Networking Foundations for Collaborative Computing at Internet Scope

This paper presents a new distributed computing framework for Many Task Computing (MTC) applications, based on the Extensible Messaging and Presence Protocol (XMPP). A lightweight, highly available system, named Kestrel, has been developed to explore XMPP-based techniques for improving MTC system tolerance to faults that result from scaling and intermittent computing agent presence. By leveraging technologies used in large instant messaging systems that scale to millions of clients, this MTC system is designed to scale to millions of agents at various levels of granularity: cores, machines, clusters, and even sensors, which makes it a good fit for MTC.Kestrel's architecture is inspired by the distributed design of pilot job frameworks on the grid as well as botnets, with the addition of a commodity instant messaging protocol for communications. Whereas botnet command-and-control systems have frequently used a combination of Internet Relay Chat (IRC), Distributed Hash Table (DHT), and other Peer-to-Peer (P2P) technologies, Kestrel utilizes XMPP for its presence notification capabilities, which allow the system to maintain continuous tracking of machine presence and state in real time. XMPP is also easily extensible with application-specific subprotocols, which can be utilized to transfer machine profile descriptions and job requirements. These sub-protocols can be used to implement distributed matching of jobs to systems, using a mechanism similar to ClassAds in the Condor High Throughput Computing (HTC) system.

The exploding number of computational models produced by Systems Biologists over the last years is an invitation to structure and exploit this new wealth of information. Researchers would like to trace models relevant to specific scientific questions, to explore their biological content, to align and combine them, and to match them with experimental data. To automate these processes, it is essential to consider semantic annotations, which describe their biological meaning. As a prerequisite for a wide range of computational methods, we propose general and flexible similarity measures for Systems Biology models computed from semantic annotations. By using these measures and a large extensible ontology, we implement a platform that can retrieve, cluster, and align Systems Biology models and experimental data sets. At present, its major application is the search for relevant models in the BioModels Database, starting from initial models, data sets, or lists of biological concepts. Beyond similarity searches, the representation of models by semantic feature vectors may pave the way for visualisation, exploration, and statistical analysis of large collections of models and corresponding data.

In this article, we investigate the role of gender in collaboration patterns by analyzing gender-based homophily-the tendency for researchers to co-author with individuals of the same gender. We develop and apply novel methodology to the corpus of JSTOR articles, a broad scholarly landscape, which we analyze at various levels of granularity. Most notably, for a precise analysis of gender homophily, we develop methodology which explicitly accounts for the fact that the data comprises heterogeneous intellectual communities and that not all authorships are exchangeable. In particular, we distinguish three phenomena which may affect the distribution of observed gender homophily in collaborations: a structural component that is due to demographics and non-gendered authorship norms of a scholarly community, a compositional component which is driven by varying gender representation across sub-disciplines and time, and a behavioral component which we define as the remainder of observed gender homophily after its structural and compositional components have been taken into account. Using minimal modeling assumptions, the methodology we develop allows us to test for behavioral homophily. We find that statistically significant behavioral homophily can be detected across the JSTOR corpus and show that this finding is robust to missing gender indicators in our data. In a secondary analysis, we show that the proportion of women representation in a field is positively associated with the probability of finding statistically significant behavioral homophily.

In today’s business environment, applications generate massive amounts of business data at various levels of granularity. During execution of business processes, a number of issues may occur, e.g., system failures, process failures, service failures, or human errors, that can result in the processes not executing as expected, and as a result not adhering to the required compliance concerns. Business provenance is an emerging concept which gives the flexibility to capture information required to address a specific compliance or performance goal. This paper discusses the importance of data provenance and presents a framework to capture, model, and persists provenance for business events data. We propose a method to model the business events in such a way that can be used for continuous compliance monitoring and for historical root cause analysis. We present a design of our proposed framework and its components along with a prototype implementation.

