Efficient model-checking of weighted CTL with upper-bound constraints

We present a symbolic extension of dependency graphs by Liu and Smolka in to model-check weighted Kripke structures against the computation tree logic with upper-bound weight constraints. Our extension introduces a new type of edges into dependency graphs and lifts the computation of fixed-points from boolean domain to nonnegative integers to cope with the weights. We present both global and local algorithms for the fixed-point computation on symbolic dependency graphs and argue for the advantages of our approach compared to the direct encoding of the model-checking problem into dependency graphs. We implement all algorithms in a publicly available tool and evaluate them on several experiments. The principal conclusion is that our local algorithm is the most efficient one with an order of magnitude improvement for model checking problems with a high number of “witnesses”.     

Analysis and evaluation of V*-kNN: an efficient algorithm for moving kNN queries

The moving k nearest neighbor (MkNN) query continuously finds the k nearest neighbors of a moving query point. MkNN queries can be efficiently processed through the use of safe regions. In general, a safe region is a region within which the query point can move without changing the query answer. This paper presents an incremental safe-region-based technique for answering MkNN queries, called the V*-Diagram, as well as analysis and evaluation of its associated algorithm, V*-kNN. Traditional safe-region approaches compute a safe region based on the data objects but independent of the query location. Our approach exploits the knowledge of the query location and the boundary of the search space in addition to the data objects. As a result, V*-kNN has much smaller I/O and computation costs than existing methods. We further provide cost models to estimate the number of data accesses for V*-kNN and a competitive technique, RIS-kNN. The V*-Diagram and V*-kNN are also applicable to the domain of spatial networks and we present algorithms to construct a spatial-network V*-Diagram. Our experimental results show that V*-kNN significantly outperforms the competitive technique. The results also verify the accuracy of the cost models.     

MS analysis of rheumatoid arthritic synovial tissue identifies specific citrullination sites on fibrinogen

Purpose : Citrullination is a post‐translational modification of arginine residues to citrulline catalyzed by peptidyl arginine deiminases. Induced expression of citrullinated proteins are frequently detected in various inflammatory states including arthritis; however, direct detection of citrullination in arthritic samples has not been successfully performed in the past. Experimental design : Citrullination of human fibrinogen, a candidate autoantigen in arthritis, was studied. Accurate identification of citrullinated fibrinogen peptides from rheumatoid arthritis synovial tissue specimens was performed using accurate mass and retention time analysis. Results : A peptide with the sequence ESSSHHPGIAEFPSRGK corresponding to amino acids 559–575 of fibrinogen α‐chain was identified to be citrullinated with an occupancy rate between 1.4 and 2.5%. Citrullination of the peptide KREEAPSLRPAPPPISGGGYRARPAK corresponding to amino acids 52–77 of the fibrinogen β‐chain was identified with an occupancy rate of 1.2%. Conclusions and clinical relevance : We report a proof of principle study for the identification of citrullinated proteins and within them, identification of citrullination sites and quantification of their occupancies in synovial tissue from rheumatoid arthritis patients using high‐resolution MS. Detailed studies on which molecules are citrullinated in arthritis can provide information about their role in immune regulation and serve as novel biomarkers and potentially even as therapeutic targets.     

The maximum gain of increasing the number of preemptions in multiprocessor scheduling

We consider the optimal makespan C(P, m, i) of an arbitrary set P of independent jobs scheduled with i preemptions on a multiprocessor with m identical processors. We compare the ratio for such makespans for i and j preemptions, respectively, where i < j. This ratio depends on P, but we are interested in the P that maximizes this ratio, i.e. we calculate a formula for the worst case ratio G(m, i, j) defined as G(m,i,j)=max\fracC(P,m,i)C(P,m,j),{G(m,i,j)=\max \frac{C(P,m,i)}{C(P,m,j)},} where the maximum is taken over all sets P of independent jobs.     

Self-Densification of Highly Mesoporous Wood Structure into a Strong and Transparent Film

In the native wood cell wall, cellulose microfibrils are highly aligned and organized in the secondary cell wall. A new preparation strategy is developed to achieve individualization of cellulose microfibrils within the wood cell wall structure without introducing mechanical disintegration. The resulting mesoporous wood structure has a high specific surface area of 197 m² g⁻¹ when prepared by freeze-drying using liquid nitrogen, and 249 m² g⁻¹ by supercritical drying. These values are 5 to 7 times higher than conventional delignified wood (36 m² g⁻¹) dried by supercritical drying. Such highly mesoporous structure with individualized cellulose microfibrils maintaining their natural alignment and organization can be processed into aerogels with high porosity and high compressive strength. In addition, a strong film with a tensile strength of 449.1 ± 21.8 MPa and a Young's modulus of 51.1 ± 5.2 GPa along the fiber direction is obtained simply by air drying owing to the self-densification of cellulose microfibrils driven by the elastocapillary forces upon water evaporation. The self-densified film also shows high optical transmittance (80%) and high optical haze (70%) with interesting biaxial light scattering behavior owing to the natural alignment of cellulose microfibrils.     

Defining the abstract syntax of visual languages with advanced graph grammars—A case study based on behavior trees

Diagrammatic visual languages can increase the ability of engineers to model and understand complex systems. However, to effectively use visual models, the syntax and semantics of these languages should be defined precisely. Since most diagrammatic visual models that are currently used to specify systems can be described as (directed) typed graphs, graph grammars have been identified as a suitable formalism to describe the abstract syntax of visual modeling languages. In this article, we investigate how advanced graph-transformation techniques, such as conditional, structure-generic and type-generic graph-transformation rules, can help to improve and simplify the specification of the abstract syntax of a visual modeling language. To demonstrate the practicability of an approach that unifies these advanced graph-transformation techniques, we define the abstract syntax of behavior trees (BTs), a graphical specification language for functional requirements. Additionally, we provide a translational semantics of BTs by formalizing a translation scheme to the input language of the SAL model checking tool for each of the graph-transformation rules.     

