Miracle-dz/UniFAR-FTUs huggingface dataset:
Puzzling with potential: Dynamic testing of analogical reasoning in children

Assessment procedures are frequent in children's school careers; however, measuring potential for learning has remained a puzzle. Dynamic testing is a method to assess cognitive potential that includes training in the assessment process. The goal of this thesis project was to develop a new dynamic test of analogical reasoning for school children. The main aims were to (1) investigate factors that influence children’s differences in performance and change during dynamic testing and (2) examine the predictive value of dynamic measures on children’s school performance. Children showed great variation in cognitive potential. Higher ability children generally required less training and showed greater transfer to other problem sets. Yet, lower ability children tended to improve more during dynamic testing. Performance change during testing appears to be a unique predictor of math and reading achievement, but was unrelated to working memory or cultural background, providing evidence that this may be a separate construct important in the assessment of cognitive potential – especially in culturally diverse schools. This performance change measure, often criticized within classical test theory, has demonstrated its worth when estimated using item response theory models and will hopefully find its place again among the valuable measurement outcomes of children’s potential for learning.
A Closed-Form GARCH Option Pricing Model

This paper develops a closed-form option pricing formula for a spot asset whose variance follows a GARCH process. The model allows for correlation between returns of the spot asset and variance and also admits multiple lags in the dynamics of the GARCH process. The single-factor (one-lag) version of this model contains Heston’s (1993) stochastic volatility model as a diffusion limit and therefore unifies the discrete-time GARCH and continuous-time stochastic volatility literature of option pricing. The new model provides the first readily computed option formula for a random volatility model in which current volatility is easily estimated from historical asset prices observed at discrete intervals. Empirical analysis on S&P 500 index options shows the single-factor version of the GARCH model to be a substantial improvement over the Black-Scholes (1973) model. The GARCH model continues to substantially outperform the Black-Scholes model even when the BlackScholes model is updated every period and uses implied volatilities from option prices, while the parameters of the GARCH model are held constant and volatility is filtered from the history of asset prices. The improvement is due largely to the ability of the GARCH model to describe the correlation of volatility with spot returns. This allows the GARCH model to capture strike-price biases in the Black-Scholes model that give rise to the skew in implied volatilities in the index options market. JEL classification: G13
Hybrid breadth-first search on a single-chip FPGA-CPU heterogeneous platform

Large and sparse small-world graphs are ubiquitous across many scientific domains from bioinformatics to computer science. As these graphs grow in scale, traversal algorithms such as breadth-first search (BFS), fundamental to many graph processing applications and metrics, become more costly to compute. The cause is attributed to poor temporal and spatial locality due to the inherently irregular memory access patterns of these algorithms. A large body of research has targeted accelerating and parallelizing BFS on a variety of computing platforms, including hybrid CPU-GPU approaches for exploiting the small-world property. In the same spirit, we show how a single-die FPGA-CPU heterogeneous device can be used to leverage the varying degree of parallelism in small-world graphs. Additionally, we demonstrate how dense rather than sparse treatment of the BFS frontier vector yields simpler memory access patterns for BFS, trading redundant computation for DRAM bandwidth utilization and faster graph exploration. On a range of synthetic small-world graphs, our hybrid approach performs 7.8× better than software-only and 2× better than accelerator-only implementations. We achieve an average traversal speed of 172 MTEPS (millions of traversed edges per second) on the ZedBoard platform, which is more than twice as effective as the best previously published FPGA BFS implementation in terms of traversals per bandwidth.
The Dark Side of Engagement for Older Workers

Late-career workers must remain employed for longer, but in doing so face the unique challenge of remaining active and productive at work while not disregarding the need to maintain health and plan for retirement, both of which are necessary for work longevity and successful aging. This study investigated whether work engagement, a motivator of proactive behavior in the work domain, would exhibit a dark side by acting as a de-motivator of proactive behavior in the health and retirement domains. Results from 1917 participants showed that one dimension of engagement, vigor, was positively associated with proactivity across domains. In contrast, absorption, although positively related to proactive behavior at work, was associated with fewer health behaviors and was unrelated to retirement planning.
Multisector intervention to accelerate reductions in child stunting: an observational study from 9 sub-Saharan African countries.