As technology scales, interconnects dominate the performance and power behavior of deep submicron designs. Three-dimensional integrated circuits (3D ICs) have been proposed as a way to mitigate the interconnect challenges. In this paper, we explore the architectural design of cache memories using 3D circuits. We present a delay and energy model, 3DCacti, to explore different 3D design options of partitioning a cache. The tool allows partitioning of the cache across different device layers at various levels of granularity. The tool has been validated by comparing its results with those obtained from circuit simulation of custom 3D layouts. We also explore the effects of various cache partitioning parameters and 3D technology parameters on delay and energy to demonstrate the utility of the tool.

BackgroundGiven the rapid growth of translational research and personalized healthcare paradigms, the ability to relate and reason upon networks of bio-molecular and phenotypic variables at various levels of granularity in order to diagnose, stage and plan treatments for disease states is highly desirable. Numerous techniques exist that can be used to develop networks of co-expressed or otherwise related genes and clinical features. Such techniques can also be used to create formalized knowledge collections based upon the information incumbent to ontologies and domain literature. However, reports of integrative approaches that bridge such networks to create systems-level models of disease or wellness are notably lacking in the contemporary literature.ResultsIn response to the preceding gap in knowledge and practice, we report upon a prototypical series of experiments that utilize multi-modal approaches to network induction. These experiments are intended to elicit meaningful and significant biomarker-phenotype complexes spanning multiple levels of granularity. This work has been performed in the experimental context of a large-scale clinical and basic science data repository maintained by the National Cancer Institute (NCI) funded Chronic Lymphocytic Leukemia Research Consortium.ConclusionsOur results indicate that it is computationally tractable to link orthogonal networks of genes, clinical features, and conceptual knowledge to create multi-dimensional models of interrelated biomarkers and phenotypes. Further, our results indicate that such systems-level models contain interrelated bio-molecular and clinical markers capable of supporting hypothesis discovery and testing. Based on such findings, we propose a conceptual model intended to inform the cross-linkage of the results of such methods. This model has as its aim the identification of novel and knowledge-anchored biomarker-phenotype complexes.

BackgroundNext generation sequencing technologies have greatly advanced many research areas of the biomedical sciences through their capability to generate massive amounts of genetic information at unprecedented rates. The advent of next generation sequencing has led to the development of numerous computational tools to analyze and assemble the millions to billions of short sequencing reads produced by these technologies. While these tools filled an important gap, current approaches for storing, processing, and analyzing short read datasets generally have remained simple and lack the complexity needed to efficiently model the produced reads and assemble them correctly.ResultsPreviously, we presented an overlap graph coarsening scheme for modeling read overlap relationships on multiple levels. Most current read assembly and analysis approaches use a single graph or set of clusters to represent the relationships among a read dataset. Instead, we use a series of graphs to represent the reads and their overlap relationships across a spectrum of information granularity. At each information level our algorithm is capable of generating clusters of reads from the reduced graph, forming an integrated graph modeling and clustering approach for read analysis and assembly. Previously we applied our algorithm to simulated and real 454 datasets to assess its ability to efficiently model and cluster next generation sequencing data. In this paper we extend our algorithm to large simulated and real Illumina datasets to demonstrate that our algorithm is practical for both sequencing technologies.ConclusionsOur overlap graph theoretic algorithm is able to model next generation sequencing reads at various levels of granularity through the process of graph coarsening. Additionally, our model allows for efficient representation of the read overlap relationships, is scalable for large datasets, and is practical for both Illumina and 454 sequencing technologies.

Developing Industry 4.0 applications is a complex endeavor, as it requires adjusting several relevant perspectives and items throughout design. RAMI4.0 has been introduced to facilitate Industry 4.0 developments. It puts items from three different development dimensions in mutual context. Some items are standardized due to existing technical standards, business-oriented ones depend on the development case at hand. Case-sensitive adjustment includes resolving inconsistencies and overcoming coherence deficits. We propose scaffolding to structure and thus, facilitate design of Industry 4.0 applications based on RAMI4.0. It visualizes static and dynamic development entities for guiding the connection of RAMI4.0 elements on various levels of granularity. We exemplify our approach using a common Industry 4.0 demonstrator, MyJoghurt. It provides evidence of supportive guidance for developers when exploring RAMI4.0 through engaging scaffolds.

