Large heterogeneous classes. 'Clases heterog??neas'

El art??culo es un resumen de la propia autora sobre la conferencia realizada en la sede del CDL. Contiene una presentaci??n en catal??n, realizada por Aina Carreras

Fusion of motion segmentation algorithms

Many algorithms have been developed to achieve motion segmentation for video surveillance. The algorithms produce varying performances under the infinite amount of changing conditions. It has been recognised that individually these algorithms have useful properties. Fusing the statistical result of these algorithms is investigated, with robust motion segmentation in mind.

Molecular genetic differentiation in earthworms inhabiting a heterogeneous Pb-polluted landscape

A Pb-mine site situated on acidic soil, but comprising of Ca-enriched islands around derelict buildings was used to study the spatial pattern of genetic diversity in Lumbricus rubellus. Two distinct genetic lineages ('A' and 'B'), differentiated at both the mitochondrial (mtDNA COII) and nuclear level (AFLPs) were revealed with a mean inter-lineage mtDNA sequence divergence of approximately 13%, indicative of a cryptic species complex. AFLP analysis indicates that lineage A individuals within one central 'ecological island' site are uniquely clustered, with little genetic overlap with lineage A individuals at the two peripheral sites. FTIR microspectroscopy of Pb-sequestering chloragocytes revealed different phosphate profiles in residents of adjacent acidic and calcareous islands. Bioinformatics found over-representation of Ca pathway genes in ESTPb libraries. Subsequent sequencing of a Ca-transport gene, SERCA, revealed mutations in the protein's cytosolic domain. We recommend the mandatory genotyping of all individuals prior to field-based ecotoxicological assays, particularly those using discriminating genomic technologies.

Dynamic load balancing for the distributed mining of molecular structures

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.

Generic systolic array for genetic algorithms

The authors present a systolic design for a simple GA mechanism which provides high throughput and unidirectional pipelining by exploiting the inherent parallelism in the genetic operators. The design computes in O(N+G) time steps using O(N2) cells where N is the population size and G is the chromosome length. The area of the device is independent of the chromosome length and so can be easily scaled by replicating the arrays or by employing fine-grain migration. The array is generic in the sense that it does not rely on the fitness function and can be used as an accelerator for any GA application using uniform crossover between pairs of chromosomes. The design can also be used in hybrid systems as an add-on to complement existing designs and methods for fitness function acceleration and island-style population management

Reactive uptake coefficients for heterogeneous reaction of N2O5 with submicron aerosols of NaCl and natural sea salt

The kinetics of uptake of gaseous N2O5 on submicron aerosols containing NaCl and natural sea salt have been investigated in a flow reactor as a function of relative humidity (RH) in the range 30-80% at 295±2K and a total pressure of 1bar. The measured uptake coefficients, γ, were larger on the aerosols containing sea salt compared to those of pure NaCl, and in both cases increased with increasing RH. These observations are explained in terms of the variation in the size of the salt droplets, which leads to a limitation in the uptake rate into small particles. After correction for this effect the uptake coefficients are independent of relative humidity, and agree with those measured previously on larger droplets. A value of γ=0.025 is recommended for the reactive uptake coefficient for N2O5 on deliquesced sea salt droplets at 298K and RH>40%.

Parallel genetic algorithms for the tuning of a fuzzy AQM controller

This paper presents the results of the application of a parallel Genetic Algorithm (GA) in order to design a Fuzzy Proportional Integral (FPI) controller for active queue management on Internet routers. The Active Queue Management (AQM) policies are those policies of router queue management that allow the detection of network congestion, the notification of such occurrences to the hosts on the network borders, and the adoption of a suitable control policy. Two different parallel implementations of the genetic algorithm are adopted to determine an optimal configuration of the FPI controller parameters. Finally, the results of several experiments carried out on a forty nodes cluster of workstations are presented.

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.