BACKGROUND In sub-Saharan Africa, ~ 40% of children <5 y old are stunted, with levels that have remained largely unchanged over the past 2 decades. Although the complex determinants of undernutrition are well recognized, few studies have evaluated strategies that combine nutrition-specific, health-based approaches with food system- and livelihood-based interventions. OBJECTIVE We examined changes in childhood stunting and its determinants after 3 y of exposure to an integrated, multisector intervention and compared these changes with national trends. DESIGN A prospective observational trial was conducted across rural sites in 9 sub-Saharan African countries with baseline levels of childhood stunting >20%. A stratified random sample of households and resident children <2 y old from villages exposed to the program were enrolled in the study. Main outcome measures included principal determinants of undernutrition and childhood stunting, which was defined as a height-for-age z score less than -2. National trends in stunting were generated from demographic and health surveys. RESULTS Three years after the start of the program in 2005-2006, consistent improvements were observed in household food security and diet diversity, whereas coverage with child care and disease-control interventions improved for most outcomes. The prevalence of stunting in children <2 y old at year 3 of the program (2008-2009) was 43% lower (adjusted OR: 0.57; 95% CI: 0.38, 0.83) than at baseline. The average national stunting prevalence for the countries included in the study had remained largely unchanged over the past 2 decades. CONCLUSION These findings provide encouraging evidence that a package of multisector interventions has the potential to produce reductions in childhood stunting.
Fast Image Deblurring Algorithm Based on Normalized Sparsity Measure and Space-Frenquency Transformation

Blind restoration of blurry image is a challenging and significant problem. In this paper, we propose a deblurring algorithm which restores the latent image from a single blurry image. The method consists of two parts, kernel estimation and image restoration. To estimate the blur kernel, a cost function is constructed using a regularization term based on normalized sparsity measure and a fast optimization algorithm is employed to achieve the optimal solution based on space-frequency transformation. For image restoration, we construct the cost function through seeking the MAP estimation based on natural image gradient distribution, and solve it with a similar fast optimization algorithm. The experiment results with real natural images manifest that our method is able to obtain higher quality restored images with higher proceeding speed than other methods from current literatures.
Student reactions to teacher feedback in two Hong Kong secondary classrooms

This study investigates the reactions of students in two Hong Kong secondary classrooms to their teachers’ feedback, focusing particularly on the factors that might have influenced their reactions. Student data from questionnaires, checklists and protocols were triangulated with teacher data from interviews, classroom observations and feedback analysis to situate student reactions in their specific contexts. The results show that students, irrespective of proficiency level, wanted more written comments from teachers. The students of lower proficiency were less interested in error feedback than those of higher proficiency, though both groups preferred more explicit error feedback from teachers. Students did not understand all of the teacher feedback, which could be due to its illegibility, apart from other plausible factors not explored in the study. The results suggest that the teachers’ feedback, which was mostly teacher-centred, made students passive and dependent on teachers. The paper concludes that it is important for teachers to be aware of the impact of their feedback practices on student expectations and attitudes, which should be fed back to teachers to help them develop reflective and effective feedback practices. # 2007 Elsevier Inc. All rights reserved.
Semi-metric spaces and fixed points of α−φ -contractive maps

A negative answer to an open problem is provided. Fixed point results for α-φ-contractive mappings in semi-metric spaces are proved. To show the generality of our results, examples are given. Finally, an application of our result to probabilistic spaces is derived. c ©2016 All rights reserved.
Specification Testing in Nonlinear Time Series with Nonstationarity

This paper considers a class of nonparametric autoregressive processes and then a class of nonparametric time series regression models with a nonstationary regressor. For the autoregression case, we propose a nonparametric unit–root test for the conditional mean. For the nonparametric time series regression case, we construct a nonparametric test for testing whether the regression is of a known parametric form indexed by a vector of unknown parameters. We establish asymptotic distributions of the proposed test statistics. Both the setting and the results differ from earlier work on nonparametric time series regression with stationarity. In addition, we develop a bootstrap simulation scheme for the selection of suitable bandwidth parameters involved in the kernel tests as well as the choice of simulated critical values. An example of implementation is given to show that the proposed tests work in practice.
Sum-Product Networks for Structured Prediction: Context-Specific Deep Conditional Random Fields

Linear-chain conditional random fields (LCCRFs) have been successfully applied in many structured prediction tasks. Many previous extensions, e.g. replacing local factors by neural networks, are computationally demanding. In this paper, we extend conventional LC-CRFs by replacing the local factors with sum-product networks, i.e. a promising new deep architecture allowing for exact and efficient inference. The proposed local factors can be interpreted as an extension of Gaussian mixture models (GMMs). Thus, we provide a powerful alternative to LC-CRFs extended by GMMs. In extensive experiments, we achieved performance competitive to state-ofthe-art methods in phone classification and optical character recognition tasks.
Personality and Cognitive Decline in Older Adults: Data From a Longitudinal Sample and Meta-Analysis.

