420,100 research outputs found
Teaching the Grid: Learning Distributed Computing with the M-grid Framework
A classic challenge within Computer Science is to distribute data and processes so as to take advantage of multiple computers tackling a single problem in a simultaneous and coordinated way. This situation arises in a number of different scenarios, including Grid computing which is a secure, service-based architecture for tackling massively parallel problems and creating virtual organizations. Although the Grid seems destined to be an important part of the future computing landscape, it is very difficult to learn how to use as real Grid software requires extensive setting up and complex security processes. M-grid mimics the core features of the Grid, in a much simpler way, enabling the rapid prototyping of distributed applications. We describe m-grid and explore how it may be used to teach foundation Grid computing skills at the Higher Education level and report some of our experiences of deploying it as an exercise within a programming course
A GRID-BASED E-LEARNING MODEL FOR OPEN UNIVERSITIES
E-learning has grown to become a widely
accepted method of learning all over the world. As a
result, many e-learning platforms which have been
developed based on varying technologies were faced
with some limitations ranging from storage
capability, computing power, to availability or access
to the learning support infrastructures. This has
brought about the need to develop ways to
effectively manage and share the limited resources
available in the e-learning platform. Grid computing
technology has the capability to enhance the quality
of pedagogy on the e-learning platform.
In this paper we propose a Grid-based e-learning
model for Open Universities. An attribute of such
universities is the setting up of multiple remotely
located campuses within a country.
The grid-based e-learning model presented in
this work possesses the attributes of an elegant
architectural framework that will facilitate efficient
use of available e-learning resources and cost
reduction, leading to general improvement of the
overall quality of the operations of open universities
Learning Generative ConvNets via Multi-grid Modeling and Sampling
This paper proposes a multi-grid method for learning energy-based generative
ConvNet models of images. For each grid, we learn an energy-based probabilistic
model where the energy function is defined by a bottom-up convolutional neural
network (ConvNet or CNN). Learning such a model requires generating synthesized
examples from the model. Within each iteration of our learning algorithm, for
each observed training image, we generate synthesized images at multiple grids
by initializing the finite-step MCMC sampling from a minimal 1 x 1 version of
the training image. The synthesized image at each subsequent grid is obtained
by a finite-step MCMC initialized from the synthesized image generated at the
previous coarser grid. After obtaining the synthesized examples, the parameters
of the models at multiple grids are updated separately and simultaneously based
on the differences between synthesized and observed examples. We show that this
multi-grid method can learn realistic energy-based generative ConvNet models,
and it outperforms the original contrastive divergence (CD) and persistent CD.Comment: CVPR 201
Towards collaborative learning via shared artefacts over the Grid
The Web is the most pervasive collaborative technology in widespread use today; and its use to support
eLearning has been highly successful. There are many web-based Virtual Learning Environments such as
WebCT, FirstClass, and BlackBoard as well as associated web-based Managed Learning Environments. In
the future, the Grid promises to provide an extremely powerful infrastructure allowing both learners and
teachers to collaborate in various learning contexts and to share learning materials, learning processes,
learning systems, and experiences. This position paper addresses the role of support for sharing artefacts
in distributed systems such as the Grid. An analogy is made between collaborative software development
and collaborative learning with the goal of gaining insights into the requisite support for artefact sharing
within the eLearning community
Grid Cell Hexagonal Patterns Formed by Fast Self-Organized Learning within Entorhinal Cortex
Grid cells in the dorsal segment of the medial entorhinal cortex (dMEC) show remarkable hexagonal activity patterns, at multiple spatial scales, during spatial navigation. How these hexagonal patterns arise has excited intense interest. It has previously been shown how a selforganizing map can convert firing patterns across entorhinal grid cells into hippocampal place cells that are capable of representing much larger spatial scales. Can grid cell firing fields also arise during navigation through learning within a self-organizing map? A neural model is proposed that converts path integration signals into hexagonal grid cell patterns of multiple scales. This GRID model creates only grid cell patterns with the observed hexagonal structure, predicts how these hexagonal patterns can be learned from experience, and can process biologically plausible neural input and output signals during navigation. These results support a unified computational framework for explaining how entorhinal-hippocampal interactions support spatial navigation.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of Defense Advanced Research Projects Agency (HR00ll-09-3-0001, HR0011-09-C-0011
Exact Topology and Parameter Estimation in Distribution Grids with Minimal Observability
Limited presence of nodal and line meters in distribution grids hinders their
optimal operation and participation in real-time markets. In particular lack of
real-time information on the grid topology and infrequently calibrated line
parameters (impedances) adversely affect the accuracy of any operational power
flow control. This paper suggests a novel algorithm for learning the topology
of distribution grid and estimating impedances of the operational lines with
minimal observational requirements - it provably reconstructs topology and
impedances using voltage and injection measured only at the terminal (end-user)
nodes of the distribution grid. All other (intermediate) nodes in the network
may be unobserved/hidden. Furthermore no additional input (e.g., number of grid
nodes, historical information on injections at hidden nodes) is needed for the
learning to succeed. Performance of the algorithm is illustrated in numerical
experiments on the IEEE and custom power distribution models
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