Combining k-Induction with Continuously-Refined Invariants

Bounded model checking (BMC) is a well-known and successful technique for finding bugs in software. k-induction is an approach to extend BMC-based approaches from falsification to verification. Automatically generated auxiliary invariants can be used to strengthen the induction hypothesis. We improve this approach and further increase effectiveness and efficiency in the following way: we start with light-weight invariants and refine these invariants continuously during the analysis. We present and evaluate an implementation of our approach in the open-source verification-framework CPAchecker. Our experiments show that combining k-induction with continuously-refined invariants significantly increases effectiveness and efficiency, and outperforms all existing implementations of k-induction-based software verification in terms of successful verification results.Comment: 12 pages, 5 figures, 2 tables, 2 algorithm

Archaeology and autonomies: the legal framework of heritage management in a new Bolivia

The 2009 Bolivian Constitution significantly changed the structure of the state and paved the way for the creation of regional, local, and even indigenous autonomies. These autonomies are charged with the management of archaeological sites and museums within their territory. This article answers the question of who currently owns the Bolivian past, it stems from concerns raised at the 2011 renewal hearing of the Memorandum of Understanding preventing the import of illicit Bolivian antiquities into the United States. By combining an analysis of recent legal changes related to the creation of the autonomies and a short discussion of a notable case study of local management of a Bolivian archaeological site, this article offers a basic summary of the legal framework in which Bolivian archaeology and heritage management functions and some preliminary recommendations for governments and professionals wishing to work with Bolivian authorities at the state and local level

Benchmarking in cluster analysis: A white paper

To achieve scientific progress in terms of building a cumulative body of knowledge, careful attention to benchmarking is of the utmost importance. This means that proposals of new methods of data pre-processing, new data-analytic techniques, and new methods of output post-processing, should be extensively and carefully compared with existing alternatives, and that existing methods should be subjected to neutral comparison studies. To date, benchmarking and recommendations for benchmarking have been frequently seen in the context of supervised learning. Unfortunately, there has been a dearth of guidelines for benchmarking in an unsupervised setting, with the area of clustering as an important subdomain. To address this problem, discussion is given to the theoretical conceptual underpinnings of benchmarking in the field of cluster analysis by means of simulated as well as empirical data. Subsequently, the practicalities of how to address benchmarking questions in clustering are dealt with, and foundational recommendations are made

Large Time-Varying Parameter VARs

In this paper, we develop methods for estimation and forecasting in large time-varying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints, we draw on ideas from the dynamic model averaging literature which achieve reductions in the computational burden through the use forgetting factors. We then extend the TVP-VAR so that its dimension can change over time. For instance, we can have a large TVP-VAR as the forecasting model at some points in time, but a smaller TVP-VAR at others. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output and interest rates demonstrates the feasibility and usefulness of our approach

Arabidopsis TAO1 is a TIR-NB-LRR protein that contributes to disease resistance induced by the Pseudomonas syringae effector AvrB

The type III effector protein encoded by avirulence gene B (AvrB) is delivered into plant cells by pathogenic strains of Pseudomonas syringae. There, it localizes to the plasma membrane and triggers immunity mediated by the Arabidopsis coiled-coil (CC)-nucleotide binding (NB)-leucine-rich repeat (LRR) disease resistance protein RPM1. The sequence unrelated type III effector avirulence protein encoded by avirulence gene Rpm1 (AvrRpm1) also activates RPM1. AvrB contributes to virulence after delivery from P. syringae in leaves of susceptible soybean plants, and AvrRpm1 does the same in Arabidopsis rpm1 plants. Conditional overexpression of AvrB in rpm1 plants results in leaf chlorosis. In a genetic screen for mutants that lack AvrB-dependent chlorosis in an rpm1 background, we isolated TAO1 (target of AvrB operation), which encodes a Toll-IL-1 receptor (TIR)-NB-LRR disease resistance protein. In rpm1 plants, TAO1 function results in the expression of the pathogenesis-related protein 1 (PR-1) gene, suggestive of a defense response. In RPM1 plants, TAO1 contributes to disease resistance in response to Pto (P. syringae pathovars tomato) DC3000(avrB), but not against Pto DC3000(avrRpm1). The tao1–5 mutant allele, a stop mutation in the LRR domain of TAO1, posttranscriptionally suppresses RPM1 accumulation. These data provide evidence of genetically separable disease resistance responses to AvrB and AvrRpm1 in Arabidopsis. AvrB activates both RPM1, a CC-NB-LRR protein, and TAO1, a TIR-NB-LRR protein. These NB-LRR proteins then act additively to generate a full disease resistance response to P. syringae expressing this type III effector

