Decomposition techniques for training linear programming support vector machines

The number of fully sequenced fungal genomes is rapidly increasing. Since genetic tools are poorly developed for most filamentous fungi, it is currently difficult to employ genetic engineering for understanding the biology of these fungi and to fully exploit them industrially. For that reason there is a demand for developing versatile methods that can be used to genetically manipulate non-model filamentous fungi. To facilitate this, we have developed a CRISPR-Cas9 based system adapted for use in filamentous fungi. The system is simple and versatile, as RNA guided mutagenesis can be achieved by transforming a target fungus with a single plasmid. The system currently contains four CRISPR-Cas9 vectors, which are equipped with commonly used fungal markers allowing for selection in a broad range of fungi. Moreover, we have developed a script that allows identification of protospacers that target gene homologs in multiple species to facilitate introduction of common mutations in different filamentous fungi. With these tools we have performed RNA-guided mutagenesis in six species of which one has not previously been genetically engineered. Moreover, for a wild-type Aspergillus aculeatus strain, we have used our CRISPR Cas9 system to generate a strain that contains an AACU_pyrG marker and demonstrated that the resulting strain can be used for iterative gene targeting

An ontology enhanced parallel SVM for scalable spam filter training

This is the post-print version of the final paper published in Neurocomputing. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Spam, under a variety of shapes and forms, continues to inflict increased damage. Varying approaches including Support Vector Machine (SVM) techniques have been proposed for spam filter training and classification. However, SVM training is a computationally intensive process. This paper presents a MapReduce based parallel SVM algorithm for scalable spam filter training. By distributing, processing and optimizing the subsets of the training data across multiple participating computer nodes, the parallel SVM reduces the training time significantly. Ontology semantics are employed to minimize the impact of accuracy degradation when distributing the training data among a number of SVM classifiers. Experimental results show that ontology based augmentation improves the accuracy level of the parallel SVM beyond the original sequential counterpart

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202