Glyph-based SPECT visualization for the diagnosis of coronary artery disease

Myocardial perfusion imaging with single photon emission computed tomography (SPECT) is an established method for the detection and evaluation of coronary artery disease (CAD). State-of-the-art SPECT scanners yield a large number of regional parameters of the left-ventricular myocardium (e.g., blood supply at rest and during stress, wall thickness, and wall thickening during heart contraction) that all need to be assessed by the physician. Today, the individual parameters of this multivariate data set are displayed as stacks of 2D slices, bull's eye plots, or, more recently, surfaces in 3D, which depict the left-ventricular wall. In all these visualizations, the data sets are displayed side-by-side rather than in an integrated manner, such that the multivariate data have to be examined sequentially and need to be fused mentally. This is time consuming and error-prone. In this paper we present an interactive 3D glyph visualization, which enables an effective integrated visualization of the multivariate data. Results from semiotic theory are used to optimize the mapping of different variables to glyph properties. This facilitates an improved perception of important information and thus an accelerated diagnosis. The 3D glyphs are linked to the established 2D views, which permit a more detailed inspection, and to relevant meta-information such as known stenoses of coronary vessels supplying the myocardial region. Our method has demonstrated its potential for clinical routine use in real application scenarios assessed by nuclear physicians.     

Surface extraction from multi-field particle volume data using multi-dimensional cluster visualization

Data sets resulting from physical simulations typically contain a multitude of physical variables. It is, therefore, desirable that visualization methods take into account the entire multi-field volume data rather than concentrating on one variable. We present a visualization approach based on surface extraction from multi-field particle volume data. The surfaces segment the data with respect to the underlying multi-variate function. Decisions on segmentation properties are based on the analysis of the multi-dimensional feature space. The feature space exploration is performed by an automated multi-dimensional hierarchical clustering method, whose resulting density clusters are shown in the form of density level sets in a 3D star coordinate layout. In the star coordinate layout, the user can select clusters of interest. A selected cluster in feature space corresponds to a segmenting surface in object space. Based on the segmentation property induced by the cluster membership, we extract a surface from the volume data. Our driving applications are Smoothed Particle Hydrodynamics (SPH) simulations, where each particle carries multiple properties. The data sets are given in the form of unstructured point-based volume data. We directly extract our surfaces from such data without prior resampling or grid generation. The surface extraction computes individual points on the surface, which is supported by an efficient neighborhood computation. The extracted surface points are rendered using point-based rendering operations. Our approach combines methods in scientific visualization for object-space operations with methods in information visualization for feature-space operations.     

Smooth surface extraction from unstructured point-based volume data using PDEs

Smooth surface extraction using partial differential equations (PDEs) is a well-known and widely used technique for visualizing volume data. Existing approaches operate on gridded data and mainly on regular structured grids. When considering unstructured point-based volume data where sample points do not form regular patterns nor are they connected in any form, one would typically resample the data over a grid prior to applying the known PDE-based methods. We propose an approach that directly extracts smooth surfaces from unstructured point-based volume data without prior resampling or mesh generation. When operating on unstructured data one needs to quickly derive neighborhood information. The respective information is retrieved by partitioning the 3D domain into cells using a kd-tree and operating on its cells. We exploit neighborhood information to estimate gradients and mean curvature at every sample point using a four-dimensional least-squares fitting approach. Gradients and mean curvature are required for applying the chosen PDE-based method that combines hyperbolic advection to an isovalue of a given scalar field and mean curvature flow. Since we are using an explicit time-integration scheme, time steps and neighbor locations are bounded to ensure convergence of the process. To avoid small global time steps, we use asynchronous local integration. We extract the surface by successively fitting a smooth auxiliary function to the data set. This auxiliary function is initialized as a signed distance function. For each sample and for every time step we compute the respective gradient, the mean curvature, and a stable time step. With these informations the auxiliary function is manipulated using an explicit Euler time integration. The process successively continues with the next sample point in time. If the norm of the auxiliary function gradient in a sample exceeds a given threshold at some time, the auxiliary function is reinitialized to a signed distance function. After convergence of the evolution, the resulting smooth surface is obtained by extracting the zero isosurface from the auxiliary function using direct isosurface extraction from unstructured point-based volume data and rendering the extracted surface using point-based rendering methods.     

Model-based development of a course of action scheduling tool

This paper shows how a formal method in the form of Coloured Petri Nets (CPNs) and the supporting CPN Tools have been used in the development of the Course of Action Scheduling Tool (COAST). The aim of COAST is to support human planners in the specification and scheduling of tasks in a Course of Action. CPNs have been used to develop a formal model of the task execution framework underlying COAST. The CPN model has been extracted in executable form from CPN Tools and embedded directly into COAST, thereby automatically bridging the gap between the formal specification and its implementation. The scheduling capabilities of COAST are based on state space exploration of the embedded CPN model. Planners interact with COAST using a domain-specific graphical user interface (GUI) that hides the embedded CPN model and analysis algorithms. This means that COAST is based on a rigorous semantical model, but the use of formal methods is transparent to the users. Trials of operational planning using COAST have been conducted within the Australian Defence Force. The work of Lin Zhang was done while he was with the Command and Control Division of the Australian Defence Science and Technology Organisation.