"I like my beer cold, my TV loud, and my homosexuals flaming!" : preference for stereotype consistency as a function of a person x situation interaction /

The study centers on the power of Right-Wing Authoritarianism (RWA) and Social Dominance Orientation (SDO) as predictors of prejudice against stereotypical and nonstereotypical homosexuals under the threat of death and the threat of uncertainty. Right-wing authoritarianism (RWA) is an individual difference variable that measures the tendency for individuals to unquestionably follow those perceived to be authorities. Social Dominance Orientation (SDO) is an individual difference variable that measures the degree to which an individual prefers inequality among social groups. The RWA and SDO Scales are considered to be two of the strongest predictors of prejudice, such as prejudice against homosexuals. The study focuses on the unique predictive power of these two variables in predicting prejudice against homosexuals. The study also examines the role of situational threat in prejudice, specifically the threat of death (mortality salience) and the threat of uncertainty (uncertainty salience). Competing predictions from theories involving the threat of death (Terror Management Theory) and the threat of uncertainty (Uncertainty Management Theory) are also tested. The preference for expected information in the form of stereotypes concerning male homosexuals (that is, a stereotypical or non-stereotypical homosexual) were tested. The difference between the predictive power ofRWA and SDO was examined by measuring how these variables predict liking of a stereotypical or non-stereotypical homosexual under the threat of death, the threat of uncertainty, or a control condition. Along with completing a measure for RWA and a measure for SDO, participants were asked to think of their own death, of their being uncertain or about watching television then were asked to read about a week in the life of either a stereotypical or non-stereotypical male homosexual. Participants were then asked to evaluate the individual and his essay. Based on the participants' evaluations, results from 180 heterosexual university students show that RWA and SDO are strong predictors for disliking of a stereotypical homosexual under the threat of uncertainty and disliking of a non-stereotypical homosexual under the threat of death. Furthermore, however, results show that RWA is a particularly strong predictor of disliking of a stereotypical homosexual under the threat of uncertainty, whereas SDO is an exceptionally strong predictor of disliking of the non-stereotypical homosexual under the threat of death. This further adds to the notion that RWA and SDO are indeed unique predictors of prejudice. Implications are also explored, including the fact that the study simuhaneously examined the role of individual difference variables and situational threat variables, as well as exploratory analysis on Dominating Authoritarians.

Evidence on the relationship between incentives and exogenous risk

Theoretical models on moral hazard provide competing predictions on the incentive-risk relationship. These predictions are derived under the assumptions of homogeneous agents and exogenous risk. However, the existing empirical evidence does not account for risk-aversion heterogeneity and risk endogeneity. This paper uses a well-built database on tenancy contracts to address these issues. Detailed information on cropping activities is used to measure the exogenous risk. Risk-aversion heterogeneity and other self-selection problems are addressed through a portfolio schedule and a subsample of farmers who simultaneously own and sharecrop different farms. This controlled exercise finds a direct relation between incentives and exogenous risk.

The role of team identification in the dissimilarity-conflict relationship

This study examined the role of team identification in the dissimilarity and conflict relationship. We tested competing predictions that team identification would either mediate or moderate the positive associations between visible (age, gender and ethnic background), professional (background) and value dissimilarity and task and relationship conflict. Data was collected from 27 MBA student teams twice during a semester. Multilevel modelling and a longitudinal design were used. Results showed that value dissimilarity was positively associated with task and relationship conflict at Time 2. Its effects on relationship conflict at Time 1 were moderated by team identification. Team identification also moderated the effects of gender, age and ethnic dissimilarity on task conflict at Time 2, and the effects of gender and professional dissimilarity on relationship conflict at Time 2. No support was obtained for the mediating role of team identification on the associations between dissimilarity and conflict, or for changes in the effects of dissimilarity over time.

Aspect selection in adult L2 Spanish and the competing systems hypothesis: when pedagogical and linguistic rules conflict.

Native-like use of preterit and imperfect morphology in all contexts by English learners of L2 Spanish is the exception rather than the rule, even for successful learners. Nevertheless, recent research has demonstrated that advanced English learners of L2 Spanish attain a native-like morphosyntactic competence for the preterit/imperfect contrast, as evidenced by their native-like knowledge of associated semantic entailments (Goodin-Mayeda and Rothman 2007, Montrul and Slabakova 2003, Slabakova and Montrul 2003, Rothman and Iverson 2007). In addition to an L2 disassociation of morphology and syntax (e.g., Bruhn de Garavito 2003, Lardiere 1998, 2000, 2005, Prévost and White 1999, 2000, Schwartz 2003), I hypothesize that a system of learned pedagogical rules contributes to target-deviant L2 performance in this domain through the most advanced stages of L2 acquisition via its competition with the generative system. I call this hypothesis the Competing Systems Hypothesis. To test its predictions, I compare and contrast the use of the preterit and imperfect in two production tasks by native, tutored (classroom), and naturalistic learners of L2 Spanish.

