On group properties and reality conditions of UOSp(1|2) gauge transformations

For osp(1|2;C) graded Lie algebra, which proper Lie subalgebra is su(2), we consider the Baker-Campbell-Hausdorff formula and formulate a reality condition for the Grassmann-odd transformation parameters that multiply the pair of odd generators of the graded Lie algebra. Utilization of su(2)-spinors clarifies the nature of Grassmann-odd transformation parameters and allow us an investigation of the corresponding infinitesimal gauge transformations. We also explore action of the corresponding group element of UOSp(1|2) on an appropriately graded representation space and find that the graded generalization of hermitian conjugation is compatible with the Dirac adjoint. Consistency of generalized (graded) unitary condition with the proposed reality condition is shown.Comment: 14 page

Classification of protein interaction sentences via gaussian processes

The increase in the availability of protein interaction studies in textual format coupled with the demand for easier access to the key results has lead to a need for text mining solutions. In the text processing pipeline, classification is a key step for extraction of small sections of relevant text. Consequently, for the task of locating protein-protein interaction sentences, we examine the use of a classifier which has rarely been applied to text, the Gaussian processes (GPs). GPs are a non-parametric probabilistic analogue to the more popular support vector machines (SVMs). We find that GPs outperform the SVM and na\"ive Bayes classifiers on binary sentence data, whilst showing equivalent performance on abstract and multiclass sentence corpora. In addition, the lack of the margin parameter, which requires costly tuning, along with the principled multiclass extensions enabled by the probabilistic framework make GPs an appealing alternative worth of further adoption

Promoter prediction using physico-chemical properties of DNA

The ability to locate promoters within a section of DNA is known to be a very difficult and very important task in DNA analysis. We document an approach that incorporates the concept of DNA as a complex molecule using several models of its physico-chemical properties. A support vector machine is trained to recognise promoters by their distinctive physical and chemical properties. We demonstrate that by combining models, we can improve upon the classification accuracy obtained with a single model. We also show that by examining how the predictive accuracy of these properties varies over the promoter, we can reduce the number of attributes needed. Finally, we apply this method to a real-world problem. The results demonstrate that such an approach has significant merit in its own right. Furthermore, they suggest better results from a planned combined approach to promoter prediction using both physicochemical and sequence based techniques

Optimization of Interplanetary Rendezvous Trajectories for Solar Sailcraft Using a Neurocontroller

As for all low-thrust spacecraft, finding optimal solar sailcraft trajectories is a difficult and time-consuming task that involves a lot of experience and expert knowledge, since the convergence behavior of optimizers that are based on numerical optimal control methods depends strongly on an adequate initial guess, which is often hard to find. Even if the op-timizer converges to an ”optimal trajectory”, this trajectory is typically close to the initial guess that is rarely close to the global optimum. This paper demonstrates, that artificial neural networks in combination with evolutionary algorithms can be applied successfully for optimal solar sailcraft steering. Since these evolutionary neurocontrollers explore the trajectory search space more exhaustively than a human expert can do by using tradi-tional optimal control methods, they are able to find steering strategies that generate better trajectories, which are closer to the global optimum. Results are presented for a Near Earth Asteroid rendezvous mission and for a Mercury rendezvous mission

Exploiting separability in large-scale linear support vector machine training

Linear support vector machine training can be represented as a large quadratic program. We present an efficient and numerically stable algorithm for this problem using interior point methods, which requires only O(n) operations per iteration. Through exploiting the separability of the Hessian, we provide a unified approach, from an optimization perspective, to 1-norm classification, 2-norm classification, universum classification, ordinal regression and ɛ-insensitive regression. Our approach has the added advantage of obtaining the hyperplane weights and bias directly from the solver. Numerical experiments indicate that, in contrast to existing methods, the algorithm is largely unaffected by noisy data, and they show training times for our implementation are consistent and highly competitive. We discuss the effect of using multiple correctors, and monitoring the angle of the normal to the hyperplane to determine termination

Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms

10.1109/TNN.2002.1031955IEEE Transactions on Neural Networks1351225-1229ITNN

A simple and efficient algorithm for gene selection using sparse logistic regression

10.1093/bioinformatics/btg308Bioinformatics19172246-2253BOIN

Information geometry and Plefka's mean-field theory

10.1088/0305-4470/33/7/301Journal of Physics A: Mathematical and General3371307-131

SMO algorithm for least-squares SVM formulations

10.1162/089976603762553013Neural Computation152487-50