Reliable scientific service compositions

Abstract. Distributed service oriented architectures (SOAs) are increas-ingly used by users, who are insufficiently skilled in the art of distributed system programming. A good example are computational scientists who build large-scale distributed systems using service-oriented Grid comput-ing infrastructures. Computational scientists use these infrastructure to build scientific applications, which are composed from basic Web ser-vices into larger orchestrations using workflow languages, such as the Business Process Execution Language. For these users reliability of the infrastructure is of significant importance and that has to be provided in the presence of hardware or operational failures. The primitives avail-able to achieve such reliability currently leave much to be desired by users who do not necessarily have a strong education in distributed sys-tem construction. We characterise scientific service compositions and the environment they operate in by introducing the notion of global scien-tific BPEL workflows. We outline the threats to the reliability of such workflows and discuss the limited support that available specifications and mechanisms provide to achieve reliability. Furthermore, we propose a line of research to address the identified issues by investigating auto-nomic mechanisms that assist computational scientists in building, exe-cuting and maintaining reliable workflows.

Grid service orchestration using the Business Process Execution Language (BPEL)

Modern scientific applications often need to be distributed across grids. Increasingly applications rely on services, such as job submission, data transfer or data portal services. We refer to such services as grid services. While the invocation of grid services could be hard coded in theory, scientific users want to orchestrate service invocations more flexibly. In enterprise applications, the orchestration of web services is achieved using emerging orchestration standards, most notably the Business Process Execution Language (BPEL). We describe our experience in orchestrating scientific workflows using BPEL. We have gained this experience during an extensive case study that orchestrates grid services for the automation of a polymorph prediction application

Canalizing Kauffman networks: non-ergodicity and its effect on their critical behavior

Boolean Networks have been used to study numerous phenomena, including gene regulation, neural networks, social interactions, and biological evolution. Here, we propose a general method for determining the critical behavior of Boolean systems built from arbitrary ensembles of Boolean functions. In particular, we solve the critical condition for systems of units operating according to canalizing functions and present strong numerical evidence that our approach correctly predicts the phase transition from order to chaos in such systems.Comment: to be published in PR

Structural motifs of pre-nucleation clusters

Structural motifs of pre-nucleation clusters prepared in single, optically levitated supersaturated aqueous aerosol microparticles containing CaBr2 as a model system are reported. Cluster formation is identified by means of X-ray absorption in the Br K-edge regime. The salt concentration beyond the saturation point is varied by controlling the humidity in the ambient atmosphere surrounding the 15–30 μm microdroplets. This leads to the formation of metastable supersaturated liquid particles. Distinct spectral shifts in near-edge spectra as a function of salt concentration are observed, in which the energy position of the Br K-edge is red-shifted by up to 7.1 ± 0.4 eV if the dilute solution is compared to the solid. The K-edge positions of supersaturated solutions are found between these limits. The changes in electronic structure are rationalized in terms of the formation of pre- nucleation clusters. This assumption is verified by spectral simulations using first-principle density functional theory and molecular dynamics calculations, in which structural motifs are considered, explaining the experimental results. These consist of solvated CaBr2 moieties, rather than building blocks forming calcium bromide hexahydrates, the crystal system that is formed by drying aqueous CaBr2 solutions

A Simple Separable Exact C*-Algebra not Anti-isomorphic to Itself

We give an example of an exact, stably finite, simple. separable C*-algebra D which is not isomorphic to its opposite algebra. Moreover, D has the following additional properties. It is stably finite, approximately divisible, has real rank zero and stable rank one, has a unique tracial state, and the order on projections over D is determined by traces. It also absorbs the Jiang-Su algebra Z, and in fact absorbs the 3^{\infty} UHF algebra. We can also explicitly compute the K-theory of D, namely K_0 (D) = Z[1/3] with the standard order, and K_1 (D) = 0, as well as the Cuntz semigroup of D.Comment: 16 pages; AMSLaTeX. The material on other possible K-groups for such an algebra has been moved to a separate paper (1309.4142 [math.OA]

Uso da farinha de minhoca como alimento para pós-larvas de tilápia.

