Tornado Detection with Support Vector Machines

Abstract. The National Weather Service (NWS) Mesocyclone Detec-tion Algorithms (MDA) use empirical rules to process velocity data from the Weather Surveillance Radar 1988 Doppler (WSR-88D). In this study Support Vector Machines (SVM) are applied to mesocyclone detection. Comparison with other classification methods like neural networks and radial basis function networks show that SVM are more effective in meso-cyclone/tornado detection.

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

A Study of Machine Learning Techniques for Daily Solar Energy Forecasting using Numerical Weather Models

Proceedings of: 8th International Symposium on Intelligent Distributed Computing (IDC'2014). Madrid, September 3-5, 2014Forecasting solar energy is becoming an important issue in the context of renewable energy sources and Machine Learning Algorithms play an important rule in this field. The prediction of solar energy can be addressed as a time series prediction problem using historical data. Also, solar energy forecasting can be derived from numerical weather prediction models (NWP). Our interest is focused on the latter approach.We focus on the problem of predicting solar energy from NWP computed from GEFS, the Global Ensemble Forecast System, which predicts meteorological variables for points in a grid. In this context, it can be useful to know how prediction accuracy improves depending on the number of grid nodes used as input for the machine learning techniques. However, using the variables from a large number of grid nodes can result in many attributes which might degrade the generalization performance of the learning algorithms. In this paper both issues are studied using data supplied by Kaggle for the State of Oklahoma comparing Support Vector Machines and Gradient Boosted Regression. Also, three different feature selection methods have been tested: Linear Correlation, the ReliefF algorithm and, a new method based on local information analysis.Publicad

Bounds on the electromagnetic interactions of excited spin-3/2 leptons

We discuss possible deviations from QED produced by a virtual excited spin-3/2 lepton in the reaction $e^+e^- \longrightarrow 2\gamma$. Data recorded by the OPAL Collaboration at a c.m. energy $\sqrt{s} = 183 GeV$ are used to establish bounds on the nonstandard-lepton mass and coupling strengths.Comment: Latex, 5 pages, 7 ps figures. To be published in Phys. Rev.

Holonomy Transformation in the FRW Metric

In this work we investigate loop variables in Friedman-Robertson-Walker spacetime. We analyze the parallel transport of vectors and spinors in several paths in this spacetime in order to classify its global properties. The band holonomy invariance is analysed in this background.Comment: 8 page

How Big Can Anomalous W Couplings Be?

Conventional wisdom has it that anomalous gauge-boson self-couplings can be at most a percent or so in size. We test this wisdom by computing these couplings at one loop in a generic renormalizable model of new physics. (For technical reasons we consider the CP-violating couplings here, but our results apply more generally.) By surveying the parameter space we find that the largest couplings (several percent) are obtained when the new particles are at the weak scale. For heavy new physics we compare our findings with expectations based on an effective-lagrangian analysis. We find general patterns of induced couplings which robustly reflect the nature of the underlying physics. We build representative models for which the new physics could be first detected in the anomalous gauge couplings.Comment: 40 pages, 11 figures, (dvi file and figures combined into a uuencoded compressed file), (We correct an error in eq. 39 and its associated figure (9). No changes at all to the text.), McGill-93/40, UQAM-PHE-93/03, NEIPH-93-00

On Optimizing Locally Linear Nearest Neighbour Reconstructions Using Prototype Reduction Schemes

This paper concerns the use of Prototype Reduction Schemes (PRS) to optimize the computations involved in typical k-Nearest Neighbor (k-NN) rules. These rules have been successfully used for decades in statistical Pattern Recognition (PR) applications, and have numerous applications because of their known error bounds. For a given data point of unknown identity, the k-NN possesses the phenomenon that it combines the information about the samples from a priori target classes (values) of selected neighbors to, for example, predict the target class of the tested sample. Recently, an implementation of the k-NN, named as the Locally Linear Reconstruction (LLR) [11], has been proposed. The salient feature of the latter is that by invoking a quadratic optimization process, it is capable of systematically setting model parameters, such as the number of neighbors (specified by the parameter, k) and the weights. However, the LLR takes more time than other conventional methods when it has to be applied to classification tasks. To overcome this problem, we propose a strategy of using a PRS to efficiently compute the optimization problem. In this paper, we demonstrate, first of all, that by completely discarding the points not included by the PRS, we can obtain a reduced set of sample points, using which, in turn, the quadratic optimization problem can be computed far more expediently. The values of the corresponding indices are comparable to those obtained with the original training set (i.e., the one which considers all the data points) even though the computations required to obtain the prototypes and the corresponding classification accuracies are noticeably less. The proposed method has been tested on artificial and real-life data sets, and the results obtained are very promising, and has potential in PR applications

Gravity and Geometric Phases

The behavior of a quantum test particle satisfying the Klein-Gordon equation in a certain class of 4 dimensional stationary space-times is examined. In a space-time of a spinning cosmic string, the wave function of a particle in a box is shown to acquire a geometric phase when the box is transported around a closed path surrounding the string. When interpreted as an Aharonov-Anandan geometric phase, the effect is shown to be related to the Aharonov-Bohm effect.Comment: 11 pages, latex fil

Cumulants and the moment algebra: tools for analysing weak measurements

Recently it has been shown that cumulants significantly simplify the analysis of multipartite weak measurements. Here we consider the mathematical structure that underlies this, and find that it can be formulated in terms of what we call the moment algebra. Apart from resulting in simpler proofs, the flexibility of this structure allows generalizations of the original results to a number of weak measurement scenarios, including one where the weakly interacting pointers reach thermal equilibrium with the probed system.Comment: Journal reference added, minor correction