Data issues in general equilibrium modelling

In recent years global general equilibrium (GE) models have been used to analyse policy issues such as trade liberalisation, regional integration and environmental policies. While a global CGE model is an excellent tool to perform a comprehensive analysis of such policies, it requires an enormous amount of data to calibrate or parameterise the model itself. Quality differences in the data sets might affect the results of the model simulation. Identifying and removing problems in the database is an important task for the effective use of a CGE model. In this paper, we suggest an approach to detect and remove major inconsistencies from the database of a CGE model. To illustrate, we examine and modify selected interindustry transactions in the GTAP version 5 database, a general equilibrium database of the global economy. The revised database was evaluated against the original by undertaking technical change and trade liberalisation experiments using Global Trade Analysis Project (GTAP) model. Results show that estimates using the modified database are more credible than those from the original database.International Relations/Trade, Research Methods/ Statistical Methods,

Multi-Class Classification for Identifying JPEG Steganography Embedding Methods

Over 725 steganography tools are available over the Internet, each providing a method for covert transmission of secret messages. This research presents four steganalysis advancements that result in an algorithm that identifies the steganalysis tool used to embed a secret message in a JPEG image file. The algorithm includes feature generation, feature preprocessing, multi-class classification and classifier fusion. The first contribution is a new feature generation method which is based on the decomposition of discrete cosine transform (DCT) coefficients used in the JPEG image encoder. The generated features are better suited to identifying discrepancies in each area of the decomposed DCT coefficients. Second, the classification accuracy is further improved with the development of a feature ranking technique in the preprocessing stage for the kernel Fisher s discriminant (KFD) and support vector machines (SVM) classifiers in the kernel space during the training process. Third, for the KFD and SVM two-class classifiers a classification tree is designed from the kernel space to provide a multi-class classification solution for both methods. Fourth, by analyzing a set of classifiers, signature detectors, and multi-class classification methods a classifier fusion system is developed to increase the detection accuracy of identifying the embedding method used in generating the steganography images. Based on classifying stego images created from research and commercial JPEG steganography techniques, F5, JP Hide, JSteg, Model-based, Model-based Version 1.2, OutGuess, Steganos, StegHide and UTSA embedding methods, the performance of the system shows a statistically significant increase in classification accuracy of 5%. In addition, this system provides a solution for identifying steganographic fingerprints as well as the ability to include future multi-class classification tools

Basic Parameter Estimation of Binary Neutron Star Systems by the Advanced LIGO/Virgo Network

Within the next five years, it is expected that the Advanced LIGO/Virgo network will have reached a sensitivity sufficient to enable the routine detection of gravitational waves. Beyond the initial detection, the scientific promise of these instruments relies on the effectiveness of our physical parameter estimation capabilities. The majority of this effort has been towards the detection and characterization of gravitational waves from compact binary coalescence, e.g. the coalescence of binary neutron stars. While several previous studies have investigated the accuracy of parameter estimation with advanced detectors, the majority have relied on approximation techniques such as the Fisher Matrix. Here we report the statistical uncertainties that will be achievable for optimal detection candidates (SNR = 20) using the full parameter estimation machinery developed by the LIGO/Virgo Collaboration via Markov-Chain Monte Carlo methods. We find the recovery of the individual masses to be fractionally within 9% (15%) at the 68% (95%) credible intervals for equal-mass systems, and within 1.9% (3.7%) for unequal-mass systems. We also find that the Advanced LIGO/Virgo network will constrain the locations of binary neutron star mergers to a median uncertainty of 5.1 deg^2 (13.5 deg^2) on the sky. This region is improved to 2.3 deg^2 (6 deg^2) with the addition of the proposed LIGO India detector to the network. We also report the average uncertainties on the luminosity distances and orbital inclinations of ideal detection candidates that can be achieved by different network configurations.Comment: Second version: 15 pages, 9 figures, accepted in Ap

Multi-Class Classification Averaging Fusion for Detecting Steganography

Multiple classifier fusion has the capability of increasing classification accuracy over individual classifier systems. This paper focuses on the development of a multi-class classification fusion based on weighted averaging of posterior class probabilities. This fusion system is applied to the steganography fingerprint domain, in which the classifier identifies the statistical patterns in an image which distinguish one steganography algorithm from another. Specifically we focus on algorithms in which jpeg images provide the cover in order to communicate covertly. The embedding methods targeted are F5, JSteg, Model Based, OutGuess, and StegHide. The developed multi-class steganalvsis system consists of three levels: (1) feature preprocessing in which a projection function maps the input vectors into a separable space, (2) classifier system using an ensemble of classifiers, and (3) two weighted fusion techniques are compared, the first is a well known variance weighted fusion and an Gaussian weighted fusion. Results show that through the novel addition of the classifier fusion step to the multi-class steganalysis system, the classification accuracy is improved by up to 12%

Forecasting machine performance check output using Holt-Winters approach

Background: Machine Performance Check (MPC) is an automated TrueBeam quality control (QC) tool used to verify beam output, isocenter, and uniformity. The aim of this study was to build an MPC output variation time series modeled on the Holt-Winters method over thirty days. Methods: After AAPM TG-51 and baseline data were established for the Edge TrueBeam, daily MPC output data were gathered and analyzed through a Holt-Winters (additive and multiplicative) method. The model's performance was assessed via three standard error measures: the mean squared error (MSE), the mean absolute percentage error (MAPE), and the mean absolute deviation (MAE). The aim was achieved using a nonlinear multistart solver on the Excel platform. Results: The results showed that MPC output variation forecasting is energy and model dependent. Both additive and multiplicative Holt-Winters methods were suitable for the analysis. The performance metrics MSE, MAPE, and MAD were found to be well within acceptable limits. Conclusions: A Holt-Winters model was able to accurately forecast the MPC output variation

Statistical process control: machine performance check output variation

Background: The aim of this study was to illustrate and evaluate the use of different statistical process control (SPC) aspects to examine linear accelerator daily output variation through machine performance check (MPC) over a month. Methods: MPC daily output data were obtained over a month after AAPM TG-51 were performed. Baseline data were set, and subsequent data were conducted through SPC. The Shewhart chart was used to determine the upper and lower control limits, whereas CUSUM for subtle changes. Results: The upper and lower control limits obtained via SPC analysis of the MPC data were found to fall within AAPM Task Group 142 guidelines. MPC output variation data were within ±3% of their action limits values and were within 1% over thirty days of data. The process capability ratio and process acceptability ratio, Cp and Cpk values were ≥2 for all energies. Potential undetected deviations were captured by the CUSUM chart for photons and electrons beam energy. Conclusions: Control charts were found to be useful in terms of detecting changes in MPC output