Online algorithms for maximizing weighted throughput of unit jobs with temperature constraints

We consider a temperature-aware online deadline scheduling model. The objective is to schedule a number of unit jobs, with release dates, deadlines, weights and heat contributions, to maximize the weighted throughput subject to a temperature threshold. We first give an optimally competitive randomized algorithm. Then we give a constant competitive randomized algorithm that allows a tradeoff between the maximum heat contribution of jobs and the competitiveness. Finally we consider the multiple processor case and give several tight upper and lower bounds.     

On the functional form of temporal discounting: An optimized adaptive test

The tendency to discount the value of future rewards has become one of the best-studied constructs in the behavioral sciences. Although hyperbolic discounting remains the dominant quantitative characterization of this phenomenon, a variety of models have been proposed and consensus around the one that most accurately describes behavior has been elusive. To help bring some clarity to this issue, we propose an Adaptive Design Optimization (ADO) method for fitting and comparing models of temporal discounting. We then conduct an ADO experiment aimed at discriminating among six popular models of temporal discounting. Rather than supporting a single underlying model, our results show that each model is inadequate in some way to describe the full range of behavior exhibited across subjects. The precision of results provided by ADO further identify specific properties of models, such as accommodating both increasing and decreasing impatience, that are mandatory to describe temporal discounting broadly.     

Separate and unequal: post-tsunami aid distribution in Southern India

Objective. Disasters are a regular occurrence throughout the world. Whether all eligible victims of a catastrophe receive similar amounts of aid from governments and donors following a crisis remains an open question.Methods. I use data on 62 similarly damaged inland fishing villages in five districts of southeastern India following the 2004 Indian Ocean tsunami to measure the causal influence of caste, location, wealth, and bridging social capital on the receipt of aid. Using two-limit tobit and negative binomial models, I investigate the factors that influence the time spent in refugee camps, receipt of an initial aid packet, and receipt of 4,000 rupees.Results. Caste, family status, and wealth proved to be powerful predictors of beneficiaries and nonbeneficiaries during the aid process.Conclusion. While many scholars and practitioners envision aid distribution as primarily a technocratic process, this research shows that discrimination and financial resources strongly affect the flow of disaster aid.     

Predicting treatment efficacy via quantitative magnetic resonance imaging: a Bayesian joint model

The prognosis for patients with high grade gliomas is poor, with a median survival of 1 year. Treatment efficacy assessment is typically unavailable until 5-6 months post diagnosis. Investigators hypothesize that quantitative magnetic resonance imaging can assess treatment efficacy 3 weeks after therapy starts, thereby allowing salvage treatments to begin earlier. The purpose of this work is to build a predictive model of treatment efficacy by using quantitative magnetic resonance imaging data and to assess its performance. The outcome is 1-year survival status. We propose a joint, two-stage Bayesian model. In stage I, we smooth the image data with a multivariate spatiotemporal pairwise difference prior. We propose four summary statistics that are functionals of posterior parameters from the first-stage model. In stage II, these statistics enter a generalized non-linear model as predictors of survival status. We use the probit link and a multivariate adaptive regression spline basis. Gibbs sampling and reversible jump Markov chain Monte Carlo methods are applied iteratively between the two stages to estimate the posterior distribution. Through both simulation studies and model performance comparisons we find that we can achieve higher overall correct classification rates by accounting for the spatiotemporal correlation in the images and by allowing for a more complex and flexible decision boundary provided by the generalized non-linear model.     

A Preliminary Report on the Relationship Between Microaggressions Against Black People and Racism Among White College Students

Previous efforts to understand microaggressions have surveyed stigmatized group members’ experiences of receiving microaggressions. This report presents the first attempt to measure self-reported likelihood of delivering microaggressions rather than receiving microaggressions and to explore the association between the likelihood of delivering microaggressions and racial prejudice. We conducted a cross-sectional survey of 33 black and 118 non-Hispanic white undergraduate students at a large public Southern/Midwest university. Black students reported the degree to which a series of statements would be experienced as microaggressive. White students reported their likelihood of delivering those statements and completed measures of racial prejudice. White students’ self-reported likelihood of engaging in microaggressive acts was significantly related to all measures of racial prejudice. The single item “A lot of minorities are too sensitive” was the strongest predictor of negative feelings toward black people. Results offer preliminary support that the delivery of microaggressions by white students is not simply innocuous behavior and may be indicative of broad, complex, and negative racial attitudes and explicit underlying hostility and negative feelings toward black students.     

“You Cannot Be Like That Here”: Discourses of Sexual Identities among Urban Ghanaian Families

Gender Issues - While social discourses on gender and sexuality have become controversial in the African context, there is a tendency to overlook how the domestic space contrives and participates...     

Race, crime, and Republican strength: Minority politics in the post-civil rights era

This investigation assesses the relationships between racial threat and partisan dominance in state legislatures with tests of interactive hypotheses. The findings show that historically contingent expectations derived from racial threat theory, Republican law and order campaign appeals, and fundamentalist strength account for Republican strength in the legislatures. Two-way fixed-effects estimates based on pooled time-series analyses of 799 state-years in the post-civil rights era show that the percentage of Republicans in the state legislatures grew after increases in African American presence and the violent crime rates. The combined effects of a growth in African American and fundamentalist populations also account for increased Republican seats in these bodies. Statements by Republican campaign officials on how they deliberately used mass resentments against minorities to gain votes provide evidence about the intervening links between minority threat, the menace posed by high violent crime rates, and increased Republican strength in the state legislatures.     

A hierarchical Bayesian model for predicting the functional consequences of amino-acid polymorphisms

Summary.  Genetic polymorphisms in deoxyribonucleic acid coding regions may have a phenotypic effect on the carrier, e.g. by influencing susceptibility to disease. Detection of deleterious mutations via association studies is hampered by the large number of candidate sites; therefore methods are needed to narrow down the search to the most promising sites. For this, a possible approach is to use structural and sequence-based information of the encoded protein to predict whether a mutation at a particular site is likely to disrupt the functionality of the protein itself. We propose a hierarchical Bayesian multivariate adaptive regression spline (BMARS) model for supervised learning in this context and assess its predictive performance by using data from mutagenesis experiments on lac repressor and lysozyme proteins. In these experiments, about 12 amino-acid substitutions were performed at each native amino-acid position and the effect on protein functionality was assessed. The training data thus consist of repeated observations at each position, which the hierarchical framework is needed to account for. The model is trained on the lac repressor data and tested on the lysozyme mutations and vice versa. In particular, we show that the hierarchical BMARS model, by allowing for the clustered nature of the data, yields lower out-of-sample misclassification rates compared with both a BMARS and a frequen-tist MARS model, a support vector machine classifier and an optimally pruned classification tree.