The competing roles of extensional viscosity and normal stress differences in complex flows of elastic liquids

In various attempts to relate the behaviour of highly-elastic liquids in complex flows to their rheometrical behaviour, obvious candidates for study have been the variation of shear viscosity with shear rate, the two normal stress differences N(1) and N(2) especially N(1), and the extensional viscosity eta(E). In this paper, we shall be mainly interested in `constant-viscosity` Boger fluids, and, accordingly, we shall limit attention to N(1) and eta(E). We shall concentrate on two important flows - axisymmetric contraction flow and ""splashing"" (particularly that which arises when a liquid drop falls onto the free Surface of the same liquid). Modem numerical techniques are employed to provide the theoretical predictions. It is shown that the two obvious manifestations of viscoelastic rheometrical behaviour can sometimes be opposing influences in determining flow characteristics. Specifically, in an axisymmetric contraction flow, high eta(E) , can retard the flow, whereas high N(1) can have the opposite effect. In the splashing experiment, high eta(E) can certainly reduce the height of the so-called Worthington jet, thus confirming some early suggestions, but, again, other rheometrical influences can also have a role to play and the overall picture may not be as clear as it was once envisaged.

Costly Revenue-Raising and the Case for Favoring Import-Competing Industries

A standard finding in the political economy of trade policy literature is that we should expect export-oriented industries to attract more assistance than import-competing industries. In reality, however, trade policy is heavily biased toward supporting import industries. This paper shows within a standard protection for sale framework, how the costliness of raising revenue via taxation makes trade subsidies less desirable and trade taxes more desirable. The model is then estimated and its predictions tested using U.S. tariff data. An empirical estimate of the costliness of revenue-raising is also obtained.

Re-Examining the Stop-Signal Task to Test Competing Theories of AD/HD

The current study tested two competing models of Attention-Deficit/Hyperactivity Disorder (AD/HD), the inhibition and state regulation theories, by conducting fine-grained analyses of the Stop-Signal Task and another putative measure of behavioral inhibition, the Gordon Continuous Performance Test (G-CPT), in a large sample of children and adolescents. The inhibition theory posits that performance on these tasks reflects increased difficulties for AD/HD participants to inhibit prepotent responses. The model predicts that putative stop-signal reaction time (SSRT) group differences on the Stop-Signal Task will be primarily related to AD/HD participants requiring more warning than control participants to inhibit to the stop-signal and emphasizes the relative importance of commission errors, particularly "impulsive" type commissions, over other error types on the G-CPT. The state regulation theory, on the other hand, proposes response variability due to difficulties maintaining an optimal state of arousal as the primary deficit in AD/HD. This model predicts that SSRT differences will be more attributable to slower and/or more variable reaction time (RT) in the AD/HD group, as opposed to reflecting inhibitory deficits. State regulation assumptions also emphasize the relative importance of omission errors and "slow processing" type commissions over other error types on the G-CPT. Overall, results of Stop-Signal Task analyses were more supportive of state regulation predictions and showed that greater response variability (i.e., SDRT) in the AD/HD group was not reducible to slow mean reaction time (MRT) and that response variability made a larger contribution to increased SSRT in the AD/HD group than inhibitory processes. Examined further, ex-Gaussian analyses of Stop-Signal Task go-trial RT distributions revealed that increased variability in the AD/HD group was not due solely to a few excessively long RTs in the tail of the AD/HD distribution (i.e., tau), but rather indicated the importance of response variability throughout AD/HD group performance on the Stop-Signal Task, as well as the notable sensitivity of ex-Gaussian analyses to variability in data screening procedures. Results of G-CPT analyses indicated some support for the inhibition model, although error type analyses failed to further differentiate the theories. Finally, inclusion of primary variables of interest in exploratory factor analysis with other neurocognitive predictors of AD/HD indicated response variability as a separable construct and further supported its role in Stop-Signal Task performance. Response variability did not, however, make a unique contribution to the prediction of AD/HD symptoms beyond measures of motor processing speed in multiple deficit regression analyses. Results have implications for the interpretation of the processes reflected in widely-used variables in the AD/HD literature, as well as for the theoretical understanding of AD/HD.

