Paging tradeoffs in distributed-shared-memory multiprocessors

Massively parallel processors have begun using commodity operating systems that support demand-paged virtual memory. To evaluate the utility of virtual memory, we measured the behavior of seven shared-memory parallel application programs on a simulated distributed-shared-memory machine. Our results (1) confirm the importance of gang CPU scheduling, (2) show that a page-faulting processor should spin rather than invoke a parallel context switch, (3) show that our parallel programs frequently touch most of their data, and (4) indicate that memory, not just CPUs, must be gang scheduled. Overall, our experiments demonstrate that demand paging has limited value on current parallel machines because of the applications' synchronization and memory reference patterns and the machines' high page-fault and parallel context-switch overheads.An earlier version of this paper was presented at Supercomputing '94.This work is supported in part by NSF Presidential Young Investigator Award CCR-9157366; NSF Grants MIP-9225097, CCR-9100968, and CDA-9024618; Office of Naval Research Grant N00014-89-J-1222; Department of Energy Grant DE-FG02-93ER25176; and donations from Thinking Machines Corporation, Xerox Corporation, and Digital Equipment Corporation.     

BirdVis: visualizing and understanding bird populations

Birds are unrivaled windows into biotic processes at all levels and are proven indicators of ecological well-being. Understanding the determinants of species distributions and their dynamics is an important aspect of ecology and is critical for conservation and management. Through crowdsourcing, since 2002, the eBird project has been collecting bird observation records. These observations, together with local-scale environmental covariates such as climate, habitat, and vegetation phenology have been a valuable resource for a global community of educators, land managers, ornithologists, and conservation biologists. By associating environmental inputs with observed patterns of bird occurrence, predictive models have been developed that provide a statistical framework to harness available data for predicting species distributions and making inferences about species-habitat associations. Understanding these models, however, is challenging because they require scientists to quantify and compare multiscale spatialtemporal patterns. A large series of coordinated or sequential plots must be generated, individually programmed, and manually composed for analysis. This hampers the exploration and is a barrier to making the cross-species comparisons that are essential for coordinating conservation and extracting important ecological information. To address these limitations, as part of a collaboration among computer scientists, statisticians, biologists and ornithologists, we have developed BirdVis, an interactive visualization system that supports the analysis of spatio-temporal bird distribution models. BirdVis leverages visualization techniques and uses them in a novel way to better assist users in the exploration of interdependencies among model parameters. Furthermore, the system allows for comparative visualization through coordinated views, providing an intuitive interface to identify relevant correlations and patterns. We justify our design decisions and present case studies that show how BirdVis has helped scientists obtain new evidence for existing hypotheses, as well as formulate new hypotheses in their domain.     

BallotMaps: detecting name bias in alphabetically ordered ballot papers

The relationship between candidates' position on a ballot paper and vote rank is explored in the case of 5000 candidates for the UK 2010 local government elections in the Greater London area. This design study uses hierarchical spatially arranged graphics to represent two locations that affect candidates at very different scales: the geographical areas for which they seek election and the spatial location of their names on the ballot paper. This approach allows the effect of position bias to be assessed; that is, the degree to which the position of a candidate's name on the ballot paper influences the number of votes received by the candidate, and whether this varies geographically. Results show that position bias was significant enough to influence rank order of candidates, and in the case of many marginal electoral wards, to influence who was elected to government. Position bias was observed most strongly for Liberal Democrat candidates but present for all major political parties. Visual analysis of classification of candidate names by ethnicity suggests that this too had an effect on votes received by candidates, in some cases overcoming alphabetic name bias. The results found contradict some earlier research suggesting that alphabetic name bias was not sufficiently significant to affect electoral outcome and add new evidence for the geographic and ethnicity influences on voting behaviour. The visual approach proposed here can be applied to a wider range of electoral data and the patterns identified and hypotheses derived from them could have significant implications for the design of ballot papers and the conduct of fair elections.     

Emerging AI & Law approaches to automating analysis and retrieval of electronically stored information in discovery proceedings

This article provides an overview of, and thematic justification for, the special issue of the journal of Artificial Intelligence and Law entitled “E-Discovery”. In attempting to define a characteristic “AI & Law” approach to e-discovery, and since a central theme of AI & Law involves computationally modeling legal knowledge, reasoning and decision making, we focus on the theme of representing and reasoning with litigators’ theories or hypotheses about document relevance through a variety of techniques including machine learning. We also identify two emerging techniques for enabling users’ document queries to better express the theories of relevance and connect them to documents: social network analysis and a hypothesis ontology.     

