Transforming morning to afternoon using linear regression techniques

Visual localization in outdoor environments is often hampered by the natural variation in appearance caused by such things as weather phenomena, diurnal fluctuations in lighting, and seasonal changes. Such changes are global across an environment and, in the case of global light changes and seasonal variation, the change in appearance occurs in a regular, cyclic manner. Visual localization could be greatly improved if it were possible to predict the appearance of a particular location at a particular time, based on the appearance of the location in the past and knowledge of the nature of appearance change over time. In this paper, we investigate whether global appearance changes in an environment can be learned sufficiently to improve visual localization performance. We use time of day as a test case, and generate transformations between morning and afternoon using sample images from a training set. We demonstrate the learned transformation can be generalized from training data and show the resulting visual localization on a test set is improved relative to raw image comparison. The improvement in localization remains when the area is revisited several weeks later.

Multiplexed tandem polymerase chain reaction identifies strong expression of oestrogen receptor and Her-2 from single, formalin-fixed, paraffin-embedded breast cancer sections

Aim To establish the suitability of multiplex tandem polymerase chain reaction (MT-PCR) for rapid identification of oestrogen receptor (ER) and Her-2 status using a single, formalin-fixed, paraffin-embedded (FFPE) breast tumour section. Methods Tissue sections from 29 breast tumours were analysed by immunohistochemistry (IHC) and fluorescence in situ hybridisation (FISH). RNA extracted from 10μm FFPE breast tumour sections from 24 of 29 tumours (14 ER positive and 5 Her-2 positive) was analysed by MT-PCR. After establishing a correlation between IHC and/or FISH and MT-PCR results, the ER/Her-2 status of a further 32 randomly selected, archival breast tumour specimens was established by MT-PCR in a blinded fashion, and compared to IHC/FISH results. Results MT-PCR levels of ER and Her-2 showed good concordance with IHC and FISH results. Furthermore, among the ER positive tumours, MT-PCR provided a quantitative score with a high dynamic range. Threshold values obtained from this data set applied to 32 archival tumour specimens showed that tumours strongly positive for ER and/or Her-2 expression were easily identified by MT-PCR. Conclusion MT-PCR can provide rapid, sensitive and cost-effective analysis of FFPE material and may prove useful as triage to identify patients suited to endocrine or trastuzumab (Herceptin) treatment.

Near-miss event detection at railway level crossings

Recent modelling of socio-economic costs by the Australian railway industry in 2010 has estimated the cost of level crossing accidents to exceed AU$116 million annually. To better understand the causal factors of these accidents, a video analytics application is being developed to automatically detect near-miss incidents using forward facing videos from trains. As near-miss events occur more frequently than collisions, by detecting these occurrences there will be more safety data available for analysis. The application that is being developed will improve the objectivity of near-miss reporting by providing quantitative data about the position of vehicles at level crossings through the automatic analysis of video footage. In this paper we present a novel method for detecting near-miss occurrences at railway level crossings from video data of trains. Our system detects and localizes vehicles at railway level crossings. It also detects the position of railways to calculate the distance of the detected vehicles to the railway centerline. The system logs the information about the position of the vehicles and railway centerline into a database for further analysis by the safety data recording and analysis system, to determine whether or not the event is a near-miss. We present preliminary results of our system on a dataset of videos taken from a train that passed through 14 railway level crossings. We demonstrate the robustness of our system by showing the results of our system on day and night videos.

Fast unscented transformation-based transient stability margin estimation incorporating uncertainty of wind generation

Intermittent generation from wind farms leads to fluctuating power system operating conditions pushing the stability margin to its limits. The traditional way of determining the worst case generation dispatch for a system with several semi-scheduled wind generators yields a conservative solution. This paper proposes a fast estimation of the transient stability margin (TSM) incorporating the uncertainty of wind generation. First, the Kalman filter (KF) is used to provide linear estimation of system angle and then unscented transformation (UT) is used to estimate the distribution of the TSM. The proposed method is compared with the traditional Monte Carlo (MC) method and the effectiveness of the proposed approach is verified using Single Machine Infinite Bus (SMIB) and IEEE 14 generator Australian dynamic system. This method will aid grid operators to perform fast online calculations to estimate TSM distribution of a power system with high levels of intermittent wind generation.

