A bivariate first order autoregressive time series model in exponential variables (BEAR (1))

A simple time series model for bivariate exponential variables having first-order auto-regressive structure is presented. The linear random coefficient difference equation model is an adaptation of the New Exponential Autoregressive model (NEAR (2)). The process is Markovian in the bivariate sense and has correlation structure analogous to that of the Gaussian AR(1) bivariate time series model. The model exhibits a full range of positive correlations and cross-correlations. With some modification in either the innovation or the random coefficients, the model admits some negative values for the cross- correlations. The marginal processes are shown to have correlation structure of ARMA (2,1) modelsPrepared for: Naval Postgraduate School Monterey, CAhttp://archive.org/details/bivariatefirstor00dewaNAN

Characteristics of market for natural beef in Colorado and northern New Mexico

March 2002Includes bibliographical references.The objective of this study is to identify those consumers with the highest willingness to pay and identify which properties of ‘natural meats’ are most important to these consumers. Findings show that consumers are willing to pay a higher percentage premium for natural ground beef than for natural beefsteak. We also categorize respondents using the estimated likelihood of paying a premium for natural beef to compare and contrast how several variables differ among potential customers Several demographics (age and income), as well as shopping behavior and types of meat purchased, are significantly associated with those willing to buy at a premium

Automatic Diagnosis for Prostate Cancer Using Run-Length Matrix Method

Prostate cancer is the most common type of cancer and the second leading cause of cancer death among men in US1. Quantitative assessment of prostate histology provides potential automatic classification of prostate lesions and prediction of response to therapy. Traditionally, prostate cancer diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy samples under microscopes. In this application, we utilize a texture analysis method based on the run-length matrix for identifying tissue abnormalities in prostate histology. A tissue sample was collected from a radical prostatectomy, H&E fixed, and assessed by a pathologist as normal tissue or prostatic carcinoma (PCa). The sample was then subsequently digitized at 50X magnification. We divided the digitized image into sub-regions of 20 X 20 pixels and classified each sub-region as normal or PCa by a texture analysis method. In the texture analysis, we computed texture features for each of the sub-regions based on the Gray-level Run-length Matrix(GL-RLM). Those features include LGRE, HGRE and RPC from the run-length matrix, mean and standard deviation of the pixel intensity. We utilized a feature selection algorithm to select a set of effective features and used a multi-layer perceptron (MLP) classifier to distinguish normal from PCa. In total, the whole histological image was divided into 42 PCa and 6280 normal regions. Three-fold cross validation results show that the proposed method achieves an average classification accuracy of 89.5% with a sensitivity and specificity of 90.48% and 89.49%, respectively

Subduction and vertical coastal motions in the eastern Mediterranean

Convergence in the eastern Mediterranean of oceanic Nubia with Anatolia and the Aegean is complex and poorly understood. Large volumes of sediment obscure the shallow structure of the subduction zone, and since much of the convergence is accommodated aseismically, there are limited earthquake data to constrain its kinematics. We present new source models for recent earthquakes, combining these with field observations, published GPS velocities and reflection-seismic data to investigate faulting in three areas: the Florence Rise, SW Turkey and the Pliny and Strabo Trenches. The depths and locations of earthquakes reveal the geometry of the subducting Nubian plate NE of the Florence Rise, a bathymetric high that is probably formed by deformation of sediment at the surface projection of the Anatolia–Nubia subduction interface. In SW Turkey, the presence of a strike-slip shear zone has often been inferred despite an absence of strike-slip earthquakes. We show that the GPS-derived strain-rate field is consistent with extension on the orthogonal systems of normal faults observed in the region and that strike-slip faulting is not required to explain observed GPS velocities. Further SW, the Pliny and Strabo Trenches are also often interpreted as strike-slip shear zones, but almost all nearby earthquakes have either reverse-faulting or normal-faulting focal mechanisms. Oblique convergence across the trenches may be accommodated either by a partitioned system of strike-slip and reverse faults or by oblique slip on the Aegean–Nubia subduction interface. The observed late-Quaternary vertical motions of coastlines close to the subduction zone are influenced by the interplay between: (1) thickening of the material overriding the subduction interface associated with convergence, which promotes coastal uplift; and (2) subsidence due to extension and associated crustal thinning. Long-wavelength gravity data suggest that some of the observed topographic contrasts in the eastern Mediterranean are supported by mantle convection. However, whether the convection is time dependent and whether its pattern moves relative to Nubia are uncertain, and its contribution to present-day rates of vertical coastal motions is therefore hard to constrain. The observed extension of the overriding material in the subduction system is probably partly related to buoyancy forces arising from topographic contrasts between the Aegean, Anatolia and the Mediterranean seafloor, but the reasons for regional variations are less clear

