The BAR domain proteins: Molding membranes in fission, fusion, and phagy

The Bin1/amphiphysin/Rvs167 (BAR) domain proteins are a ubiquitous protein family. Genes encoding members of this family have not yet been found in the genomes of prokaryotes, but within eukaryotes, BAR domain proteins are found universally from unicellular eukaryotes such as yeast through to plants, insects, and vertebrates. BAR domain proteins share an N-terminal BAR domain with a high propensity to adopt alpha-helical structure and engage in coiled-coil interactions with other proteins. BAR domain proteins are implicated in processes as fundamental and diverse as fission of synaptic vesicles, cell polarity, endocytosis, regulation of the actin cytoskeleton, transcriptional repression, cell-cell fusion, signal transduction, apoptosis, secretory vesicle fusion, excitation-contraction coupling, learning and memory, tissue differentiation, ion flux across membranes, and tumor suppression. What has been lacking is a molecular understanding of the role of the BAR domain protein in each process. The three-dimensional structure of the BAR domain has now been determined and valuable insight has been gained in understanding the interactions of BAR domains with membranes. The cellular roles of BAR domain proteins, characterized over the past decade in cells as distinct as yeasts, neurons, and myocytes, can now be understood in terms of a fundamental molecular function of all BAR domain proteins: to sense membrane curvature, to bind GTPases, and to mold a diversity of cellular membranes.

Machine learning for matching astronomy catalogues

An emerging issue in the field of astronomy is the integration, management and utilization of databases from around the world to facilitate scientific discovery. In this paper, we investigate application of the machine learning techniques of support vector machines and neural networks to the problem of amalgamating catalogues of galaxies as objects from two disparate data sources: radio and optical. Formulating this as a classification problem presents several challenges, including dealing with a highly unbalanced data set. Unlike the conventional approach to the problem (which is based on a likelihood ratio) machine learning does not require density estimation and is shown here to provide a significant improvement in performance. We also report some experiments that explore the importance of the radio and optical data features for the matching problem.

Boosting the tree augmented naive bayes classifier

The Tree Augmented Naïve Bayes (TAN) classifier relaxes the sweeping independence assumptions of the Naïve Bayes approach by taking account of conditional probabilities. It does this in a limited sense, by incorporating the conditional probability of each attribute given the class and (at most) one other attribute. The method of boosting has previously proven very effective in improving the performance of Naïve Bayes classifiers and in this paper, we investigate its effectiveness on application to the TAN classifier.

Improving support vector solutions by selecting a sequence of training subsets

In this paper we demonstrate that it is possible to gradually improve the performance of support vector machine (SVM) classifiers by using a genetic algorithm to select a sequence of training subsets from the available data. Performance improvement is possible because the SVM solution generally lies some distance away from the Bayes optimal in the space of learning parameters. We illustrate performance improvements on a number of benchmark data sets.

A modified particle filter for simultaneous robot localization and landmark tracking in an indoor environment

This paper presents the implementation of a modified particle filter for vision-based simultaneous localization and mapping of an autonomous robot in a structured indoor environment. Through this method, artificial landmarks such as multi-coloured cylinders can be tracked with a camera mounted on the robot, and the position of the robot can be estimated at the same time. Experimental results in simulation and in real environments show that this approach has advantages over the extended Kalman filter with ambiguous data association and various levels of odometric noise.

A machine learning approach to intraday trading on foreign exchange markets

Foreign Exchange trading has emerged in recent times as a significant activity in many countries. As with most forms of trading, the activity is influenced by many random parameters so that the creation of a system that effectively emulates the trading process will be very helpful. In this paper we try to create such a system using Machine learning approach to emulate trader behaviour on the Foreign Exchange market and to find the most profitable trading strategy.

An implementation of genetic algorithms as a basis for a trading system on the foreign exchange market

Foreign exchange trading has emerged in recent times as a significant activity in many countries. As with most forms of trading, the activity is influenced by many random parameters so that the creation of a system that effectively emulates the trading process is very helpful. In this paper, we try to create such a system with a genetic algorithm engine to emulate trader behaviour on the foreign exchange market and to find the most profitable trading strategy.

Tuning pattern classifier parameters using a genetic algorithm with an application in mobile robotics

Support vector machines (SVMs) have recently emerged as a powerful technique for solving problems in pattern classification and regression. Best performance is obtained from the SVM its parameters have their values optimally set. In practice, good parameter settings are usually obtained by a lengthy process of trial and error. This paper describes the use of genetic algorithm to evolve these parameter settings for an application in mobile robotics.