Microdroplet generation in gaseous and liquid environments

As trends in biology, chemistry, medicine and manufacturing have pushed macroscopic processes onto the micro scale, droplet generation has been a key factor in allowing these methods to translate. For both surface-based liquid-in-gas generation and lab-on-a-chip-based liquid-in-liquid generation, the ability to create small monodisperse liquid droplets is critically important in constructing reliable and practical devices. This article reviews liquid microdroplet generation in gaseous and liquid environments, covering the general characteristics of generators and the specific methods and technologies used for generation. Furthermore, this study compiles the issues encountered when operating generators, and the measurements and instrumentation used to characterize generated droplets. Applications of droplet generation in printing, analysis, synthesis and manufacturing are also discussed.     

Approximated approximations for SAO

We propose to replace a number of popular approximations by their diagonal quadratic Taylor series expansions. The resulting separable quadratic approximations are easily convexified, and are well suited for use in dual sequential approximate optimization (SAO) algorithms. Global convergence of the resulting SAO algorithms may be enforced in a natural way using conservatism. The approximated approximation approach is explicitly illustrated for (i) reciprocal and exponential intervening variables, (ii) the intervening variables used in the method of moving asymptotes (MMA), (iii) the intervening variables used in CONLIN, and (iv) the TANA-3 approximations. The use of intermediate responses for use in, for example, truss and frame-like structures, is also discussed. Key advantages of replacing nonlinear approximations by their diagonal quadratic approximations are that these approximated approximations can all be used simultaneously in a single dual statement; the dual does not depend on the form of the original approximations. In addition, in a dual setting, the resulting subproblems yield simple analytical relationships between the primal and dual variables, which is often not the case with the original nonlinear approximations. An important example hereof is the exponential approximation. Although the diagonal quadratic approximations may differ notably from their original counterparts, they typically are quite similar in a sufficiently small search subregion, which relates to the move limits commonly used in SAO anyway.     

Using Developer Activity Data to Enhance Awareness during Collaborative Software Development

Software development is a global activity unconstrained by the bounds of time and space. A major effect of this increasing scale and distribution is that the shared understanding that developers previously acquired by formal and informal face-to-face meetings is difficult to obtain. This paper proposes a shared awareness model that uses information gathered automatically from developer IDE interactions to make explicit orderings of tasks, artefacts and developers that are relevant to particular work contexts in collaborative, and potentially distributed, software development projects. The research findings suggest that such a model can be used to: identify entities (developers, tasks, artefacts) most associated with a particular work context in a software development project; identify relevance relationships amongst tasks, developers and artefacts e.g. which developers and artefacts are currently most relevant to a task or which developers have contributed to a task over time; and, can be used to identify potential bottlenecks in a project through a ‘social graph’ view. Furthermore, this awareness information is captured and provided as developers work in different locations and at different times.     

Applying a critical approach to investigate barriers to digital inclusion and online social networking among young people with disabilities

Despite the seeming ubiquity of young people's Internet use, there are still many for whom access to the Internet and online social networking remains inequitable and patterned by disadvantage. The connection between information technology and young people with disabilities is particularly under‐researched. This article contributes to the field of critical information systems research by exposing significant barriers and facilitators to Internet accessibility for young people with disabilities. It uses Bourdieu's critical theory to explore how the unequal distribution of resources shapes processes of digital inclusion for young people with disabilities. It highlights access needs and experiences that are both disability and non‐disability related. The article draws on interviews in South Australia with 18 young people aged 10–18 years with a physical disability (such as cerebral palsy) or acquired brain injury and with 17 of their family members. Interviews evaluated participants' and parents' reflections on the benefits of a home‐based, goal‐oriented intervention to increase the young person's Internet use for social participation purposes. The Bourdieuian analysis demonstrated how varying levels of accrued individual and family offline capital resources are related to digital/online resources and disability‐specific online resources. This revealed how unequal resources of capital can influence technology use and hence digital inclusion for young people with disabilities. Our study demonstrates that young people with particular types of disabilities require intensive, personalised and long‐term support from within and beyond the family to ‘get online’. We conclude that Internet studies need to more frequently adopt critical approaches to investigate the needs of users and barriers to information technology use within sub‐groups, such as young people with disabilities. © 2016 John Wiley & Sons Ltd     

Analysis of zero-inflated clustered count data: A marginalized model approach

Min and Agresti (2005) proposed random effect hurdle models for zero-inflated clustered count data with two-part random effects for a binary component and a truncated count component. In this paper, we propose new marginalized models for zero-inflated clustered count data using random effects. The marginalized models are similar to Dobbie and Welsh’s (2001) model in which generalized estimating equations were exploited to find estimates. However, our proposed models are based on a likelihood-based approach. A Quasi-Newton algorithm is developed for estimation. We use these methods to carefully analyze two real datasets.