Interaction Tree Specifications: A Framework for Specifying Recursive, Effectful Computations That Supports Auto-Active Verification (Artifact)

This paper presents a specification framework for monadic, recursive, interactive programs that supports auto-active verification, an approach that combines user-provided guidance with automatic verification techniques. This verification tool is designed to have the flexibility of a manual approach to verification along with the usability benefits of automatic approaches. We accomplish this by augmenting Interaction Trees, a Coq datastructure for representing effectful computations, with logical quantifier events. We show that this yields a language of specifications that are easy to understand, automatable, and are powerful enough to handle properties that involve non-termination. Our framework is implemented as a library in Coq. We demonstrate the effectiveness of this framework by verifying real, low-level code

Interaction Tree Specifications: A Framework for Specifying Recursive, Effectful Computations That Supports Auto-Active Verification

This paper presents a specification framework for monadic, recursive, interactive programs that supports auto-active verification, an approach that combines user-provided guidance with automatic verification techniques. This verification tool is designed to have the flexibility of a manual approach to verification along with the usability benefits of automatic approaches. We accomplish this by augmenting Interaction Trees, a Coq datastructure for representing effectful computations, with logical quantifier events. We show that this yields a language of specifications that are easy to understand, automatable, and are powerful enough to handle properties that involve non-termination. Our framework is implemented as a library in Coq. We demonstrate the effectiveness of this framework by verifying real, low-level code

Factors related to psychotherapists' self-assessment when treating anxiety and other disorders.

The aim of the study was to replicate and extend recent findings regarding therapists' self-assessment biases. This study examined clinicians' estimates of their abilities when working with general clinical groups and with anxious patients, and of the recovery/improvement rates of their clients. It also considered what clinician personality traits and clinical practice elements were associated with such estimates. A total of 195 out 801 clinicians completed a survey regarding self-ratings, team ratings, therapy outcomes for their clients, and their own personality traits. The great majority of clinicians rated themselves and their teams as being better clinicians than their peers, though not to as extreme a level as in the previous study. They also reported exceptionally positive therapy outcomes. Due to the large proportion of non-responders, it is possible that these findings do not reflect actual self-assessment bias, but a greater willingness to participate among clinicians who are more skilled and with particular personality styles. However, the data suggest that perceptions of skill and therapy outcome might be associated with clinician personality characteristics, though not with other clinical practice variables. These interpretations should be treated with caution due to the limited response rate. Different possible explanations for these patterns of self-assessment are outlined, including conscious and unconscious processes. Methods for enhancing accurate skill perception are discussed, including self-monitoring and supervision

Towards a comprehensive structural coverage of completed genomes: a structural genomics viewpoint

BACKGROUND: Structural genomics initiatives were established with the aim of solving protein structures on a large-scale. For many initiatives, such as the Protein Structure Initiative (PSI), the primary aim of target selection is focussed towards structurally characterising protein families which, so far, lack a structural representative. It is therefore of considerable interest to gain insights into the number and distribution of these families, and what efforts may be required to achieve a comprehensive structural coverage across all protein families. RESULTS: In this analysis we have derived a comprehensive domain annotation of the genomes using CATH, Pfam-A and Newfam domain families. We consider what proportions of structurally uncharacterised families are accessible to high-throughput structural genomics pipelines, specifically those targeting families containing multiple prokaryotic orthologues. In measuring the domain coverage of the genomes, we show the benefits of selecting targets from both structurally uncharacterised domain families, whilst in addition, pursuing additional targets from large structurally characterised protein superfamilies. CONCLUSION: This work suggests that such a combined approach to target selection is essential if structural genomics is to achieve a comprehensive structural coverage of the genomes, leading to greater insights into structure and the mechanisms that underlie protein evolution