Optimizing surveillance for livestock disease spreading through animal movements

The spatial propagation of many livestock infectious diseases critically depends on the animal movements among premises; so the knowledge of movement data may help us to detect, manage and control an outbreak. The identification of robust spreading features of the system is however hampered by the temporal dimension characterizing population interactions through movements. Traditional centrality measures do not provide relevant information as results strongly fluctuate in time and outbreak properties heavily depend on geotemporal initial conditions. By focusing on the case study of cattle displacements in Italy, we aim at characterizing livestock epidemics in terms of robust features useful for planning and control, to deal with temporal fluctuations, sensitivity to initial conditions and missing information during an outbreak. Through spatial disease simulations, we detect spreading paths that are stable across different initial conditions, allowing the clustering of the seeds and reducing the epidemic variability. Paths also allow us to identify premises, called sentinels, having a large probability of being infected and providing critical information on the outbreak origin, as encoded in the clusters. This novel procedure provides a general framework that can be applied to specific diseases, for aiding risk assessment analysis and informing the design of optimal surveillance systems.Comment: Supplementary Information at https://sites.google.com/site/paolobajardi/Home/archive/optimizing_surveillance_ESM_l.pdf?attredirects=

A new algorithm for point spread function subtraction in high-contrast imaging: a demonstration with angular differential imaging

Direct imaging of exoplanets is limited by bright quasi-static speckles in the point spread function (PSF) of the central star. This limitation can be reduced by subtraction of reference PSF images. We have developed an algorithm to construct an optimized reference PSF image from a set of reference images. This image is built as a linear combination of the reference images available and the coefficients of the combination are optimized inside multiple subsections of the image independently to minimize the residual noise within each subsection. The algorithm developed can be used with many high-contrast imaging observing strategies relying on PSF subtraction, such as angular differential imaging (ADI), roll subtraction, spectral differential imaging, reference star observations, etc. The performance of the algorithm is demonstrated for ADI data. It is shown that for this type of data the new algorithm provides a gain in sensitivity by up to a factor 3 at small separation over the algorithm used in Marois et al. (2006).Comment: 7 pages, 11 figures, to appear in May 10, 2007 issue of Ap

Detection of hidden structures on all scales in amorphous materials and complex physical systems: basic notions and applications to networks, lattice systems, and glasses

Recent decades have seen the discovery of numerous complex materials. At the root of the complexity underlying many of these materials lies a large number of possible contending atomic- and larger-scale configurations and the intricate correlations between their constituents. For a detailed understanding, there is a need for tools that enable the detection of pertinent structures on all spatial and temporal scales. Towards this end, we suggest a new method by invoking ideas from network analysis and information theory. Our method efficiently identifies basic unit cells and topological defects in systems with low disorder and may analyze general amorphous structures to identify candidate natural structures where a clear definition of order is lacking. This general unbiased detection of physical structure does not require a guess as to which of the system properties should be deemed as important and may constitute a natural point of departure for further analysis. The method applies to both static and dynamic systems.Comment: (23 pages, 9 figures

Fast, accurate and flexible data locality analysis

This paper presents a tool based on a new approach for analyzing the locality exhibited by data memory references. The tool is very fast because it is based on a static locality analysis enhanced with very simple profiling information, which results in a negligible slowdown. This feature allows the tool to be used for highly time-consuming applications and to include it as a step in a typical iterative analysis-optimization process. The tool can provide a detailed evaluation of the reuse exhibited by a program, quantifying and qualifying the different types of misses either globally or detailed by program sections, data structures, memory instructions, etc. The accuracy of the tool is validated by comparing its results with those provided by a simulator.Peer ReviewedPostprint (published version

Applying an abstract data structure description approach to parallelizing scientific pointer programs

Even though impressive progress has been made in the area of parallelizing scientific programs with arrays, the application of similar techniques to programs with pointer data structures has remained difficult. Unlike arrays which have a small number of well-defined properties that can be utilized by a parallelizing compiler, pointer data structures are used to implement a wide variety of structures that exhibit a much more diverse set of properties. The complexity and diversity of such properties means that, in general, scientific programs with pointer data structures cannot be effectively analyzed by an optimizing and parallelizing compiler.In order to provide a system in which the compiler can fully utilize the properties of different types of pointer data structures, we have developed a mechanism for the Abstract Description of Data Structures (ADDS). With our approach, the programmer can explicitly describe important properties such as dimensionality of the pointer data structure, independence of dimensions, and direction of traversal. These abstract descriptions of pointer data structures are then used by the compiler to guide analysis, optimization, and parallelization.In this paper we summarize the ADDS approach through the use of numerous examples of data structures used in scientific computations, we illustrate how such declarations are natural and non-tedious to specify, and we show how the ADDS declarations can be used to improve compile-time analysis. In order to demonstrate the viability of our approach, we show how such techniques can be used to parallelize an important class of scientific codes which naturally use recursive pointer data structures. In particular, we use our approach to develop the parallelization of an N-body simulation that is based on a relatively complicated pointer data structure, and we report the speedup results for a Sequent multiprocessor

How do Changes in Land Use Patterns Affect Species Diversity? an Approach for Optimizing Landscape Configuration

Heterogeneity of agricultural landscapes is supposed to be of significant importance for species diversity in agroecosystems (Weibull et al. 2003). Thus it is necessary to account for structural aspects of landscapes in land management decision processes. Spatial optimization models of land use can serve as tools for decision support. These models can aim at various landscape functions like nutrient leaching and economical aspects (Seppelt and Voinov 2002), water quality (Randhir et al. 2000) or habitat suitability (Nevo and Garcia 1996). However neighbourhood effects stay unconsidered in these approaches. In this paper we present an optimization model concept that aims at maximizing habitat suitability of selected species by identifying optimum spatial configurations of agricultural land use patterns. Bird species with diverging habitat requirements were chosen as target species. Habitat suitability models for these species are used to set up the performance criterion. Landscape structure is quantified by landscape metrics (McGarigal et al. 2002) estimated within the species home range. Statistical significance of these metrics for species presence was proven by a logistic regression model (Fielding and Haworth 1995). The landscape is represented by a grid based data set. Based on a genetic algorithm the optimization task is to identify an optimum configuration of model units. These model units are defined by contiguous cells of identical land use. Within this concept we can study how optimum but possibly artificial landscapes vary in structure depending on the selected species for which habitat suitability is maximized.