2,677 research outputs found
Non-recursive equivalent of the conjugate gradient method without the need to restart
A simple alternative to the conjugate gradient(CG) method is presented; this
method is developed as a special case of the more general iterated Ritz method
(IRM) for solving a system of linear equations. This novel algorithm is not
based on conjugacy, i.e. it is not necessary to maintain overall
orthogonalities between various vectors from distant steps. This method is more
stable than CG, and restarting techniques are not required. As in CG, only one
matrix-vector multiplication is required per step with appropriate
transformations. The algorithm is easily explained by energy considerations
without appealing to the A-orthogonality in n-dimensional space. Finally,
relaxation factor and preconditioning-like techniques can be adopted easily.Comment: 9 page
Banking Performance in South-Eastern Europe During the Interwar Period
In the framework of the broader political and economic development of the individual states on Balkan Peninsula the author has made the comparison between the performance of the banking sector in Yugoslavia, Romania, Greece and Bulgaria. The analysis was carried out on the sample of balance sheets for the most important joint stock banking companies in the respective countries in the years 1928 and 1929 which represent the peak of the activity and performance of banks in region. In the following years the whole region sank in the abyss of the Great Depression of the thirties when the issue of banking performance was considered on the different way. One of the common features of the banks in region is certainly the prevailing role of short-term resources and a huge imbalance in interest incomes and incomes from other bank transactions. This fact does not only testify to high margins and effective interest rates, but also to a limited portfolio in bank services and other transactions, which was the consequence of the social and economic environment that banks had to operate in.South-eastern Europe; Banks; Banking; Balance sheets.
Resource and Application Models for Advanced Grid Schedulers
As Grid computing is becoming an inevitable future, managing, scheduling and monitoring dynamic, heterogeneous resources will present new challenges. Solutions will have to be agile and adaptive, support self-organization and autonomous management, while maintaining optimal resource utilisation. Presented in this paper are basic principles and architectural concepts for efficient resource allocation in heterogeneous Grid environment
A Study of Grid Applications: Scheduling Perspective
As the Grid evolves from a high performance cluster middleware to a multipurpose utility computing framework, a good understanding of Grid applications, their statistics and utilisation patterns is required. This study looks at job execution times and resource utilisations in a Grid environment, and their significance in cluster and network dimensioning, local level scheduling and resource management
Enabling Adaptive Grid Scheduling and Resource Management
Wider adoption of the Grid concept has led to an increasing amount of federated
computational, storage and visualisation resources being available to scientists and
researchers. Distributed and heterogeneous nature of these resources renders most of the
legacy cluster monitoring and management approaches inappropriate, and poses new
challenges in workflow scheduling on such systems. Effective resource utilisation monitoring
and highly granular yet adaptive measurements are prerequisites for a more efficient Grid
scheduler. We present a suite of measurement applications able to monitor per-process
resource utilisation, and a customisable tool for emulating observed utilisation models. We
also outline our future work on a predictive and probabilistic Grid scheduler. The research is
undertaken as part of UK e-Science EPSRC sponsored project SO-GRM (Self-Organising
Grid Resource Management) in cooperation with BT
Managing Uncertainty: A Case for Probabilistic Grid Scheduling
The Grid technology is evolving into a global, service-orientated
architecture, a universal platform for delivering future high demand
computational services. Strong adoption of the Grid and the utility computing
concept is leading to an increasing number of Grid installations running a wide
range of applications of different size and complexity. In this paper we
address the problem of elivering deadline/economy based scheduling in a
heterogeneous application environment using statistical properties of job
historical executions and its associated meta-data. This approach is motivated
by a study of six-month computational load generated by Grid applications in a
multi-purpose Grid cluster serving a community of twenty e-Science projects.
The observed job statistics, resource utilisation and user behaviour is
discussed in the context of management approaches and models most suitable for
supporting a probabilistic and autonomous scheduling architecture
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Improving the efficiency and accuracy of nocturnal bird Surveys through equipment selection and partial automation
This thesis was submitted for the degree of Engineering Doctorate and awarded by Brunel University.Birds are a key environmental asset and this is recognised through comprehensive legislation and policy ensuring their protection and conservation. Many species are active at night and surveys are required to understand the implications of proposed developments such as towers and reduce possible conflicts with these structures. Night vision devices are commonly used in nocturnal surveys, either to scope an area for bird numbers and activity, or in remotely sensing an area to determine potential risk. This thesis explores some practical and theoretical approaches that can improve the accuracy, confidence and efficiency of nocturnal bird surveillance. As image intensifiers and thermal imagers have operational differences, each device has associated strengths and limitations. Empirical work established that image intensifiers are best used for species identification of birds against the ground or vegetation. Thermal imagers perform best in detection tasks and monitoring bird airspace usage. The typically used approach of viewing bird survey video from remote sensing in its entirety is a slow, inaccurate and inefficient approach. Accuracy can be significantly improved by viewing the survey video at half the playback speed. Motion detection efficiency and accuracy can be greatly improved through the use of adaptive background subtraction and cumulative image differencing. An experienced ornithologist uses bird flight style and wing oscillations to identify bird species. Changes in wing oscillations can be represented in a single inter-frame similarity matrix through area-based differencing. Bird species classification can then be automated using singular value decomposition to reduce the matrices to one-dimensional vectors for training a feed-forward neural network
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