OBJECTIVES Personality traits are associated with risk of dementia; less is known about their association with the trajectory of cognitive functioning. This research examines the association between the 5 major dimensions of personality and cognitive function and decline in older adulthood and includes a meta-analysis of published studies. METHOD Personality traits, objective and subjective memory, and cognitive status were collected in a large national sample (N = 13,987) with a 4-year follow-up period. For each trait, the meta-analysis pooled results from up to 5 prospective studies to examine personality and change in global cognition. RESULTS Higher Neuroticism was associated with worse performance on all cognitive measures and greater decline in memory, whereas higher Conscientiousness and Openness were associated with better memory performance concurrently and less decline over time. All traits were associated with subjective memory. Higher Conscientiousness and lower Extraversion were associated with better cognitive status and less decline. Although modest, these associations were generally larger than that of hypertension, diabetes, history of psychological treatment, obesity, smoking, and physical inactivity. The meta-analysis supported the association between Neuroticism and Conscientiousness and cognitive decline. DISCUSSION Personality is associated with cognitive decline in older adults, with effects comparable to established clinical and lifestyle risk factors.
An MPC Approach to Dual Control

Abstract We present a model predictive control (MPC) approach to solve the dual adaptive control problem. The cost function minimized by the controller rewards probing the system for information when the parameter estimates are poor. The control algorithm is designed to handle poorly identified models and excites the system so that information can be gathered to achieve the optimal trade-off between process control and identification. This excitation is achieved without requiring the input to be persistently exciting; rather, the probing objective is based on an exact formulation of the expected value of the output error at the first time stage. The resulting expression is also used for the second time stage; this ensures that a proper trade-off between excitation and output regulation is maintained. The algorithm can be viewed as the merging of adaptive control with MPC and its design can easily be implemented with modifications to an existing MPC. As an example we consider a first-order linear process system with two unknown parameters. Our proposed algorithm probes the system even when the output error is small and quickly gathers enough information to correctly identify the unknown plant parameters.
A Calcium-Regulated MEF2 Sumoylation Switch Controls Postsynaptic Differentiation

Postsynaptic differentiation of dendrites is an essential step in synapse formation. We report here a requirement for the transcription factor myocyte enhancer factor 2A (MEF2A) in the morphogenesis of postsynaptic granule neuron dendritic claws in the cerebellar cortex. A transcriptional repressor form of MEF2A that is sumoylated at lysine-403 promoted dendritic claw differentiation. Activity-dependent calcium signaling induced a calcineurin-mediated dephosphorylation of MEF2A at serine-408 and, thereby, promoted a switch from sumoylation to acetylation at lysine-403, which led to inhibition of dendritic claw differentiation. Our findings define a mechanism underlying postsynaptic differentiation that may modulate activity-dependent synapse development and plasticity in the brain.
The Effects of Aggregate Demand for Meeting Earnings Expectations on Managerial Behavior

We examine whether time-varying investor demand for meeting earnings expectations affects firms’ discretionary accounting choices. We find that the proportion of firms that meet or just beat earnings expectations is greater when the premium for meeting expectations is high in the preceding quarter. We also separate the premium into penalty and reward components and find that the effects of these components (or signals) on the proportion of firms that meet or beat expectations are more pronounced when the signals are high-precision signals. In addition, we find that managers respond more to an increase in the penalty for missing expectations than and increase in the reward for achieving expectations. Our main results are found at the aggregate level and these results are robust to various controls and are also confirmed by firm-specific analyses.
Limit of normalized quadrangulations: The Brownian map

Consider qn a random pointed quadrangulation chosen equally likely among the pointed quadrangulations with n faces. In this paper we show that, when n goes to +∞, qn suitably normalized converges weakly in a certain sense to a random limit object, which is continuous and compact, and that we name the Brownian map. The same result is shown for a model of rooted quadrangulations and for some models of rooted quadrangulations with random edge lengths. A metric space of rooted (resp. pointed) abstract maps that contains the model of discrete rooted (resp. pointed) quadrangulations and the model of the Brownian map is defined. The weak convergences hold in these metric spaces.
Distributed Balancing of Wind Power Forecast Deviations by Intraday Trading and Internal Ex-ante Self-Balancing -- A Modelling Approach

