Verifying Class Invariants in Concurrent Programs

Class invariants are a highly useful feature for the verification of object-oriented programs, because they can be used to capture all valid object states. In a sequential program setting, the validity of class invariants is typically described in terms of a visible state semantics, i.e., invariants only have to hold whenever a method begins or ends execution, and they may be broken inside a method body. However, in a concurrent setting, this restriction is no longer usable, because due to thread interleavings, any program state is potentially a visible state. In this paper we present a new approach for reasoning about class invariants in multithreaded programs. We allow a thread to explicitly break an invariant at specific program locations, while ensuring that no other thread can observe the broken invariant. We develop our technique in a permission-based separation logic environment. However, we deviate from separation logic's standard rules and allow a class invariant to express properties over shared memory locations (the invariant footprint), independently of the permissions on these locations. In this way, a thread may break or reestablish an invariant without holding permissions to all locations in its footprint. To enable modular verification, we adopt the restrictions of Muller's ownership-based type system

Bayesian semiparametric multivariate stochastic volatility with application

In this article, we establish a Cholesky-type multivariate stochastic volatility estimation framework, in which we let the innovation vector follow a Dirichlet process mixture (DPM), thus enabling us to model highly flexible return distributions. The Cholesky decomposition allows parallel univariate process modeling and creates potential for estimating high-dimensional specifications. We use Markov chain Monte Carlo methods for posterior simulation and predictive density computation. We apply our framework to a five-dimensional stock-return data set and analyze international stockmarket co-movements among the largest stock markets. The empirical results show that our DPM modeling of the innovation vector yields substantial gains in out-of-sample density forecast accuracy when compared with the prevalent benchmark models

Polymorphism at High Molecular Weight Glutenin Subunits and Morphological Diversity of Aegilops geniculata Roth Collected in Algeria

A collection of 35 accessions of the tetraploid wild wheat Aegilops geniculata Roth (MM, UU) sampled in northern Algeria was evaluated for morphological and biochemical variability. Morphological and ecological analyses based on morphological traits and bioclimatic parameters, respectively, were assessed using principal component analysis (PCA). Accessions were differentiated by width characters, namely spike’s width, and a weak relationship between morphological traits and ecological parameters was found. Polymorphism of high molecular weight (HMW) glutenin subunits was carried on by sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE). Among accessions analyzed, 27 alleles were identified at the two loci Glu-M1 and Glu-U1: resulting in twenty-nine patterns and a nomenclature was proposed. Two alleles at the Glu-U1 locus expressed a new subunit with a slightly slower mobility than subunit 8. These results provide new information regarding the genetic variability of HMW glutenin subunits, as well as their usefulness in cultivated wheat quality improvement

Bayesian semiparametric multivariate stochastic volatility with application

In this article, we establish a Cholesky-type multivariate stochastic volatility estimation framework, in which we let the innovation vector follow a Dirichlet process mixture (DPM), thus enabling us to model highly flexible return distributions. The Cholesky decomposition allows parallel univariate process modeling and creates potential for estimating high-dimensional specifications. We use Markov chain Monte Carlo methods for posterior simulation and predictive density computation. We apply our framework to a five-dimensional stock-return data set and analyze international stock-market co-movements among the largest stock markets. The empirical results show that our DPM modeling of the innovation vector yields substantial gains in out-of-sample density forecast accuracy when compared with the prevalent benchmark models