binary judgement of wrong or right. Furthermore, PCP is employed to extend the focus from the interpretation of student responses to include the consideration of key features of conceptual development. These can be related within a proposed model of learning.
Explore-exploit in top-N recommender systems via Gaussian processes

We address the challenge of ranking recommendation lists based on click feedback by efficiently encoding similarities among users and among items. The key challenges are threefold: (1) combinatorial number of lists; (2) sparse feedback and (3) context dependent recommendations. We propose the CGPRank algorithm, which exploits prior information specified in terms of a Gaussian process kernel function, which allows to share feedback in three ways: Between positions in a list, between items, and between contexts. Under our model, we provide strong performance guarantees and empirically evaluate our algorithm on data from two large scale recommendation tasks: Yahoo! news article recommendation, and Google books. In our experiments, CGPRank significantly outperforms state-of-the-art multi-armed bandit and learning-to-rank methods, with an 18% increase in clicks.
A Role for Uric Acid and the Nalp3 Inflammasome in Antiphospholipid Antibody-Induced IL-1β Production by Human First Trimester Trophoblast

Women with antiphospholipid syndrome (APS) are at risk of recurrent pregnancy loss and obstetrical disorders, such as preeclampsia and intrauterine growth restriction (IUGR). Antiphospholipid antibodies (aPL) directly target the placenta by binding beta2-glycoprotein I (β2GPI) expressed on the trophoblast. We recently demonstrated in human first trimester trophoblast cells that anti-β2GPI antibodies (Abs) induce the secretion of IL-1β in a Toll-like receptor 4 (TLR4)-dependent manner. IL-1β secretion requires processing of pro-IL-1β and this is mediated by the inflammasome, a complex of Nalp3, apoptosis-associated speck-like protein containing a CARD (ASC) and caspase-1. The objective of this study was to determine if aPL induce IL-1β production in trophoblast via the inflammasome. Using a human first trimester trophoblast cell line, we demonstrated that a mouse anti-β2GPI mAb and human polyclonal aPL-IgG induce IL-1β processing and secretion, which was partially blocked upon caspase-1 inhibition. Nalp3 and ASC knockdown also attenuated anti-β2GPI Ab-induced IL-1β secretion. Furthermore, aPL stimulated the production of uric acid in a TLR4-dependent manner; and inhibition of uric acid prevented aPL-induced IL-1β production by the trophoblast. These findings demonstrate that aPL, via TLR4 activation, induce a uric acid response in human trophoblast, which in turn activates the Nalp3/ASC inflammasome leading to IL-1β processing and secretion. This novel mechanism may account for the inflammation at the maternal-fetal interface, which causes placental dysfunction and increases the risk of adverse pregnancy outcome in patients with APS.