Wind power generation does on the one hand contribute to a less polluting and more sustainable electric power generation mix. On the other hand, its power output is variable and subject to forecast errors. In real-time, deviations from wind power forecast are handled by the system operator. But expected deviations can already me minimised by power generating companies before real-time. Ways to decrease their expected deviations are intraday trading and/or re-scheduling of own power plants. Both can be regarded as forms of self-balancing before the period of delivery (ex-ante), intraday trading as a form of external ex-ante self-balancing and re-scheduling as internal ex-ante self-balancing. Both can decrease the need for real-time balancing by the system operator. As existing intraday markets are often plagued by low liquidity, it is important to model such not-perfectly liquid intraday markets and simulate different trading and scheduling strategies. This paper presents an approach to model the choice between purely internal self-balancing and internal self-balancing combined with intraday trading on a not fully liquid intraday market. Results from the model runs indicate that intraday trading on a not-perfectly liquid market can be beneficial from the producers as well as from the system's perspective. However, it can result in increased costs if the possibilities to trade on the intraday market are very limited. This is important to consider when investigating the question whether it would be beneficial to distribute a larger share of the balancing responsibility among the power generating companies in order to relieve some pressure from the system operator.
Update on NHANES Dietary Data: Focus on Collection, Release, Analytical Considerations, and Uses to Inform Public Policy

NHANES is the cornerstone for national nutrition monitoring to inform nutrition and health policy. Nutritional assessment in NHANES is described with a focus on dietary data collection, analysis, and uses in nutrition monitoring. NHANES has been collecting thorough data on diet, nutritional status, and chronic disease in cross-sectional surveys with nationally representative samples since the early 1970s. Continuous data collection began in 1999 with public data release in 2-y cycles on ∼10,000 participants. In 2002, the Continuing Survey of Food Intakes by Individuals and the NHANES dietary component were merged, forming a consolidated dietary data collection known as What We Eat in America; since then, 24-h recalls have been collected on 2 d using the USDA’s Automated Multiple-Pass Method. Detailed and targeted food-frequency questionnaires have been collected in some NHANES cycles. Dietary supplement use data have been collected (in detail since 2007) so that total nutrient intakes can be described for the population. The continuous NHANES can adapt its content to address emerging public health needs and reflect federal priorities. Changes in data collection methods are made after expert input and validation/crossover studies. NHANES dietary data are used to describe intake of foods, nutrients, food groups, and dietary patterns by the US population and large sociodemographic groups to plan and evaluate nutrition programs and policies. Usual dietary intake distributions can be estimated after adjusting for day-to-day variation. NHANES remains open and flexible to incorporate improvements while maintaining data quality and providing timely data to track the nation’s nutrition and health status. In summary, NHANES collects dietary data in the context of its broad, multipurpose goals; the strengths and limitations of these data are also discussed in this review.
From Surface Dependencies towards Deeper Semantic Representations

In the past, a divide could be seen between ’deep’ parsers on the one hand, which construct a semantic representation out of their input, but usually have significant coverage problems, and more robust parsers on the other hand, which are usually based on a (statistical) model derived from a treebank and have larger coverage, but leave the problem of semantic interpretation to the user. More recently, approaches have emerged that combine the robustness of datadriven (statistical) models with more detailed linguistic interpretation such that the output could be used for deeper semantic analysis. Cahill et al. (2002) use a PCFG-based parsing model in combination with a set of principles and heuristics to derive functional (f-)structures of Lexical-Functional Grammar (LFG). They show that the derived functional structures have a better quality than those generated by a parser based on a state-of-the-art hand-crafted LFG grammar. Advocates of Dependency Grammar usually point out that dependencies already are a semantically meaningful representation (cf. Menzel, 2003). However, parsers based on dependency grammar normally create underspecified representations with respect to certain phenomena such as coordination, apposition and control structures. In these areas they are too ’shallow’ to be directly used for semantic interpretation. In this paper, we adopt a similar approach to Cahill et al. (2002) using a dependency-based analysis to derive functional structure, and demonstrate the feasibility of this approach using German data. A major focus of our discussion is on the treatment of coordination and other potentially underspecified structures of the dependency data input. F-structure is one of the two core levels of syntactic representation in LFG1