Safety verification of asynchronous pushdown systems with shaped stacks

In this paper, we study the program-point reachability problem of concurrent pushdown systems that communicate via unbounded and unordered message buffers. Our goal is to relax the common restriction that messages can only be retrieved by a pushdown process when its stack is empty. We use the notion of partially commutative context-free grammars to describe a new class of asynchronously communicating pushdown systems with a mild shape constraint on the stacks for which the program-point coverability problem remains decidable. Stacks that fit the shape constraint may reach arbitrary heights; further a process may execute any communication action (be it process creation, message send or retrieval) whether or not its stack is empty. This class extends previous computational models studied in the context of asynchronous programs, and enables the safety verification of a large class of message passing programs

Rapid Automatic Motor Encoding of Competing Reach Options.

Mounting neural evidence suggests that, in situations in which there are multiple potential targets for action, the brain prepares, in parallel, competing movements associated with these targets, prior to implementing one of them. Central to this interpretation is the idea that competing viewed targets, prior to selection, are rapidly and automatically transformed into corresponding motor representations. Here, by applying target-specific, gradual visuomotor rotations and dissociating, unbeknownst to participants, the visual direction of potential targets from the direction of the movements required to reach the same targets, we provide direct evidence for this provocative idea. Our results offer strong empirical support for theories suggesting that competing action options are automatically represented in terms of the movements required to attain them. The rapid motor encoding of potential targets may support the fast optimization of motor costs under conditions of target uncertainty and allow the motor system to inform decisions about target selection.Funding from the Natural Sciences and Engineering Research Council of Canada (NSERC), the Wellcome Trust, the Royal Society Noreen Murray Professorship in Neurobiology (to D.M.W.), the Canadian Foundation for Innovation, and the Ontario Innovation Trust supported this study. J.P.G. was supported by an NSERC Banting Postdoctoral Fellowship and a Canadian Institutes of Health Research Postdoctoral Fellowship. B.M.S. was supported by an NSERC graduate scholarship

Incorporating predicted functions of nonsynonymous variants into gene-based analysis of exome sequencing data: a comparative study

Next-generation sequencing has opened up new avenues for the genetic study of complex traits. However, because of the small number of observations for any given rare allele and high sequencing error, it is a challenge to identify functional rare variants associated with the phenotype of interest. Recent research shows that grouping variants by gene and incorporating computationally predicted functions of variants may provide higher statistical power. On the other hand, many algorithms are available for predicting the damaging effects of nonsynonymous variants. Here, we use the simulated mini-exome data of Genetic Analysis Workshop 17 to study and compare the effects of incorporating the functional predictions of single-nucleotide polymorphisms using two popular algorithms, SIFT and PolyPhen-2, into a gene-based association test. We also propose a simple mixture model that can effectively combine test results based on different functional prediction algorithms

Conformal Transformations in Cosmology of Modified Gravity: the Covariant Approach Perspective

The 1+3 covariant approach and the covariant gauge-invariant approach to perturbations are used to analyze in depth conformal transformations in cosmology. Such techniques allow us to obtain very interesting insights on the physical content of these transformations, when applied to non-standard gravity. The results obtained lead to a number of general conclusions on the change of some key quantities describing any two conformally related cosmological models. In particular, it is shown that the physics in the Einstein frame has characteristics which are completely different from those in the Jordan frame. Even if some of the geometrical properties of the cosmology are preserved (homogeneous and isotropic Universes are mapped into homogeneous and isotropic universes), it can happen that decelerating cosmologies are mapped into accelerated ones. Differences become even more pronounced when first-order perturbations are considered: from the 1+3 equations it is seen that first-order vector and tensor perturbations are left unchanged in their structure by the conformal transformation, but this cannot be said of the scalar perturbations, which include the matter density fluctuations. Behavior in the two frames of the growth rate, as well as other evolutionary features, like the presence or absence of oscillations, etc., appear to be different too. The results obtained are then explicitly interpreted and verified with the help of some clarifying examples based on $f(R)$-gravity cosmologies.Comment: 26 pages, 8 figure

Phantom crossing, equation-of-state singularities, and local gravity constraints in f(R) models

We identify the class of f(R) dark energy models which have a viable cosmology, i.e. a matter dominated epoch followed by a late-time acceleration. The deviation from a LambdaCDM model (f=R-Lambda) is quantified by the function m=Rf_{,RR}/f_{,R}. The matter epoch corresponds to m(r=-1) simeq +0 (where r=-Rf_{,R}/f) while the accelerated attractor exists in the region 0<m<1. We find that the equation of state w_DE of all such ``viable'' f(R) models exhibits two features: w_DE diverges at some redshift z_{c} and crosses the cosmological constant boundary (``phantom crossing'') at a redshift z_{b} smaller than z_{c}. Using the observational data of Supernova Ia and Cosmic Microwave Background, we obtain the constraint m<O(0.1) and we find that the phantom crossing could occur at z_{b}>1, i.e. within reach of observations. If we add local gravity constraints, the bound on m becomes very stringent, with m several orders of magnitude smaller than unity in the region whose density is much larger than the present cosmological density. The representative models that satisfy both cosmological and local gravity constraints take the asymptotic form m(r)=C(-r-1)^p with p>1 as r approaches -1.Comment: 8 pages, 3 figures, version to appear in Physics Letters

Conformal aspects of Palatini approach in Extended Theories of Gravity

The debate on the physical relevance of conformal transformations can be faced by taking the Palatini approach into account to gravitational theories. We show that conformal transformations are not only a mathematical tool to disentangle gravitational and matter degrees of freedom (passing from the Jordan frame to the Einstein frame) but they acquire a physical meaning considering the bi-metric structure of Palatini approach which allows to distinguish between spacetime structure and geodesic structure. Examples of higher-order and non-minimally coupled theories are worked out and relevant cosmological solutions in Einstein frame and Jordan frames are discussed showing that also the interpretation of cosmological observations can drastically change depending on the adopted frame

Periodogram Connectivity of EEG Signals for the Detection of Dyslexia

Electroencephalography (EEG) signals provide an important source of information of brain activity at different areas. This information can be used to diagnose brain disorders according to different activation patterns found in controls and patients. This acquisition technology can be also used to explore the neural basis of less evident learning disabilities such as Developmental Dyslexia (DD). DD is a specific difficulty in the acquisition of reading skills not related to mental age or inadequate schooling, whose prevalent is estimated between 5% and 12% of the population. In this paper we propose a method to extract discriminative features from EEG signals based on the relationship among the spectral density at each channel. This relationship is computed by means of different correlation measures, inferring connectivity-like markers that are eventually selected and classified by a linear support vector machine. The experiments performed shown AUC values up to 0.7, demonstrating the applicability of the proposed approach for objective DD diagnosis