Vulnerability to poverty: An empirical comparison of alternative measures

The recent common feeling about a skyrocketing economic risk has drawn increasing attention to its role and consequences on individuals' welfare. In literature one of the concepts that aims to measure it is vulnerability to poverty, that is the probability, today, of being in poverty or to fall into deeper poverty in the future (The World Bank, 2011). This paper compares empirically the several measures of individual vulnerability proposed in the literature, in order to understand which is the best signal of poverty that can be used for policies purposes. To this aim the Receiver Operating Characteristic (ROC) curve, the Pearson and Spearman correlation coefficients are used as precision criteria. The results show that two groups of indexes can be identified, high- and low-performers, and, among the former, that proposed by Dutta et al. (2011) is the most precise.Poverty, Risk, Vulnerability, Receiver Operating Characteristic curve(ROC)

The Effective Use of Limited Information: Do Bid Maximums Reduce Procurement Cost in Asymmetric Auctions?

Conservation programs faced with limited budgets often use a competitive enrollment mechanism. Goals of enrollment might include minimizing program expenditures, encouraging broad participation, and inducing adoption of enhanced environmental practices. We use experimental methods to evaluate an auction mechanism that incorporates bid maximums and quality adjustments. We examine this mechanism’s performance characteristics when opportunity costs are heterogeneous across potential participants, and when costs are only approximately known by the purchaser. We find that overly stringent maximums can increase overall expenditures, and that when quality of offers is important, substantial increases in offer maximums can yield a better quality-adjusted result.

Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges

Background: Systems biology has embraced computational modeling in response to the quantitative nature and increasing scale of contemporary data sets. The onslaught of data is accelerating as molecular profiling technology evolves. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) is a community effort to catalyze discussion about the design, application, and assessment of systems biology models through annual reverse-engineering challenges. Methodology and Principal Findings: We describe our assessments of the four challenges associated with the third DREAM conference which came to be known as the DREAM3 challenges: signaling cascade identification, signaling response prediction, gene expression prediction, and the DREAM3 in silico network challenge. The challenges, based on anonymized data sets, tested participants in network inference and prediction of measurements. Forty teams submitted 413 predicted networks and measurement test sets. Overall, a handful of best-performer teams were identified, while a majority of teams made predictions that were equivalent to random. Counterintuitively, combining the predictions of multiple teams (including the weaker teams) can in some cases improve predictive power beyond that of any single method. Conclusions: DREAM provides valuable feedback to practitioners of systems biology modeling. Lessons learned from the predictions of the community provide much-needed context for interpreting claims of efficacy of algorithms described in the scientific literature

The Effective Use of Limited Information: Do Bid Maximums Reduce Procurement Cost in Asymmetric Auctions?

Conservation programs faced with limited budgets often use a competitive enrollment mechanism. Goals of enrollment might include minimizing program expenditures, encouraging broad participation, and inducing adoption of enhanced environmental practices. We use experimental methods to evaluate an auction mechanism that incorporates bid maximums and quality adjustments. We examine this mechanism’s performance characteristics when opportunity costs are heterogeneous across potential participants, and when costs are only approximately known by the purchaser. We find that overly stringent maximums can increase overall expenditures, and that when quality of offers is important, substantial increases in offer maximums can yield a better quality-adjusted result.conservation auctions, Conservation Reserve Program, CRP, bid caps, experimental economics

The Effective Use of Limited Information: Do Bid Maximums Reduce Procurement Cost in Asymmetric Auctions?

Conservation programs faced with limited budgets often use a competitive enrollment mechanism. Goals of enrollment might include minimizing program expenditures, encouraging broad participation, and inducing adoption of enhanced environmental practices. We use experimental methods to evaluate an auction mechanism that incorporates bid maximums and quality adjustments. We examine this mechanism’s performance characteristics when opportunity costs are heterogeneous across potential participants, and when costs are only approximately known by the purchaser. We find that overly stringent maximums can increase overall expenditures, and that when quality of offers is important, substantial increases in offer maximums can yield a better quality-adjusted result.conservation auctions, Conservation Reserve Program, CRP, bid caps, experimental economics, Institutional and Behavioral Economics, Land Economics/Use,

Performance Metrics for the Assessment of Satellite Data Products: An Ocean Color Case Study

Performance assessment of ocean color satellite data has generally relied on statistical metrics chosen for their common usage and the rationale for selecting certain metrics is infrequently explained. Commonly reported statistics based on mean squared errors, such as the coefficient of determination (r2), root mean square error, and regression slopes, are most appropriate for Gaussian distributions without outliers and, therefore, are often not ideal for ocean color algorithm performance assessment, which is often limited by sample availability. In contrast, metrics based on simple deviations, such as bias and mean absolute error, as well as pair-wise comparisons, often provide more robust and straightforward quantities for evaluating ocean color algorithms with non-Gaussian distributions and outliers. This study uses a SeaWiFS chlorophyll-a validation data set to demonstrate a framework for satellite data product assessment and recommends a multi-metric and user-dependent approach that can be applied within science, modeling, and resource management communities