Response selection from unstructured documents for human-computer conversation systems

Many tasks (e.g., search and summarization) result in an ordered list of items. In order to evaluate such an ordered list of items, we need to compare it with an ideal ordered list created by a human expert for the same set of items. To reduce any bias, multiple human experts are often used to create multiple ideal ordered lists. An interesting challenge in such an evaluation method is thus how to aggregate these different ideal lists to compute a single score for an ordered list to be evaluated. In this paper, we propose three new methods for aggregating multiple order judgments to evaluate ordered lists: weighted correlation aggregation, rank-based aggregation, and frequent sequential pattern-based aggregation. Experiment results on ordering sentences for text summarization show that all the three new methods outperform the state of the art average correlation methods in terms of discriminativeness and robustness against noise. Among the three proposed methods, the frequent sequential pattern-based method performs the best due to the flexible modeling of agreements and disagreements among human experts at various levels of granularity.

The general objective of this work is to develop a description language for constructive 3D boundary representation [5] of neuroanatomical structures and connectivity at various levels of granularity (from coarse-resolution solids to fine meshes). This approach is motivated by a desire to capture regularities in neural circuitry as revealed by neuro-architectonic studies [4], while at the same time to explore hypotheses about anatomic variability of various origins, such as experimental uncertainties, cross species scaling factors, individual differences, and assess them for their impact on connectivity, and ultimately on network dynamics. Boundary representation is a general approach to describe 3D objects solely by their surface. Boundary representations consist of topological objects and their concrete geometrical representations in terms of enclosing boundaries. Topological elements include vertices, edges and faces, with corresponding geometrical elements being points, curves, and surfaces. The relationships between topological elements in a structure are expressed by means of a graph of topological connections. The description language is being implemented on top of the GNU Triangulated Surface library [3], and provides the ability to: • Specify compound topological objects with parametric geometry; • Specify geometric parameters for the instantiation of topological objects, such as coordinates for placement, or probability distributions for random placement of a group of identical objects; • Specify individual coordinate systems for different cell populations; • Define categories of topological objects, such as stellate, basket and Golgi cells, which may be part of a morphological continuum; • Define rules for connectivity between different categories of objects. We present the anisotropic cerebellar circuitry as a case study, and define boundary representations of the arrangement of Purkinje and Golgi cells in the cerebellar cortex. We use the hexagonal grid pattern suggested by Palkovits et al. in [1,2], while allowing for small variability in the placement of cells within a hexagon. The implementation of the language instantiates the topological objects, computes the intersections of the resulting surfaces, and given connectivity rules for the different categories of objects, computes the potential synaptic connectivity (producing graph theoretical measures, as well as connectivity histograms), and ultimately aims to generate connectivity descriptions for the NEURON simulator.

The paper describes transformation rules that map between IDEFO and IDEF3 integration definitions and Petri net modelling constructs. Use of these rules has enabled the development of an enterprise modelling methodology. This methodology ensures that different yet consistent views of business processes and their underpinning resources can be developed by members of a systems engineering team. In particular, the methodology supports the organized capture of functional models, expressed in IDEFO notation, and links them to the subsequent development of behaviour models expressed in IDEF3 notation. Decomposition rules have also been developed that determine relationships between system elements so that they can be modelled at various levels of granularity. Resultant behaviour models can be translated into an equivalent Petri net form. One target application of the modelling rules and methods has been to model interactions between business partners that share common business processes.

SEA: Hierarchically searching efficient adapters for pre-trained models.

We test a method of clustering dialects of English according to patterns of shared phonological features. Previous linguistic research has generally considered phonological features as independent of each other, but context is important: rather than considering each phonological feature individually, we compare the patterns of shared features, or Mutual Information (MI). The dependence of one phonological feature on the others is quantified and exploited. The results of this method of categorizing 59 dialect varieties by 168 binary internal (pronunciation) features are compared to traditional groupings based on external features (e.g., ethnic, geographic). The MI and size of the groups are calculated for taxonomies at various levels of granularity and these groups are compared to other analyses of geographic and ethnic distribution. Applications that could be improved by using MI methods are suggested.

Representing network knowledge using provenance-aware formalisms for cyber-situational awareness