Negative volatility spillovers in the unrestricted ECCC-GARCH model

Copyright @ 2010 Cambridge University Press.This paper considers a formulation of the extended constant or time-varying conditional correlation GARCH model that allows for volatility feedback of either the positive or negative sign. In the previous literature, negative volatility spillovers were ruled out by the assumption that all the parameters of the model are nonnegative, which is a sufficient condition for ensuring the positive definiteness of the conditional covariance matrix. In order to allow for negative feedback, we show that the positive definiteness of the conditional covariance matrix can be guaranteed even if some of the parameters are negative. Thus, we extend the results of Nelson and Cao (1992) and Tsai and Chan (2008) to a multivariate setting. For the bivariate case of order one, we look into the consequences of adopting these less severe restrictions and find that the flexibility of the process is substantially increased. Our results are helpful for the model-builder, who can consider the unrestricted formulation as a tool for testing various economic theories

SOAP Services with Clarens: Guide for Developers and Administrators

The Clarens application server enables secure, asynchronous SOAP services to run on a Grid cluster such as one of those of the TeraGrid. There is a Client, who wants to use the service and understands the application domain enough to form a reasonable service request; a Developer, who is a power-user of the TeraGrid, who understands both Clarens and the application domain, and creates and deploys a service on a TeraGrid head node; and there is a Root system administrator, who controls the Clarens installation and the cluster on which it runs. The purpose of this document is to provide all of the information a service developer needs to know in order to deploy a Clarens service, with information also provided for the system administrator of the Clarens installation. First we discuss how each of the three roles see the service

Multi-spectral and thermal scanner experiments along the Massachusetts coastline Final report

Aerial multispectral and infrared scanning of Massachusetts coastlin

Comment on "Including Systematic Uncertainties in Confidence Interval Construction for Poisson Statistics"

The incorporation of systematic uncertainties into confidence interval calculations has been addressed recently in a paper by Conrad et al. (Physical Review D 67 (2003) 012002). In their work, systematic uncertainities in detector efficiencies and background flux predictions were incorporated following the hybrid frequentist-Bayesian prescription of Cousins and Highland, but using the likelihood ratio ordering of Feldman and Cousins in order to produce "unified" confidence intervals. In general, the resulting intervals behaved as one would intuitively expect, i.e. increased with increasing uncertainties. However, it was noted that for numbers of observed events less than or of order of the expected background, the intervals could sometimes behave in a completely counter-intuitive fashion -- being seen to initially decrease in the face of increasing uncertainties, but only for the case of increasing signal efficiency uncertainty. In this comment, we show that the problematic behaviour is due to integration over the signal efficiency uncertainty while maximising the best fit alternative hypothesis likelihood. If the alternative hypothesis likelihood is determined by unconditionally maximising with respect to both the unknown signal and signal efficiency uncertainty, the limits display the correct intuitive behaviour.Comment: Submitted to Physical Review

Matching Image Sets via Adaptive Multi Convex Hull

Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image set. Existing multi-model approaches extract local models by clustering each image set individually only once, with fixed clusters used for matching with various image sets. This may not be optimal for discrimination, as undesirable environmental conditions (eg. illumination and pose variations) may result in the two closest clusters representing different characteristics of an object (eg. frontal face being compared to non-frontal face). To address the above problem, we propose a novel approach to enhance nearest points based methods by integrating affine/convex hull classification with an adapted multi-model approach. We first extract multiple local convex hulls from a query image set via maximum margin clustering to diminish the artificial variations and constrain the noise in local convex hulls. We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set. By applying ARC, noisy clusters in the query set can be discarded. Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method outperforms single model approaches and other recent techniques, such as Sparse Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant Analysis.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV), 201

The LSND and MiniBooNE Oscillation Searches at High Δm2\Delta m^2

This paper reviews the results of the LSND and MiniBooNE experiments. The primary goal of each experiment was to effect sensitive searches for neutrino oscillations in the mass region with $\Delta m^2 \sim 1$ eV$^2$. The two experiments are complementary, and so the comparison of results can bring additional information with respect to models with sterile neutrinos. Both experiments obtained evidence for $\bar \nu_\mu \rightarrow \bar \nu_e$ oscillations, and MiniBooNE also observed a $\nu_\mu \rightarrow \nu_e$ excess. In this paper, we review the design, analysis, and results from these experiments. We then consider the results within the global context of sterile neutrino oscillation models. The final data sets require a more extended model than the simple single sterile neutrino model imagined at the time that LSND drew to a close and MiniBooNE began. We show that there are apparent incompatibilities between data sets in models with two sterile neutrinos. However, these incompatibilities may be explained with variations within the systematic error. Overall, models with two (or three) sterile neutrinos seem to succeed in fitting the global data, and they make interesting predictions for future experiments.Comment: Posted with permission from the Annual Review of Nuclear and Particle Science, Volume 63. \c{opyright} 2013 by Annual Reviews, http://www.annualreviews.or

Multi-Action Recognition via Stochastic Modelling of Optical Flow and Gradients

In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification. This approach models each action using a Gaussian mixture using robust low-dimensional action features. Segmentation is achieved by performing classification on overlapping temporal windows, which are then merged to produce the final result. This approach is considerably less complicated than previous methods which use dynamic programming or computationally expensive hidden Markov models (HMMs). Initial experiments on a stitched version of the KTH dataset show that the proposed approach achieves an accuracy of 78.3%, outperforming a recent HMM-based approach which obtained 71.2%