High harmonic generation in the relativistic limit

The generation of extremely bright coherent X-ray pulses in the femtosecond and attosecond regime is currently one of the most exciting frontiers of physics - allowing, for the first time, measurements with unprecedented temporal resolution(1-6). Harmonics from laser - solid target interactions have been identified as a means of achieving fields as high as the Schwinger limit(2,7) (E = 1.3 x 10(16) V m(-1)) and as a highly promising route to high-efficiency attosecond (10(-18) s) pulses(8) owing to their intrinsically phase-locked nature. The key steps to attain these goals are achieving high conversion efficiencies and a slow decay of harmonic efficiency to high orders by driving harmonic production to the relativistic limit(1). Here we present the first experimental demonstration of high harmonic generation in the relativistic limit, obtained on the Vulcan Petawatt laser(9). High conversion efficiencies (eta> 10(-6) per harmonic) and bright emission (> 10(22) photons s(-1) mm(-2) mrad(-2) (0.1% bandwidth)) are observed at wavelengths <4 nm ( the 'water-window' region of particular interest for bio-microscopy).

Simultaneous feature selection and feature weighting using Hybrid Tabu Search/K-nearest neighbor classifier

Feature selection and feature weighting are useful techniques for improving the classification accuracy of K-nearest-neighbor (K-NN) rule. The term feature selection refers to algorithms that select the best subset of the input feature set. In feature weighting, each feature is multiplied by a weight value proportional to the ability of the feature to distinguish pattern classes. In this paper, a novel hybrid approach is proposed for simultaneous feature selection and feature weighting of K-NN rule based on Tabu Search (TS) heuristic. The proposed TS heuristic in combination with K-NN classifier is compared with several classifiers on various available data sets. The results have indicated a significant improvement in the performance in classification accuracy. The proposed TS heuristic is also compared with various feature selection algorithms. Experiments performed revealed that the proposed hybrid TS heuristic is superior to both simple TS and sequential search algorithms. We also present results for the classification of prostate cancer using multispectral images, an important problem in biomedicine.

Variation in offspring size towards the high elevational range edge of a montane annual

Understanding the ecological determinants of species’ distribution is a fundamental goal of ecology, and is increasingly important with changing limits to species’ range. Species often reach distributional limits on gradients of resource availability, but the extent to which offspring provisioning varies towards range limits is poorly understood. Selection is generally expected to favour higher provisioning of individual offspring in environments with short growing seasons and limited moisture, nutrients, or hosts for parasitism. However, individual provisioning may decline if parent size is limited by resources. This thesis focuses on three major questions: 1) does seed size vary over an elevational gradient? 2) does this variation respond adaptively towards the range limit? and 3) is potential elevational variation environmentally or genetically controlled? I tested variation in seed investment towards the upper elevational limit of the hemiparasitic annual herb Rhinanthus minor, sampled across an elevational range of 1,000m in the Rocky Mountains of Alberta, Canada. I also used a reciprocal transplant experiment to address the heritability of seed mass. Seed mass increased marginally towards higher elevations, while seed number and plant size declined. There was a strong elevational increase in seed mass scaled by overall plant size. Therefore, investment in individual seeds was higher towards the upper range edge, indicating potential adaptation of the reproductive strategy to allow for establishment in marginal environments. Genetic, environmental, and genotype-by-environment interactions were observed in transplanted populations, but the relative proportions of these effects on seed size were unclear.

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

Gene selection by cooperative competition clustering

Clustering analysis of data from DNA microarray hybridization studies is an essential task for identifying biologically relevant groups of genes. Attribute cluster algorithm (ACA) has provided an attractive way to group and select meaningful genes. However, ACA needs much prior knowledge about the genes to set the number of clusters. In practical applications, if the number of clusters is misspecified, the performance of the ACA will deteriorate rapidly. In fact, it is a very demanding to do that because of our little knowledge. We propose the Cooperative Competition Cluster Algorithm (CCCA) in this paper. In the algorithm, we assume that both cooperation and competition exist simultaneously between clusters in the process of clustering. By using this principle of Cooperative Competition, the number of clusters can be found in the process of clustering. Experimental results on a synthetic and gene expression data are demonstrated. The results show that CCCA can choose the number of clusters automatically and get excellent performance with respect to other competing methods.