Experiences of male patients and wife-caregivers in the first year post-discharge following minor stroke: A descriptive qualitative study

Background Most patients with minor stroke are discharged directly home from acute care, under the assumption that little will be required in the way of adaptation and adjustment because informal caregivers will manage the stroke recovery process. We explored male patients with minor stroke and their wife-caregivers' perceptions of factors affecting quality of life and caregiver strain encountered during the first year post-discharge. Methods Data were obtained from responses to two open-ended questions, part of quality of life and caregiver strain scales administered to participants in a larger descriptive study. Conventional content analysis was used to assess narrative accounts of living with minor stroke provided by 26 male patients and their wife-caregivers over a period of 1-year post-discharge. Results Two major themes that emerged from these data were 'being vulnerable' and 'realization'. Subthemes that arose within the vulnerability theme included changes to patients' masculine image and wife-caregivers' assumption of a hyper-vigilance role. In terms of 'realization' patients and their wife-caregivers shared 'loss' as well as 'changing self and relationships'. Patients in this study focused primarily on their physical recovery and their perceptions of necessary changes. Wife-caregivers were actively involved in managing the day-to-day demands that stroke placed on individual, family and social roles. Conclusions We conclude that patients and wife-caregivers expend considerable time and energy reestablishing control of their lives following minor stroke in an attempt to incorporate changes to self and their relationship into the fabric of their lives.

Non-parametric consistency test for multiple-sensing-modality data fusion

Fusing data from multiple sensing modalities, e.g. laser and radar, is a promising approach to achieve resilient perception in challenging environmental conditions. However, this may lead to \emph{catastrophic fusion} in the presence of inconsistent data, i.e. when the sensors do not detect the same target due to distinct attenuation properties. It is often difficult to discriminate consistent from inconsistent data across sensing modalities using local spatial information alone. In this paper we present a novel consistency test based on the log marginal likelihood of a Gaussian process model that evaluates data from range sensors in a relative manner. A new data point is deemed to be consistent if the model statistically improves as a result of its fusion. This approach avoids the need for absolute spatial distance threshold parameters as required by previous work. We report results from object reconstruction with both synthetic and experimental data that demonstrate an improvement in reconstruction quality, particularly in cases where data points are inconsistent yet spatially proximal.

Occupational injury risk among Australian paramedics: An analysis of national data

Objective To identify the occupational risks for Australian paramedics, by describing the rate of injuries and fatalities and comparing those rates with other reports. Design and participants Retrospective descriptive study using data provided by Safe Work Australia for the period 2000–2010. The subjects were paramedics who had been injured in the course of their duties and for whom a claim had been made for workers compensation payments. Main outcome measures Rates of injury calculated from the data provided. Results The risk of serious injury among Australian paramedics was found to be more than seven times higher than the Australian national average. The fatality rate for paramedics was about six times higher than the national average. On average, every 2 years during the study period, one paramedic died and 30 were seriously injured in vehicle crashes. Ten Australian paramedics were seriously injured each year as a result of an assault. The injury rate for paramedics was more than two times higher than the rate for police officers. Conclusions The high rate of occupational injuries and fatalities among paramedics is a serious public health issue. The risk of injury in Australia is similar to that in the United States. While it may be anticipated that injury rates would be higher as a result of the nature of the work and environment of paramedics, further research is necessary to identify and validate the strategies required to minimise the rates of occupational injury for paramedics.