Prediction of Brain Tumor Progression Using Multiple Histogram Matched MRI Scans

In a recent study [1], we investigated the feasibility of predicting brain tumor progression based on multiple MRI series and we tested our methods on seven patients\u27 MRI images scanned at three consecutive visits A, B and C. Experimental results showed that it is feasible to predict tumor progression from visit A to visit C using a model trained by the information from visit A to visit B. However, the trained model failed when we tried to predict tumor progression from visit B to visit C, though it is clinically more important. Upon a closer look at the MRI scans revealed that histograms of MRI scans such as T1, T2, FLAIR etc taken at different times have slight shifts or different shapes. This is because those MRI scans are qualitative instead of quantitative so MRI scans taken at different times or by different scanners might have slightly different scales or have different homogeneities in the scanning region. In this paper, we proposed a method to overcome this difficulty. The overall goal of this study is to assess brain tumor progression by exploring seven patients\u27 complete MRI records scanned during their visits in the past two years. There are ten MRI series in each visit, including FLAIR, T1-weighted, post-contrast T1-weighted, T2-weighted and five DTI derived MRI volumes: ADC, FA, Max, Min and Middle Eigen Values. After registering all series to the corresponding DTI scan at the first visit, we applied a histogram matching algorithm to non-DTI MRI scans to match their histograms to those of the corresponding MRI scans at the first visit. DTI derived series are quantitative and do not require the histogram matching procedure. A machine learning algorithm was then trained using the data containing information from visit A to visit B, and the trained model was used to predict tumor progression from visit B to visit C. An average of 72% pixel-wise accuracy was achieved for tumor progression prediction from visit B to visit C

A Systematic Approach for Engagement Analysis Under Multitasking Environments

An overload condition can lead to high stress for an operator and further cause substantial drops in performance. On the other extreme, in automated systems, an operator may become underloaded; in which case, it is difficult for the operator to maintain sustained attention. When an unexpected event occurs, either internal or external to the automated system, a disengaged operation may neglect, misunderstand, or respond slowly/inappropriately to the situation. In this paper, we discuss a systematic approach monitor for extremes of cognitive workload and engagement in multitasking environments. Inferences of cognitive workload ar engagement are based on subjective evaluations, objective performance measures, physiological signals, and task analysis results. The systematic approach developed In this paper aggregates these types of information collected under the multitasking environment and can provide a real-time assessment or engagement

Adjacent Slice Prostate Cancer Prediction to Inform MALDI Imaging Biomarker Analysis

Prostate cancer is the second most common type of cancer among men in US [1]. Traditionally, prostate cancer diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy samples under microscopes. Proteomic biomarkers can improve upon these methods. MALDI molecular spectra imaging is used to visualize protein/peptide concentrations across biopsy samples to search for biomarker candidates. Unfortunately, traditional processing methods require histopathological examination on one slice of a biopsy sample while the adjacent slice is subjected to the tissue destroying desorption and ionization processes of MALDI. The highest confidence tumor regions gained from the histopathological analysis are then mapped to the MALDI spectra data to estimate the regions for biomarker identification from the MALDI imaging. This paper describes a process to provide a significantly better estimate of the cancer tumor to be mapped onto the MALDI imaging spectra coordinates using the high confidence region to predict the true area of the tumor on the adjacent MALDI imaged slice

EEG Artifact Removal Using a Wavelet Neural Network

!n this paper we developed a wavelet neural network. (WNN) algorithm for Electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural network and the time/frequency property of wavelet. We. compared the WNN algorithm with .the ICA technique ,and a wavelet thresholding method, which was realized by using the Stein's unbiased risk estimate (SURE) with an adaptive gradient-based optimal threshold. Experimental results on a driving test data set show that WNN can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy data

Subduction and vertical coastal motions in the eastern Mediterranean

Convergence in the eastern Mediterranean of oceanic Nubia with Anatolia and the Aegean is complex and poorly understood. Large volumes of sediment obscure the shallow structure of the subduction zone, and since much of the convergence is accommodated aseismically, there are limited earthquake data to constrain its kinematics. We present new source models for recent earthquakes, combining these with field observations, published GPS velocities and reflection-seismic data to investigate faulting in three areas: the Florence Rise, SW Turkey and the Pliny and Strabo Trenches. The depths and locations of earthquakes reveal the geometry of the subducting Nubian plate NE of the Florence Rise, a bathymetric high that is probably formed by deformation of sediment at the surface projection of the Anatolia-Nubia subduction interface. In SW Turkey, the presence of a strike-slip shear zone has often been inferred despite an absence of strike-slip earthquakes. We show that the GPS-derived strain-rate field is consistent with extension on the orthogonal systems of normal faults observed in the region and that strike-slip faulting is not required to explain observed GPS velocities. Further SW, the Pliny and Strabo Trenches are also often interpreted as strike-slip shear zones, but almost all nearby earthquakes have either reverse-faulting or normal-faulting focal mechanisms. Oblique convergence across the trenches may be accommodated either by a partitioned system of strike-slip and reverse faults or by oblique slip on the Aegean-Nubia subduction interface. The observed late-Quaternary vertical motions of coastlines close to the subduction zone are influenced by the interplay between: (1) thickening of the material overriding the subduction interface associated with convergence, which promotes coastal uplift; and (2) subsidence due to extension and associated crustal thinning. Long-wavelength gravity data suggest that some of the observed topographic contrasts in the eastern Mediterranean are supported by mantle convection. However, whether the convection is time dependent and whether its pattern moves relative to Nubia are uncertain, and its contribution to present-day rates of vertical coastal motions is therefore hard to constrain. The observed extension of the overriding material in the subduction system is probably partly related to buoyancy forces arising from topographic contrasts between the Aegean, Anatolia and the Mediterranean sea floor, but the reasons for regional variations are less clear.AH is supported by a Shell Exploration studentship. This study forms part of the NERC- and 1069 ESRC-funded project "Earthquakes Without Frontiers" under grant NEJ02001X/1, and was 37 partly 1070 funded by the NERC grant "Looking inside the Continents from Space"