Consistency of Concave Regression with an Application to Current-Status Data

We consider the problem of nonparametric estimation of a concave regression function F. We show that the supremum distance between the least square s estimatorand F on a compact interval is typically of order(log(n)/n)2/5. This entails rates of convergence for the estimator’s derivative. Moreover, we discuss the impact of additional constraints on F such as monotonicity and pointwise bounds. Then we apply these results to the analysis of current status data, where the distribution function of the event times is assumed to be concave.

Striving for consistency shapes emotional responses to other’s outcomes

Based on the balance theory (Heider, 1958), we hypothesized that emotions (i.e., schadenfreude, resentment, joy and sorrow) induced by other person’s outcomes function as responses restoring balance within cognitive units consisting of the perceiver, other persons and their outcomes. As a consequence, emotional reactions towards others’ outcomes depend on the perceiver’s attitudes in such a way that outcomes of a well-liked person rise congruous responses (sorrow after failure and joy after success), while outcomes of a disliked other lead to incongruous responses (schadenfreude after failure and resentment after success). Our participants recalled a situation from their past in which somebody they liked or disliked had succeed or failed. Additionally, we manipulated whether the outcome referred to a domain where participants’ self-interest was involved or not. We analyzed the participants’ average emotional state as well as specific emotions induced by the recalled events. Consistently with expectations we found that balancing principles played a major role in shaping emotional responses to successes and failures of person who were well-liked or disliked.

Increased Resting State Network Connectivity in Synesthesia: Evidence for a Neural Basis of Synesthetic Consistency

Studying individual differences in conscious awareness can potentially lend fundamental insights into the neural bases of binding mechanisms and consciousness (Cohen Kadosh and Henik, 2007). Partly for this reason, considerable attention has been devoted to the neural mechanisms underlying grapheme–color synesthesia, a healthy condition involving atypical brain activation and the concurrent experience of color photisms in response to letters, numbers, and words. For instance, the letter C printed in black on a white background may elicit a yellow color photism that is perceived to be spatially colocalized with the inducing stimulus or internally in the “mind's eye” as, for instance, a visual image. Synesthetic experiences are involuntary, idiosyncratic, and consistent over time (Rouw et al., 2011). To date, neuroimaging research on synesthesia has focused on brain areas activated during the experience of synesthesia and associated structural brain differences. However, activity patterns of the synesthetic brain at rest remain largely unexplored. Moreover, the neural correlates of synesthetic consistency, the hallmark characteristic of synesthesia, remain elusive.

On the Evaluation of a DSL for Architectural Consistency Checking

Software architecture erodes over time and needs to be constantly monitored to be kept consistent with its original intended design. Consistency is rarely monitored using automated techniques. The cost associated to such an activity is typically not considered proportional to its benefits. To improve this situation, we propose Dicto, a uniform DSL for specifying architectural invariants. This language is designed to reduce the cost of consistency checking by offering a framework in which existing validation tools can be matched to newly-defined language constructs. In this paper we discuss how such a DSL can be qualitatively and qualitatively evaluated in practice.