Missing in space: An evaluation of imputation methods for missing data in spatial analysis of risk factors for type II diabetes

Background Spatial analysis is increasingly important for identifying modifiable geographic risk factors for disease. However, spatial health data from surveys are often incomplete, ranging from missing data for only a few variables, to missing data for many variables. For spatial analyses of health outcomes, selection of an appropriate imputation method is critical in order to produce the most accurate inferences. Methods We present a cross-validation approach to select between three imputation methods for health survey data with correlated lifestyle covariates, using as a case study, type II diabetes mellitus (DM II) risk across 71 Queensland Local Government Areas (LGAs). We compare the accuracy of mean imputation to imputation using multivariate normal and conditional autoregressive prior distributions. Results Choice of imputation method depends upon the application and is not necessarily the most complex method. Mean imputation was selected as the most accurate method in this application. Conclusions Selecting an appropriate imputation method for health survey data, after accounting for spatial correlation and correlation between covariates, allows more complete analysis of geographic risk factors for disease with more confidence in the results to inform public policy decision-making.

The hemispheric asymmetry of solar activity during the last century and the solar dynamo

We believe the Babcock-Leighton process of poloidal field generation to be the main source of irregularity in the solar cycle. The random nature of this process may make the poloidal field in one hemisphere stronger than that in the other hemisphere at the end of a cycle. We expect this to induce an asymmetry in the next sunspot cycle. We look for evidence of this in the observational data and then model it theoretically with our dynamo code. Since actual polar field measurements exist only from the 1970s, we use the polar faculae number data recorded by Sheeley (1991, 2008) as a proxy of the polar field and estimate the hemispheric asymmetry of the polar field in different solar minima during the major part of the twentieth century. This asymmetry is found to have a reasonable correlation with the asymmetry of the next cycle. We then run our dynamo code by feeding information about this asymmetry at the successive minima and compare the results with observational data. We find that the theoretically computed asymmetries of different cycles compare favorably with the observational data, with the correlation coefficient being 0.73. Due to the coupling between the two hemispheres, any hemispheric asymmetry tends to get attenuated with time. The hemispheric asymmetry of a cycle either from observational data or from theoretical calculations statistically tends to be less than the asymmetry in the polar field (as inferred from the faculae data) in the preceding minimum. This reduction factor turns out to be 0.43 and 0.51 respectively in observational data and theoretical simulations.

Stress analysis on human arterial plaques by fluid structure interactions - Multi-case study

Atherosclerotic plaque rupture has been extensively considered as the leading cause of death in the world. It is believed that high stress within plaque can be an important factor which can trigger the rupture of the plaque. High resolution multi-spectral magnetic resonance imaging (MRI) has allowed the plaque components (arterial wall, lipids, and fibrous cap) to be visualized in vivo [1]. The patient specific finite element model can be generated from the image data to perform stress analysis and provide critical information on understanding plaque rupture mechanisms [2]. The present work is to apply the procedure to a total of 14 patients (S1 ∼ S14), to study the stress distributions on carotid artery plaque reconstructed from multi-spectral magnetic resonance images, and the possible relationships between stress and plaque burdens.

On Visual Detection of Highly-occluded Objects for Harvesting Automation in Horticulture

Developing accurate and reliable crop detection algorithms is an important step for harvesting automation in horticulture. This paper presents a novel approach to visual detection of highly-occluded fruits. We use a conditional random field (CRF) on multi-spectral image data (colour and Near-Infrared Reflectance, NIR) to model two classes: crop and background. To describe these two classes, we explore a range of visual-texture features including local binary pattern, histogram of oriented gradients, and learn auto-encoder features. The pro-posed methods are evaluated using hand-labelled images from a dataset captured on a commercial capsicum farm. Experimental results are presented, and performance is evaluated in terms of the Area Under the Curve (AUC) of the precision-recall curves.Our current results achieve a maximum performance of 0.81AUC when combining all of the texture features in conjunction with colour information.