Foi avaliada a influência da substituição da farinha de peixe pela farinha de minhoca (Eisenia foetida) no crescimento de pós-larvas de tilápia nilótica (Oreochromis niloticus). A farinha de peixe, que correspondeu a 50% da proteína da dieta, foi substituída pela farinha de minhoca nos seguintes níveis: 0%, 20%, 40%, 60%, 80% e 100%. Os peixes foram alimentados à vontade, quatro vezes ao dia, sendo pesados e medidos aos 21 e 41 dias de experimentação. O delineamento experimental foi o completamente casualizado, com quatro repetições por tratamento e 20 peixes por unidade experimental. Os dados coletados foram analisados pela ANOVA, sendo as médias posteriormente classificadas pelo teste de Tukey (5%). Após 21 dias, não houve diferença significativa entre os tratamentos. Entretanto, aos 41 dias houve diferença significativa entre os tratamentos e os animais com o nível de substituição de 20% apresentaram os maiores pesos e taxas de crescimento específico, e os animais com o nível de substituição de 100% os menores. Durante o período experimental não houve diferença significativa entre os tratamentos em relação à sobrevivência dos animais. Os resultados mostram que baixos níveis de substituição da farinha de peixe (20%) melhoram o crescimento dos animais e que a substituição total da farinha de peixe pela farinha de minhoca é prejudicial ao desenvolvimento dos peixes, mas não afeta a sua sobrevivência.bitstream/item/37405/1/BP45.pd

Estimation of Brain Network Atlases using Diffusive-Shrinking Graphs:Application to Developing Brains

Many methods have been developed to spatially normalize a population of brain images for estimating a mean image as a populationaverage atlas. However, methods for deriving a network atlas from a set of brain networks sitting on a complex manifold are still absent. Learning how to average brain networks across subjects constitutes a key step in creating a reliable mean representation of a population of brain networks, which can be used to spot abnormal deviations from the healthy network atlas. In this work, we propose a novel network atlas estimation framework, which guarantees that the produced network atlas is clean (for tuning down noisy measurements) and well-centered (for being optimally close to all subjects and representing the individual traits of each subject in the population). Specifically, for a population of brain networks, we first build a tensor, where each of its frontal-views (i.e., frontal matrices) represents a connectivity network matrix of a single subject in the population. Then, we use tensor robust principal component analysis for jointly denoising all subjects’ networks through cleaving a sparse noisy network population tensor from a clean low-rank network tensor. Second, we build a graph where each node represents a frontal-view of the unfolded clean tensor (network), to leverage the local manifold structure of these networks when fusing them. Specifically, we progressively shrink the graph of networks towards the centered mean network atlas through non-linear diffusion along the local neighbors of each of its nodes. Our evaluation on the developing functional and morphological brain networks at 1, 3, 6, 9 and 12 months of age has showed a better centeredness of our network atlases, in comparison with the baseline network fusion method. Further cleaning of the population of networks produces even more centered atlases, especially for the noisy functional connectivity networks

Tightness of slip-linked polymer chains

We study the interplay between entropy and topological constraints for a polymer chain in which sliding rings (slip-links) enforce pair contacts between monomers. These slip-links divide a closed ring polymer into a number of sub-loops which can exchange length between each other. In the ideal chain limit, we find the joint probability density function for the sizes of segments within such a slip-linked polymer chain (paraknot). A particular segment is tight (small in size) or loose (of the order of the overall size of the paraknot) depending on both the number of slip-links it incorporates and its competition with other segments. When self-avoiding interactions are included, scaling arguments can be used to predict the statistics of segment sizes for certain paraknot configurations.Comment: 10 pages, 6 figures, REVTeX

Bovine oocytes in secondary follicles grow and acquire meiotic competence in severe combined immunodeficient mice

A rigorous methodology is developed that addresses numerical and statistical issues when developing group contribution (GC) based property models such as regression methods, optimization algorithms, performance statistics, outlier treatment, parameter identifiability, and uncertainty of the prediction. The methodology is evaluated through development of a GC method for the prediction of the heat of combustion (Δ<i>H</i>_c^o) for pure components. The results showed that robust regression lead to best performance statistics for parameter estimation. The bootstrap method is found to be a valid alternative to calculate parameter estimation errors when underlying distribution of residuals is unknown. Many parameters (first, second, third order group contributions) are found unidentifiable from the typically available data, with large estimation error bounds and significant correlation. Due to this poor parameter identifiability issues, reporting of the 95% confidence intervals of the predicted property values should be mandatory as opposed to reporting only single value prediction, currently the norm in literature. Moreover, inclusion of higher order groups (additional parameters) does not always lead to improved prediction accuracy for the GC-models; in some cases, it may even increase the prediction error (hence worse prediction accuracy). However, additional parameters do not affect calculated 95% confidence interval. Last but not least, the newly developed GC model of the heat of combustion (Δ<i>H</i>_c^o) shows predictions of great accuracy and quality (the most data falling within the 95% confidence intervals) and provides additional information on the uncertainty of each prediction compared to other Δ<i>H</i>_c^o models reported in literature