SEQUENTIAL DECISIONS AND PREDICTIONS IN NATURAL LANGUAGE PROCESSING

Natural language processing has achieved great success in a wide range of ap- plications, producing both commercial language services and open-source language tools. However, most methods take a static or batch approach, assuming that the model has all information it needs and makes a one-time prediction. In this disser- tation, we study dynamic problems where the input comes in a sequence instead of all at once, and the output must be produced while the input is arriving. In these problems, predictions are often made based only on partial information. We see this dynamic setting in many real-time, interactive applications. These problems usually involve a trade-off between the amount of input received (cost) and the quality of the output prediction (accuracy). Therefore, the evaluation considers both objectives (e.g., plotting a Pareto curve). Our goal is to develop a formal understanding of sequential prediction and decision-making problems in natural language processing and to propose efficient solutions. Toward this end, we present meta-algorithms that take an existent batch model and produce a dynamic model to handle sequential inputs and outputs. Webuild our framework upon theories of Markov Decision Process (MDP), which allows learning to trade off competing objectives in a principled way. The main machine learning techniques we use are from imitation learning and reinforcement learning, and we advance current techniques to tackle problems arising in our settings. We evaluate our algorithm on a variety of applications, including dependency parsing, machine translation, and question answering. We show that our approach achieves a better cost-accuracy trade-off than the batch approach and heuristic-based decision- making approaches. We first propose a general framework for cost-sensitive prediction, where dif- ferent parts of the input come at different costs. We formulate a decision-making process that selects pieces of the input sequentially, and the selection is adaptive to each instance. Our approach is evaluated on both standard classification tasks and a structured prediction task (dependency parsing). We show that it achieves similar prediction quality to methods that use all input, while inducing a much smaller cost. Next, we extend the framework to problems where the input is revealed incremen- tally in a fixed order. We study two applications: simultaneous machine translation and quiz bowl (incremental text classification). We discuss challenges in this set- ting and show that adding domain knowledge eases the decision-making problem. A central theme throughout the chapters is an MDP formulation of a challenging problem with sequential input/output and trade-off decisions, accompanied by a learning algorithm that solves the MDP.

On the accuracy of two-fluid model predictions for a particular gas-solid riser flow

Literature presents a huge number of different simulations of gas-solid flows in risers applying two-fluid modeling. In spite of that, the related quantitative accuracy issue remains mostly untouched. This state of affairs seems to be mainly a consequence of modeling shortcomings, notably regarding the lack of realistic closures. In this article predictions from a two-fluid model are compared to other published two-fluid model predictions applying the same Closures, and to experimental data. A particular matter of concern is whether the predictions are generated or not inside the statistical steady state regime that characterizes the riser flows. The present simulation was performed inside the statistical steady state regime. Time-averaged results are presented for different time-averaging intervals of 5, 10, 15 and 20 s inside the statistical steady state regime. The independence of the averaged results regarding the time-averaging interval is addressed and the results averaged over the intervals of 10 and 20 s are compared to both experiment and other two-fluid predictions. It is concluded that the two-fluid model used is still very crude, and cannot provide quantitative accurate results, at least for the particular case that was considered. (C) 2009 Elsevier Inc. All rights reserved.

Parameterization and Evaluation of Predictions of DSSAT/CANEGRO for Brazilian Sugarcane

The DSSAT/CANEGRO model was parameterized and its predictions evaluated using data from five sugarcane (Sacchetrum spp.) experiments conducted in southern Brazil. The data used are from two of the most important Brazilian cultivars. Some parameters whose values were either directly measured or considered to be well known were not adjusted. Ten of the 20 parameters were optimized using a Generalized Likelihood Uncertainty Estimation (GLUE) algorithm using the leave-one-out cross-validation technique. Model predictions were evaluated using measured data of leaf area index (LA!), stalk and aerial dry mass, sucrose content, and soil water content, using bias, root mean squared error (RMSE), modeling efficiency (Eff), correlation coefficient, and agreement index. The Decision Support System for Agrotechnology Transfer (DSSAT)/CANEGRO model simulated the sugarcane crop in southern Brazil well, using the parameterization reported here. The soil water content predictions were better for rainfed (mean RMSE = 0.122mm) than for irrigated treatment (mean RMSE = 0.214mm). Predictions were best for aerial dry mass (Eff = 0.850), followed by stalk dry mass (Eff = 0.765) and then sucrose mass (Eff = 0.170). Number of green leaves showed the worst fit (Eff = -2.300). The cross-validation technique permits using multiple datasets that would have limited use if used independently because of the heterogeneity of measures and measurement strategies.