Stochastic growth models for analyzing crustacean data

The contemporary methodology for growth models of organisms is based on continuous trajectories and thus it hinders us from modelling stepwise growth in crustacean populations. Growth models for fish are normally assumed to follow a continuous function, but a different type of model is needed for crustacean growth. Crustaceans must moult in order for them to grow. The growth of crustaceans is a discontinuous process due to the periodical shedding of the exoskeleton in moulting. The stepwise growth of crustaceans through the moulting process makes the growth estimation more complex. Stochastic approaches can be used to model discontinuous growth or what are commonly known as "jumps" (Figure 1). However, in stochastic growth model we need to ensure that the stochastic growth model results in only positive jumps. In view of this, we will introduce a subordinator that is a special case of a Levy process. A subordinator is a non-decreasing Levy process, that will assist in modelling crustacean growth for better understanding of the individual variability and stochasticity in moulting periods and increments. We develop the estimation methods for parameter estimation and illustrate them with the help of a dataset from laboratory experiments. The motivational dataset is from the ornate rock lobster, Panulirus ornatus, which can be found between Australia and Papua New Guinea. Due to the presence of sex effects on the growth (Munday et al., 2004), we estimate the growth parameters separately for each sex. Since all hard parts are shed too often, the exact age determination of a lobster can be challenging. However, the growth parameters for the aforementioned moult processes from tank data being able to estimate through: (i) inter-moult periods, and (ii) moult increment. We will attempt to derive a joint density, which is made up of two functions: one for moult increments and the other for time intervals between moults. We claim these functions are conditionally independent given pre-moult length and the inter-moult periods. The variables moult increments and inter-moult periods are said to be independent because of the Markov property or conditional probability. Hence, the parameters in each function can be estimated separately. Subsequently, we integrate both of the functions through a Monte Carlo method. We can therefore obtain a population mean for crustacean growth (e. g. red curve in Figure 1). [GRAPHICS]

Robust estimation using the Huber function with a data-dependent tuning constant

Robust estimation often relies on a dispersion function that is more slowly varying at large values than the square function. However, the choice of tuning constant in dispersion functions may impact the estimation efficiency to a great extent. For a given family of dispersion functions such as the Huber family, we suggest obtaining the "best" tuning constant from the data so that the asymptotic efficiency is maximized. This data-driven approach can automatically adjust the value of the tuning constant to provide the necessary resistance against outliers. Simulation studies show that substantial efficiency can be gained by this data-dependent approach compared with the traditional approach in which the tuning constant is fixed. We briefly illustrate the proposed method using two datasets.

Estimating equations with nonignorably missing response data

Troxel, Lipsitz, and Brennan (1997, Biometrics 53, 857-869) considered parameter estimation from survey data with nonignorable nonresponse and proposed weighted estimating equations to remove the biases in the complete-case analysis that ignores missing observations. This paper suggests two alternative modifications for unbiased estimation of regression parameters when a binary outcome is potentially observed at successive time points. The weighting approach of Robins, Rotnitzky, and Zhao (1995, Journal of the American Statistical Association 90, 106-121) is also modified to obtain unbiased estimating functions. The suggested estimating functions are unbiased only when the missingness probability is correctly specified, and misspecification of the missingness model will result in biases in the estimates. Simulation studies are carried out to assess the performance of different methods when the covariate is binary or normal. For the simulation models used, the relative efficiency of the two new methods to the weighting methods is about 3.0 for the slope parameter and about 2.0 for the intercept parameter when the covariate is continuous and the missingness probability is correctly specified. All methods produce substantial biases in the estimates when the missingness model is misspecified or underspecified. Analysis of data from a medical survey illustrates the use and possible differences of these estimating functions.

Purification of immunoglobulins from chicken sera by thiophilic gel chromatography

Immunoglobulin Y is different from most of the other immunoglobulins because it does not bind protein A or protein G. Thiophilic gel chromatography has been successfully used to purify IgY from chicken egg yolk, but the technology has not previously been used to purify IgY from serum. In this research note, we describe the optimization of T-gel chromatography for purification of IgY from serum. Data are provided on the recovery and purity of IgY obtained using potassium sulfate buffers of different concentrations. Decreasing the strength of potassium sulfate buffer from 0.5 to 0.3 M did not alter the amount of IgY recovered but increased the purity. Using 0.3 M potassium sulphate, we recovered approximately 63.7% of the serum Ig as almost pure IgY.