Forecasting the European carbon market

In an effort to meet its obligations under the Kyoto Protocol, in 2005 the European Union introduced a cap-and-trade scheme where mandated installations are allocated permits to emit CO2. Financial markets have developed that allow companies to trade these carbon permits. For the EU to achieve reductions in CO2 emissions at a minimum cost, it is necessary that companies make appropriate investments and policymakers design optimal policies. In an effort to clarify the workings of the carbon market, several recent papers have attempted to statistically model it. However, the European carbon market (EU ETS) has many institutional features that potentially impact on daily carbon prices (and associated …nancial futures). As a consequence, the carbon market has properties that are quite di¤erent from conventional financial assets traded in mature markets. In this paper, we use dynamic model averaging (DMA) in order to forecast in this newly-developing market. DMA is a recently-developed statistical method which has three advantages over conventional approaches. First, it allows the coefficients on the predictors in a forecasting model to change over time. Second, it allows for the entire forecasting model to change over time. Third, it surmounts statistical problems which arise from the large number of potential predictors that can explain carbon prices. Our empirical results indicate that there are both important policy and statistical benefits with our approach. Statistically, we present strong evidence that there is substantial turbulence and change in the EU ETS market, and that DMA can model these features and forecast accurately compared to conventional approaches. From a policy perspective, we discuss the relative and changing role of different price drivers in the EU ETS. Finally, we document the forecast performance of DMA and discuss how this relates to the efficiency and maturity of this market

13C NMR study of the mode of interaction in solution of the B fragment of staphylococcal protein A and the Fc fragments of mouse immunoglobulin G

AbstractThe mode of interaction of the B domain (FB) of staphylococcal protein A and the Fc fragments of mouse immunoglobulin G (IgG) has been investigated by 13C NMR spectroscopy. Mouse IgG1, IgG2a, and IgG2b proteins have been selectively labeled with 13C at the carbonyl carbon of His, Met, Trp or Tyr residue and used to prepare the corresponding Fc fragments by limited proteolysis. Site-specific resonance assignments have been made for each of these Fc analogues. FB was reported to form two contacts (contact 1 and contact 2) with human Fc in the crystal [Biochemistry 20 (1981) 2361-2370]. Comparisons of the chemical shift data of the Fc fragments observed in the absence and presence of FB have led us to conclude that in solution contact 1 is responsible for the formation of the Fc-FB complexes

The molecular basis of host specialization in bean pathovars of Pseudomonas syringae

Biotrophic phytopathogens are typically limited to their adapted host range. In recent decades, investigations have teased apart the general molecular basis of intraspecific variation for innate immunity of plants, typically involving receptor proteins that enable perception of pathogen-associated molecular patterns or avirulence elicitors from the pathogen as triggers for defense induction. However, general consensus concerning evolutionary and molecular factors that alter host range across closely related phytopathogen isolates has been more elusive. Here, through genome comparisons and genetic manipulations, we investigate the underlying mechanisms that structure host range across closely related strains of Pseudomonas syringae isolated from different legume hosts. Although type III secretionindependent virulence factors are conserved across these three strains, we find that the presence of two genes encoding type III effectors (hopC1 and hopM1) and the absence of another (avrB2) potentially contribute to host range differences between pathovars glycinea and phaseolicola. These findings reinforce the idea that a complex genetic basis underlies host range evolution in plant pathogens. This complexity is present even in host–microbe interactions featuring relatively little divergence among both hosts and their adapted pathogens