Chess endgame update

This is a review of progress in the Chess Endgame field. It includes news of the promulgation of Endgame Tables, their use, non-use and potential runtime creation. It includes news of data-mining achievements related to 7-man chess and to the field of Chess Studies. It includes news of an algorithm to create Endgame Tables for variants of the normal game of chess.

Dynamic load balancing in parallel KD-tree k-means

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.

A hierarchical distributed approach for mining molecular fragments

Recently, two approaches have been introduced that distribute the molecular fragment mining problem. The first approach applies a master/worker topology, the second approach, a completely distributed peer-to-peer system, solves the scalability problem due to the bottleneck at the master node. However, in many real world scenarios the participating computing nodes cannot communicate directly due to administrative policies such as security restrictions. Thus, potential computing power is not accessible to accelerate the mining run. To solve this shortcoming, this work introduces a hierarchical topology of computing resources, which distributes the management over several levels and adapts to the natural structure of those multi-domain architectures. The most important aspect is the load balancing scheme, which has been designed and optimized for the hierarchical structure. The approach allows dynamic aggregation of heterogenous computing resources and is applied to wide area network scenarios.

A parallel genetic algorithm for the Steiner Problem in Networks

This paper presents a parallel genetic algorithm to the Steiner Problem in Networks. Several previous papers have proposed the adoption of GAs and others metaheuristics to solve the SPN demonstrating the validity of their approaches. This work differs from them for two main reasons: the dimension and the characteristics of the networks adopted in the experiments and the aim from which it has been originated. The reason that aimed this work was namely to build a comparison term for validating deterministic and computationally inexpensive algorithms which can be used in practical engineering applications, such as the multicast transmission in the Internet. On the other hand, the large dimensions of our sample networks require the adoption of a parallel implementation of the Steiner GA, which is able to deal with such large problem instances.

Efficient mining of discriminative molecular fragments

Frequent pattern discovery in structured data is receiving an increasing attention in many application areas of sciences. However, the computational complexity and the large amount of data to be explored often make the sequential algorithms unsuitable. In this context high performance distributed computing becomes a very interesting and promising approach. In this paper we present a parallel formulation of the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The application is characterized by a highly irregular tree-structured computation. No estimation is available for task workloads, which show a power-law distribution in a wide range. The proposed approach allows dynamic resource aggregation and provides fault and latency tolerance. These features make the distributed application suitable for multi-domain heterogeneous environments, such as computational Grids. The distributed application has been evaluated on the well known National Cancer Institute’s HIV-screening dataset.

Synthesis of a systolic array genetic algorithm

The paper presents a design for a hardware genetic algorithm which uses a pipeline of systolic arrays. These arrays have been designed using systolic synthesis techniques which involve expressing the algorithm as a set of uniform recurrence relations. The final design divorces the fitness function evaluation from the hardware and can process chromosomes of different lengths, giving the design a generic quality. The paper demonstrates the design methodology by progressively re-writing a simple genetic algorithm, expressed in C code, into a form from which systolic structures can be deduced. This paper extends previous work by introducing a simplification to a previous systolic design for the genetic algorithm. The simplification results in the removal of 2N 2 + 4N cells and reduces the time complexity by 3N + 1 cycles.

The systolic array genetic algorithm, an example of systolic arrays as a reconfigurable design methodology

We advocate the use of systolic design techniques to create custom hardware for Custom Computing Machines. We have developed a hardware genetic algorithm based on systolic arrays to illustrate the feasibility of the approach. The architecture is independent of the lengths of chromosomes used and can be scaled in size to accommodate different population sizes. An FPGA prototype design can process 16 million genes per second.

Mining, grievance and conflict in rural Tanzania

This paper provides an extended analysis of the tensions that have surfaced between large-scale mine operators and artisanal miners in gold-rich areas of rural Tanzania. The literature on grievance is used to contextualise, these disputes, the underlying cause of which is artisanal miners' mounting frustration over not being able to secure viable concessions to work. Newly implemented legislation has, for the most part, empowered foreign large-scale mine operators, while simultaneously disempowering indigenous small-scale miners. In many cases, the former have addressed mounting security and community problems on their own. Until the country's major mine operators extend assistance to marginalised small-scale mining groups, the likelihood of violent conflict unfolding between these parties will increase.

Abatement of mercury pollution in the small-scale gold mining industry: Restructuring the policy and research agendas

This paper critiques contemporary research and policy approaches taken toward the analysis and abatement of mercury pollution in the small-scale gold mining sector. Unmonitored releases of mercury from gold amalgamation have caused considerable environmental contamination and human health complications in rural reaches of sub-Saharan Africa, Latin America and Asia. Whilst these problems have caught the attention of the scientific community over the past 15-20 years, the research that has since been undertaken has failed to identify appropriate mitigation measures, and has done little to advance understanding of why contamination persists. Moreover, the strategies used to educate operators about the impacts of acute mercury exposure, and the technologies implemented to prevent farther pollution, have been marginally effective at best. The mercury pollution problem will not be resolved until governments and donor agencies commit to carrying out research aimed at improving understanding of the dynamics of small scale gold mining communities. Acquisition of this knowledge is the key to designing and implementing appropriate support and abatement measures. (c) 2005 Elsevier B.V. All rights reserved.