Parameterized Construction of Program Representations for Sparse Dataflow Analyses

Data-flow analyses usually associate information with control flow regions. Informally, if these regions are too small, like a point between two consecutive statements, we call the analysis dense. On the other hand, if these regions include many such points, then we call it sparse. This paper presents a systematic method to build program representations that support sparse analyses. To pave the way to this framework we clarify the bibliography about well-known intermediate program representations. We show that our approach, up to parameter choice, subsumes many of these representations, such as the SSA, SSI and e-SSA forms. In particular, our algorithms are faster, simpler and more frugal than the previous techniques used to construct SSI - Static Single Information - form programs. We produce intermediate representations isomorphic to Choi et al.'s Sparse Evaluation Graphs (SEG) for the family of data-flow problems that can be partitioned per variables. However, contrary to SEGs, we can handle - sparsely - problems that are not in this family

Regional Log Market Integration in New Zealand

In this paper the integration of log prices across four regions in New Zealand was assessed. A time series of prices for six Radiata Pine (Pinus radiata D. Don) log grades in each of the regions were tested for co-integration using Johansen’s method and Engle-Granger pair wise tests. Prices for export grades display significant integration across regions and generally follow the law of one price. However, markets for domestic grades tend to be regionally segregated. These results are most likely due to the high costs of transporting logs between regions. Future modelling will need to incorporate such transportation costs in order to adequately characterise log markets in the country.log market; co-integration; law of one price

Ambient vibration re-testing and operational modal analysis of the Humber Bridge

An ambient vibration survey of the Humber Bridge was carried out in July 2008 by a combined team from the UK, Portugal and Hong Kong. The exercise had several purposes that included the evaluation of the current technology for instrumentation and system identification and the generation of an experimental dataset of modal properties to be used for validation and updating of finite element models for scenario simulation and structural health monitoring. The exercise was conducted as part of a project aimed at developing online diagnosis capabilities for three landmark European suspension bridges. Ten stand-alone tri-axial acceleration recorders were deployed at locations along all three spans and in all four pylons during five days of consecutive one-hour recordings. Time series segments from the recorders were merged, and several operational modal analysis techniques were used to analyse these data and assemble modal models representing the global behaviour of the bridge in all three dimensions for all components of the structure. The paper describes the equipment and procedures used for the exercise, compares the operational modal analysis (OMA) technology used for system identification and presents modal parameters for key vibration modes of the complete structure. The results obtained using three techniques, natural excitation technique/eigensystem realisation algorithm, stochastic subspace identification and poly-Least Squares Frequency Domain method, are compared among themselves and with those obtained from a 1985 test of the bridge, showing few significant modal parameter changes over 23 years in cases where direct comparison is possible. The measurement system and the much more sophisticated OMA technology used in the present test show clear advantages necessary due to the compressed timescales compared to the earlier exercise. Even so, the parameter estimates exhibit significant variability between different methods and variations of the same method, while also varying in time and having inherent variability. (C) 2010 Elsevier Ltd. All rights reserved

SSI Revisited

The static single information (SSI) form, proposed by Ananian, then in a more general form by Singer, is an extension of the static single assignment (SSA) form. The latter is a well-established compiler intermediate representation that has been successfully used for numerous compiler analysis and optimizations. Several interesting results have also been shown for SSI concerning liveness analysis and representation of live-ranges of variables, which could make SSI appealing for just-in-time compilation. Unfortunately, previous literature on the SSI form is sparse and appears to be partly incorrect. Our paper corrects some of the mistakes that have been made. Our main result is a complete proof that, even for the most general definition of SSI, basic blocks, and thus program points, can be totally ordered so that live-ranges of variables correspond to intervals. This corrects the erroneous proof of Brisk and Sarrafzadeh

Higher-Dimensional Timed Automata

We introduce a new formalism of higher-dimensional timed automata, based on van Glabbeek's higher-dimensional automata and Alur's timed automata. We prove that their reachability is PSPACE-complete and can be decided using zone-based algorithms. We also show how to use tensor products to combat state-space explosion and how to extend the setting to higher-dimensional hybrid automata

Segregation in Networks

Schelling (1969, 1971, 1971, 1978) considered a simple model with individual agents who only care about the types of people living in their own local neighborhood. The spatial structure was represented by a one- or two-dimensional lattice. Schelling showed that an integrated society will generally unravel into a rather segregated one even though no individual agent strictly prefers this. We make a first step to generalize the spatial proximity model to a proximity model of segregation. That is, we examine models with individual agents who interact 'locally' in a range of network structures with topological properties that are different from those of regular lattices. Assuming mild preferences about with whom they interact, we study best-response dynamics in random and regular non-directed graphs as well as in small-world and scale-free networks. Our main result is that the system attains levels of segregation that are in line with those reached in the lattice-based spatial proximity model. In other words, mild proximity preferences can explain segregation not just in regular spatial networks but also in more general social networks. Furthermore, segregation levels do not dramatically vary across different network structures. That is, Schelling's original results seem to be robust also to the structural properties of the network.Spatial proximity model, Social segregation, Schelling, Proximity preferences, Social networks, Undirected graphs, Best-response dynamics.

Segregation in Networks.

Schelling (1969, 1971a,b, 1978) considered a simple model with individual agents who only care about the types of people living in their own local neighborhood. The spatial structure was represented by a one- or two-dimensional lattice. Schelling showed that an integrated society will generally unravel into a rather segregated one even though no individual agent strictly prefers this. We make a first step to generalize the spatial proximity model to a proximity model of segregation. That is, we examine models with individual agents who interact ’locally’ in a range of network structures with topological properties that are different from those of regular lattices. Assuming mild preferences about with whom they interact, we study best-response dynamics in random and regular non-directed graphs as well as in small-world and scale-free networks. Our main result is that the system attains levels of segregation that are in line with those reached in the lattice-based spatial proximity model. In other words, mild proximity preferences can explain segregation not just in regular spatial networks but also in more general social networks. Furthermore, segregation levels do not dramatically vary across different network structures. That is, Schelling’s original results seem to be robust also to the structural properties of the network.Spatial proximity model, Social segregation, Schelling, Proximity preferences, Social networks, Undirected graphs, Best-response dynamics.