76 resultados para Balancing
em CentAUR: Central Archive University of Reading - UK
Resumo:
Peat wetlands that have been restored from agricultural Land have the potential to act as Long term sources of phosphorus (P) and, therefore have to potenital to accelerate freshwater eutrophication. During a two-year study the water table in a eutrophic fen peat that was managed by pump drainage fluctuated annually between +20 cm and -60 cm relative to ground Level. This precise management was facilitated by the high hydraulic conductivity (K) of the humified peat (1.1 x 10(-5) m s(-1)) below around 60 cm depth. However, during one week of intermittent pumping, as much as 50 g ha(-1) dissolved P entered the pumped ditch. Summer. rainfall events and autumn reflooding also triggered P losses. The P Losses were attributed to the low P sorption capacity (217 mg kg(-1)) of the saturated peat below 60 cm, combined with its high K and the reductive dissolution of Fe bound P.
Resumo:
In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable for large-scale, multi-domain, heterogeneous environments, such as computational grids.
Resumo:
In this paper, we present a distributed computing framework for problems characterized by a highly irregular search tree, whereby no reliable workload prediction is available. The framework is based on a peer-to-peer computing environment and dynamic load balancing. The system allows for dynamic resource aggregation, does not depend on any specific meta-computing middleware and is suitable for large-scale, multi-domain, heterogeneous environments, such as computational Grids. Dynamic load balancing policies based on global statistics are known to provide optimal load balancing performance, while randomized techniques provide high scalability. The proposed method combines both advantages and adopts distributed job-pools and a randomized polling technique. The framework has been successfully adopted in a parallel search algorithm for subgraph mining and evaluated on a molecular compounds dataset. The parallel application has shown good calability and close-to linear speedup in a distributed network of workstations.
Resumo:
LIght Detection And Ranging (LIDAR) data for terrain and land surveying has contributed to many environmental, engineering and civil applications. However, the analysis of Digital Surface Models (DSMs) from complex LIDAR data is still challenging. Commonly, the first task to investigate LIDAR data point clouds is to separate ground and object points as a preparatory step for further object classification. In this paper, the authors present a novel unsupervised segmentation algorithm-skewness balancing to separate object and ground points efficiently from high resolution LIDAR point clouds by exploiting statistical moments. The results presented in this paper have shown its robustness and its potential for commercial applications.
Resumo:
One among the most influential and popular data mining methods is the k-Means algorithm for cluster analysis. Techniques for improving the efficiency of k-Means have been largely explored in two main directions. The amount of computation can be significantly reduced by adopting geometrical constraints and an efficient data structure, notably a multidimensional binary search tree (KD-Tree). These techniques allow to reduce the number of distance computations the algorithm performs at each iteration. A second direction is parallel processing, where data and computation loads are distributed over many processing nodes. However, little work has been done to provide a parallel formulation of the efficient sequential techniques based on KD-Trees. Such approaches are expected to have an irregular distribution of computation load and can suffer from load imbalance. This issue has so far limited the adoption of these efficient k-Means variants in parallel computing environments. In this work, we provide a parallel formulation of the KD-Tree based k-Means algorithm for distributed memory systems and address its load balancing issue. Three solutions have been developed and tested. Two approaches are based on a static partitioning of the data set and a third solution incorporates a dynamic load balancing policy.
Resumo:
This paper provides an analysis of The Life Aquatic in the context of debates around tone, irony, the Smart Film, the New Sincerity and the Quirky. It argues that Anderson is one of a small but significant number of filmmakers to escape from the indiscriminate irony of fin de sie`cle cinema, and finds The Life Aquatic Aquatic a particularly interesting film in which to explore such matters because of its ready artifice, strong elements of pastiche and measuredly preposterous excesses. Offering a critical analysis, the paper balances an engagement with some of the systemic elements of the film’s tone with the detailed organisation of tonal elements in particular sequences.