Preserved appreciation of aesthetic elements of speech and music prosody in an amusic individual: A holistic approach

We present a follow-up study on the case of a Greek amusic adult, B.Z., whose impaired performance on scale, contour, interval, and meter was reported by Paraskevopoulos, Tsapkini, and Peretz in 2010, employing a culturally-tailored version of the Montreal Battery of Evaluation of Amusia. In the present study, we administered a novel set of perceptual judgement tasks designed to investigate the ability to appreciate holistic prosodic aspects of ‘expressiveness’ and emotion in phrase length music and speech stimuli. Our results show that, although diagnosed as a congenital amusic, B.Z. scored as well as healthy controls (N = 24) on judging ‘expressiveness’ and emotional prosody in both speech and music stimuli. These findings suggest that the ability to make perceptual judgements about such prosodic qualities may be preserved in individuals who demonstrate difficulties perceiving basic musical features such as melody or rhythm. B.Z.’s case yields new insights into amusia and the processing of speech and music prosody through a holistic approach. The employment of novel stimuli with relatively fewer non-naturalistic manipulations, as developed for this study, may be a useful tool for revealing unexplored aspects of music and speech cognition and offer the possibility to further the investigation of the perception of acoustic streams in more authentic auditory conditions

The effects of school reform under NCLB waivers: Evidence from focus schools in Kentucky.

Under waivers to the No Child Left Behind (NCLB) Act, the federal government required states to identify schools where targeted subgroups of students have the lowest achievement and to implement reforms in these “Focus Schools.” In this study, we examine the Focus School reforms in the state of Kentucky. The reforms in this state are uniquely interesting for several reasons. One is that the state developed unusually explicit guidance for Focus Schools centered on a comprehensive school-planning process. Second, the state identified Focus Schools using a “super subgroup” measure that combined traditionally low-performing subgroups into an umbrella group. This design feature may have catalyzed broader whole-school reforms and attenuated the incentives to target reform efforts narrowly. Using regression discontinuity designs, we find that these reforms led to substantial improvements in school performance, raising math achievement by 17 percent and reading achievement by 9 percent

Explainable AI (XAI): Improving At-Risk Student Prediction with Theory-Guided Data Science, K-means Classification, and Genetic Programming

This research explores the use of eXplainable Artificial Intelligence (XAI) in Educational Data Mining (EDM) to improve the performance and explainability of artificial intelligence (AI) and machine learning (ML) models predicting at-risk students. Explainable predictions provide students and educators with more insight into at-risk indicators and causes, which facilitates instructional intervention guidance. Historically, low student retention has been prevalent across the globe as nations have implemented a wide range of interventions (e.g., policies, funding, and academic strategies) with only minimal improvements in recent years. In the US, recent attrition rates indicate two out of five first-time freshman students will not graduate from the same four-year institution within six years. In response, emerging AI research leveraging recent advancements in Deep Learning has demonstrated high predictive accuracy for identifying at-risk students, which is useful for planning instructional interventions. However, research suggested a general trade-off between performance and explainability of predictive models. Those that outperform, such as deep neural networks (DNN), are highly complex and considered black boxes (i.e., systems that are difficult to explain, interpret, and understand). The lack of model transparency/explainability results in shallow predictions with limited feedback prohibiting useful intervention guidance. Furthermore, concerns for trust and ethical use are raised for decision-making applications that involve humans, such as health, safety, and education. To address low student retention and the lack of interpretable models, this research explored the use of eXplainable Artificial Intelligence (XAI) in Educational Data Mining (EDM) to improve instruction and learning. More specifically, XAI has the potential to enhance the performance and explainability of AI/ML models predicting at-risk students. The scope of this study includes a hybrid research design comprising: (1) a systematic literature review of XAI and EDM applications in education; (2) the development of a theory-guided feature selection (TGFS) conceptual learning model; and (3) an EDM study exploring the efficacy of a TGFS XAI model. The EDM study implemented K-Means Classification for explorative (unsupervised) and predictive (supervised) analysis in addition to assessing Genetic Programming (GP), a type of XAI model, predictive performance, and explainability against common AI/ML models. Online student activity and performance data were collected from a learning management system (LMS) from a four-year higher education institution. Student data was anonymized and protected to ensure data privacy and security. Data was aggregated at weekly intervals to compute and assess the predictive performance (sensitivity, recall, and f-1 score) over time. Mean differences and effect sizes are reported at the .05 significance level. Reliability and validity are improved by implementing research best practices