Moral emotions and the envisioning of mitigating circumstances for wrongdoing

Anger may be more responsive than disgust to mitigating circumstances in judgments of wrongdoing. We tested this hypothesis in two studies where we had participants envision circumstances that could serve to mitigate an otherwise wrongful act. In Study 1, participants provided moral judgments, and ratings of anger and disgust, to a number of transgressions involving either harm or bodily purity. They were then asked to imagine and report whether there might be any circumstances that would make it all right to perform the act. Across transgression type, and controlling for covariance between anger and disgust, levels of anger were found to negatively predict the envisioning of mitigating circumstances for wrongdoing, while disgust was unrelated. Study 2 replicated and extended these findings to less serious transgressions, using a continuous measure of mitigating circumstances, and demonstrated the impact of
anger independent of deontological commitments. These findings highlight the differential relationship that anger and disgust have with the ability to envision mitigating factors.

Efficient Marginal Likelihood Computation for Gaussian Process Regression

In a Bayesian learning setting, the posterior distribution of a predictive model arises from a trade-off between its prior distribution and the conditional likelihood of observed data. Such distribution functions usually rely on additional hyperparameters which need to be tuned in order to achieve optimum predictive performance; this operation can be efficiently performed in an Empirical Bayes fashion by maximizing the posterior marginal likelihood of the observed data. Since the score function of this optimization problem is in general characterized by the presence of local optima, it is necessary to resort to global optimization strategies, which require a large number of function evaluations. Given that the evaluation is usually computationally intensive and badly scaled with respect to the dataset size, the maximum number of observations that can be treated simultaneously is quite limited. In this paper, we consider the case of hyperparameter tuning in Gaussian process regression. A straightforward implementation of the posterior log-likelihood for this model requires O(N^3) operations for every iteration of the optimization procedure, where N is the number of examples in the input dataset. We derive a novel set of identities that allow, after an initial overhead of O(N^3), the evaluation of the score function, as well as the Jacobian and Hessian matrices, in O(N) operations. We prove how the proposed identities, that follow from the eigendecomposition of the kernel matrix, yield a reduction of several orders of magnitude in the computation time for the hyperparameter optimization problem. Notably, the proposed solution provides computational advantages even with respect to state of the art approximations that rely on sparse kernel matrices.

Achieving optimal dose intensity with adjuvant chemotherapy in elderly breast cancer patients:A 10-year retrospective study in a UK institution

The study was to determine if breast cancer patients aged 65 and above could be given adjuvant chemotherapy safely while achieving an acceptable relative dose intensity of at least 85%. We identified all patients aged 65 and over who received adjuvant chemotherapy over the 10 year period, November 1999 to October 2009, and determined the proportion that achieved a relative dose intensity of at least 85% as well as the tolerability of their treatment. A total of 101 patients were identified, with a median age of 69 years (range 65-78).Of these, 25.7% of patients had at least one major comorbidity, 84.2% had a tumor size of 5 cm or less, 73.3% were node positive and 58.4% were hormone receptor positive. The chemotherapy regimens used were AC (Doxorubicin and Cyclophosphamide), FEC (Fluorouracil, Epirubicin, and Cyclophosphamide), CMF (Cyclophosphamide, Methotrexate, and Fluorouracil) and ECMF (Epirubicin followed by CMF). Seventy-nine patients (78.2%) achieved the relative dose intensity of at least 85%. With respect to toxicity, 11.9% of patients developed febrile neutropenia and 23.8% of patients required hospital admission during the treatment period, but there were no treatment-related deaths in the group. A significant proportion of patients aged 65 and above achieved the intended dose intensity of at least 85% over this 10-year period, with manageable toxicity levels. This supports the use of these regimens as adjuvant chemotherapy for breast cancer in this age group. © 2011 Wiley Periodicals, Inc.

Not all social cleavages are the same: On the relationship between religious diversity and party system fragmentation

Most studies examining the relationship between social cleavages and party system fragmentation maintain that higher levels of social diversity lead to greater party system fragmentation. However, most aggregate-level studies focus on one type of social cleavage:ethnic diversity. In order to develop a better understanding of how different cleavages impact electoral competition, this paper considers another type of social cleavage: religious diversity.Contrary to previous literature, higher levels of religious diversity provide incentives for cross-religious cooperation, which in turn reduces party system fragmentation. Using a cross national data set of elections from 1946-2011, the results show that, in contrast to most studies examining the effects of social cleavage diversity on the number of parties, higher religious diversity is associated with lower levels of party system fragmentation.

Assessing the effect of reducing agents on the selective catalytic reduction of NOx over Ag/Al2O3 catalysts

The selective catalytic reduction (SCR) of NOx in the presence of different reducing agents over Ag/Al2O3 prepared by wet impregnation was investigated by probing catalyst activity and using NMR relaxation time analysis to probe the strength of surface interaction of the various reducing agent species and water. The results reveal that the strength of surface interaction of the reducing agent relative to water, the latter present in engine exhausts as a fuel combustion product and, in addition, produced during the SCR reaction, plays an important role in determining catalyst performance. Reducing agents with weak strength of interaction with the catalyst surface, such as hydrocarbons, show poorer catalytic performance than reducing agents with a higher strength of interaction, such as alcohols. This is attributed to the greater ability of oxygenated species to compete with water in terms of surface interaction with the catalyst surface, hence reducing the inhibiting effect of water molecules blocking catalyst sites. The results support the observations of earlier work in that the light off-temperature and maximum NOx conversion and temperature at which that occurs are sensitive to the reducing agent present during reaction, and the proposal that improved catalyst performance is caused by increased adsorption strength of the reducing agent, relative to water, at the catalyst surface. Importantly, the NMR relaxation time analysis approach to characterising the strength of adsorption more readily describes the trends in catalytic behaviour than does a straightforward consideration of the polarity (i.e., relative permittivity) of the reducing agents studied here. In summary, this paper describes a simple approach to characterising the interaction energy of water and reducing agent so as to aid the selection of reducing agent and catalyst to be used in SCR conversions.