Olfactory eavesdropping between two competing stingless bee species

Foragers can improve search efficiency, and ultimately fitness, by using social information: cues and signals produced by other animals that indicate food location or quality. Social information use has been well studied in predator-prey systems, but its functioning within a trophic level remains poorly understood. Eavesdropping, use of signals by unintended recipients, is of particular interest because eavesdroppers may exert selective pressure on signaling systems. We provide the most complete study to date of eavesdropping between two competing social insect species by determining the glandular source and composition of a recruitment pheromone, and by examining reciprocal heterospecific responses to this signal. We tested eavesdropping between Trigona hyalinata and Trigona spinipes, two stingless bee species that compete for floral resources, exhibit a clear dominance hierarchy and recruit nestmates to high-quality food sources via pheromone trails. Gas chromatography-mass spectrometry of T. hyalinata recruitment pheromone revealed six carboxylic esters, the most common of which is octyl octanoate, the major component of T. spinipes recruitment pheromone. We demonstrate heterospecific detection of recruitment pheromones, which can influence heterospecific and conspecific scout orientation. Unexpectedly, the dominant T. hyalinata avoided T. spinipes pheromone in preference tests, while the subordinate T. spinipes showed neither attraction to nor avoidance of T. hyalinata pheromone. We suggest that stingless bees may seek to avoid conflict through their eavesdropping behavior, incorporating expected costs associated with a choice into the decision-making process.

Molecular dynamics, density functional, ADMET predictions, virtual screening, and molecular interaction field studies for identification and evaluation of novel potential CDK2 inhibitors in cancer therapy

In this work, we have used molecular dynamics, density functional theory, virtual screening, ADMET predictions, and molecular interaction field studies to design and propose eight novel potential inhibitors of CDK2. The eight molecules proposed showed interesting structural characteristics that are required for inhibiting the CDK2 activity and show potential as drug candidates for the treatment of cancer. The parameters related to the Rule of Five were calculated, and only one of the molecules violated more than one parameter. One of the proposals and one of the drug-like compounds selected by virtual screening indicated to be promising candidates for CDK2-based cancer therapy.

Computer-aided drug design and ADMET predictions for identification and evaluation of novel potential farnesyltransferase inhibitors in cancer therapy

We have used various computational methodologies including molecular dynamics, density functional theory, virtual screening, ADMET predictions and molecular interaction field studies to design and analyze four novel potential inhibitors of farnesyltransferase (FTase). Evaluation of two proposals regarding their drug potential as well as lead compounds have indicated them as novel promising FTase inhibitors, with theoretically interesting pharmacotherapeutic profiles, when Compared to the very active and most cited FTase inhibitors that have activity data reported, which are launched drugs or compounds in clinical tests. One of our two proposals appears to be a more promising drug candidate and FTase inhibitor, but both derivative molecules indicate potentially very good pharmacotherapeutic profiles in comparison with Tipifarnib and Lonafarnib, two reference pharmaceuticals. Two other proposals have been selected with virtual screening approaches and investigated by LIS, which suggest novel and alternatives scaffolds to design future potential FTase inhibitors. Such compounds can be explored as promising molecules to initiate a research protocol in order to discover novel anticancer drug candidates targeting farnesyltransferase, in the fight against cancer. (C) 2009 Elsevier Inc. All rights reserved.

Molecular dynamics, flexible docking, virtual screening, ADMET predictions, and molecular interaction field studies to design novel potential MAO-B inhibitors

Monoamine oxidase is a flavoenzyme bound to the mitochondrial outer membranes of the cells, which is responsible for the oxidative deamination of neurotransmitter and dietary amines. It has two distinct isozymic forms, designated MAO-A and MAO-B, each displaying different substrate and inhibitor specificities. They are the well-known targets for antidepressant, Parkinson`s disease, and neuroprotective drugs. Elucidation of the x-ray crystallographic structure of MAO-B has opened the way for the molecular modeling studies. In this work we have used molecular modeling, density functional theory with correlation, virtual screening, flexible docking, molecular dynamics, ADMET predictions, and molecular interaction field studies in order to design new molecules with potential higher selectivity and enzymatic inhibitory activity over MAO-B.

On modifications to the long-term survival mixture model in the presence of competing risks

A mixture model for long-term survivors has been adopted in various fields such as biostatistics and criminology where some individuals may never experience the type of failure under study. It is directly applicable in situations where the only information available from follow-up on individuals who will never experience this type of failure is in the form of censored observations. In this paper, we consider a modification to the model so that it still applies in the case where during the follow-up period it becomes known that an individual will never experience failure from the cause of interest. Unless a model allows for this additional information, a consistent survival analysis will not be obtained. A partial maximum likelihood (ML) approach is proposed that preserves the simplicity of the long-term survival mixture model and provides consistent estimators of the quantities of interest. Some simulation experiments are performed to assess the efficiency of the partial ML approach relative to the full ML approach for survival in the presence of competing risks.