Automating Deductive Verification for Weak-Memory Programs

Writing correct programs for weak memory models such as the C11 memory model is challenging because of the weak consistency guarantees these models provide. The first program logics for the verification of such programs have recently been proposed, but their usage has been limited thus far to manual proofs. Automating proofs in these logics via first-order solvers is non-trivial, due to reasoning features such as higher-order assertions, modalities and rich permission resources. In this paper, we provide the first implementation of a weak memory program logic using existing deductive verification tools. We tackle three recent program logics: Relaxed Separation Logic and two forms of Fenced Separation Logic, and show how these can be encoded using the Viper verification infrastructure. In doing so, we illustrate several novel encoding techniques which could be employed for other logics. Our work is implemented, and has been evaluated on examples from existing papers as well as the Facebook open-source Folly library.Comment: Extended version of TACAS 2018 publicatio

A face for all seasons:searching for context-specific leadership traits and discovering a general preference for perceived health

Previous research indicates that followers tend to contingently match particular leader qualities to evolutionarily consistent situations requiring collective action (i.e., context-specific cognitive leadership prototypes) and information processing undergoes categorization which ranks certain qualities as first-order context-general and others as second-order context-specific. To further investigate this contingent categorization phenomenon we examined the “attractiveness halo”—a first-order facial cue which significantly biases leadership preferences. While controlling for facial attractiveness, we independently manipulated the underlying facial cues of health and intelligence and then primed participants with four distinct organizational dynamics requiring leadership (i.e., competition vs. cooperation between groups and exploratory change vs. stable exploitation). It was expected that the differing requirements of the four dynamics would contingently select for relatively healthier- or intelligent-looking leaders. We found perceived facial intelligence to be a second-order context-specific trait—for instance, in times requiring a leader to address between-group cooperation—whereas perceived health is significantly preferred across all contexts (i.e., a first-order trait). The results also indicate that facial health positively affects perceived masculinity while facial intelligence negatively affects perceived masculinity, which may partially explain leader choice in some of the environmental contexts. The limitations and a number of implications regarding leadership biases are discussed

Rigorous Polynomial Approximation using Taylor Models in Coq

International audienceOne of the most common and practical ways of representing a real function on machines is by using a polynomial approximation. It is then important to properly handle the error introduced by such an approximation. The purpose of this work is to offer guaranteed error bounds for a specific kind of rigorous polynomial approximation called Taylor model. We carry out this work in the Coq proof assistant, with a special focus on genericity and efficiency for our implementation. We give an abstract interface for rigorous polynomial approximations, parameter- ized by the type of coefficients and the implementation of polynomials, and we instantiate this interface to the case of Taylor models with inter- val coefficients, while providing all the machinery for computing them. We compare the performances of our implementation in Coq with those of the Sollya tool, which contains an implementation of Taylor models written in C. This is a milestone in our long-term goal of providing fully formally proved and efficient Taylor models

Scalable Purification and Characterization of the Anticancer Lunasin Peptide from Soybean

Lunasin is a peptide derived from the soybean 2S albumin seed protein that has both anticancer and anti-inflammatory activities. Large-scale animal studies and human clinical trials to determine the efficacy of lunasin in vivo have been hampered by the cost of synthetic lunasin and the lack of a method for obtaining gram quantities of highly purified lunasin from plant sources. The goal of this study was to develop a large-scale method to generate highly purified lunasin from defatted soy flour. A scalable method was developed that utilizes the sequential application of anion-exchange chromatography, ultrafiltration, and reversed-phase chromatography. This method generates lunasin preparations of >99% purity with a yield of 442 mg/kg defatted soy flour. Mass spectrometry of the purified lunasin revealed that the peptide is 44 amino acids in length and represents the original published sequence of lunasin with an additional C-terminal asparagine residue. Histone-binding assays demonstrated that the biological activity of the purified lunasin was similar to that of synthetic lunasin. This study provides a robust method for purifying commercial-scale quantities of biologically-active lunasin and clearly identifies the predominant form of lunasin in soy flour. This method will greatly facilitate the development of lunasin as a potential nutraceutical or therapeutic anticancer agent

Nomad DNA — A model for movement and duplication of DNA sequences in plant genomes

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43425/1/11103_2004_Article_BF00019160.pd