Optimum Quantum Error Recovery using Semidefinite Programming

Quantum error correction (QEC) is an essential element of physical quantum information processing systems. Most QEC efforts focus on extending classical error correction schemes to the quantum regime. The input to a noisy system is embedded in a coded subspace, and error recovery is performed via an operation designed to perfectly correct for a set of errors, presumably a large subset of the physical noise process. In this paper, we examine the choice of recovery operation. Rather than seeking perfect correction on a subset of errors, we seek a recovery operation to maximize the entanglement fidelity for a given input state and noise model. In this way, the recovery operation is optimum for the given encoding and noise process. This optimization is shown to be calculable via a semidefinite program (SDP), a well-established form of convex optimization with efficient algorithms for its solution. The error recovery operation may also be interpreted as a combining operation following a quantum spreading channel, thus providing a quantum analogy to the classical diversity combining operation.Comment: 7 pages, 3 figure

Socio-Technical Perspective on Managing Type II Diabetes

Social attributes such as education level, family history or place of residence all place a strong role in the probability of a person developing type II diabetes later in life. The aim of this paper is to develop a knowledge system based to use social attributes to estimate the prevalence of type II diabetes in a given area in Australia to support public health policymaking. The focus of this paper is towards answering the research question How can social determinants associated with type II diabetes, be used to incrementally develop a supporting knowledge-based system (KBS)? The contribution of this paper is two folds: 1. The problem domain is analysed and a suitable KBS development framework is chosen 2. A prototype is developed and presented. Initial results with preliminary data confirm the validity of the approach

Agency in Transport Service: Implications of Traveller Mode Choice Objective and Latent Attributes Using Random Parameter Logit Model

Abstract: This paper explains how principal-agent theory (PAT) can be used as an analytical tool to understand the traveller-Transport for NSW (TfNSW) relationship and minimise the agency problem in the relationship by examining traveller preferences for mode choices. The paper emphasises latent variables (LVs) and traditional objective attributes (TOAs) together during the choice process within the agency relationship, as a method by which the utility of the principal (traveller) can be maximised and evaluated using a discrete choice experiment, i.e. random parameter logit (RPL) model. The probability of car use is significantly higher than public transport, which indicates that an agency problem exists in the relationship and incorporating traveller preferences in the transport projects may minimise this problem. Citation: Anwar, A.H.M., Tieu, K., Gibson, P., Win, K.T. & Berryman, M.J. (2014). Agency in Transport Service: Implications of Traveller Mode Choice Objective and Latent Attributes Using Ransom Parameter Logit Model. In: Campbell P. and Perez P. (Eds), Proceedings of the International Symposium of Next Generation Infrastructure, 1-4 October 2013, SMART Infrastructure Facility, University of Wollongong, Australia

Numerical Flow Analysis of an Axial Flow Pump

This paper describes the detailed study of fluid flows in an axial pump that includes impeller and guide vanes. And the comparisons of flow simulations of the axial pump impeller with guide vanes and without guide vanes are carried out in this paper. In addition to this, the effect of number of guide blades on flow behaviours is analysed numerically. The computational results are performed by using one of CFD commercial software, Solidworks Flow Simulation. The input design data of the model pump are the flow rate of 0.2m3, head of 3m and the rotational speed of 1160 rpm. The outer and inner diameter of impeller is 0.3m and 0.15m respectively. . And the impeller with four blades is used in this paper. The guide blade number is varied to 5,7,9nbsp with the same input data and other geometric parameters keep constant. In this study, the nature of velocities and pressures in an axial flow pump is analysed. The comparisons are averaged flow velocities, static pressure rise, dynamic pressure and total pressure.nbs

Characteristics of Livestock Husbandry and Management Practice in the Central Dry Zone of Myanmar

The central dry zone of Myanmar is the area with the highest density of small-scale livestock farmers under harsh environmental condition. In this study, we describe and quantify ownership patterns for various livestock species and characterised management and husbandry practices of small-scale farmers. In addition, we identify the husbandry factors associated with selected outcome indicators, ‘herd or flock size’ and ‘purpose of rearing’. A total of 613 livestock farmers in 40 villages were interviewed. Multispecies rearing was common with 51.7% of farmers rearing more than one livestock species. Rearing animals to be sold as adults for slaughter (meat production) was more common for small ruminants (98.1%) and chickens (99.8%) compared to cattle (69.8%). Larger cattle herds were more likely to practice grazing (p < 0.001) and to employ labour from outside the household to manage cattle than medium or small herds (p = 0.03). Patterns of grazing differed significantly between seasons (p < 0.01) for cattle, but not for small ruminants and village chicken. Overall, multispecies rearing and species-specific husbandry practices are used to raise livestock under harsh environmental conditions. Our results reveal that herd/flock size and purpose of rearing across different livestock species were significantly associated with feeding and housing practices and experience of farmers

Characterizing and mapping cropping patterns in a complex agro-ecosystem: An iterative participatory mapping procedure using machine learning algorithms and MODIS vegetation indices

Accurate and up-to-date spatial agricultural information is essential for applications including agro-environmental assessment, crop management, and appropriate targeting of agricultural technologies. There is growing research interest in spatial analysis of agricultural ecosystems applying satellite remote sensing technologies. However, usability of information generated from many of remotely sensed data is often constrained by accuracy problems. This is of particular concern in mapping complex agro-ecosystems in countries where small farm holdings are dominated by diverse crop types. This study is a contribution to the ongoing efforts towards overcoming accuracy challenges faced in remote sensing of agricultural ecosystems. We applied time-series analysis of vegetation indices (Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) derived from the Moderate Resolution Imaging Spectrometer (MODIS) sensor to detect seasonal patterns of irrigated and rainfed cropping patterns in five townships in the Central Dry Zone of Myanmar, which is an important agricultural region of the country has been poorly mapped with respect to cropping practices. To improve mapping accuracy and map legend completeness, we implemented a combination of (i) an iterative participatory approach to field data collection and classification, (ii) the identification of appropriate size and types of predictor variables (VIs), and (iii) evaluation of the suitability of three Machine Learning algorithms: Support Vector Machine (SVM), Random Forest (RF), and C5.0 algorithms under varying training sample sizes. Through these procedures, we were able to progressively improve accuracy and achieve maximum overall accuracy of 95% When a small sized training dataset was used, accuracy achieved by RF was significantly higher compared to SVM and C5.0 (P < 0.01), but as sample size increased, accuracy differences among the three machine learning algorithms diminished. Accuracy achieved by use of NDVI was consistently better than that of EVI (P < 0.01). The maximum overall accuracy was achieved using RF and 8-days NDVI composites for three years of remote sensing data. In conclusion, our findings highlight the important role of participatory classification, especially in areas where cropping systems are highly diverse and differ over space and time. We also show that the choice of classifiers and size of predictor variables are essential and complementary to the participatory mapping approach in achieving desired accuracy of cropping pattern mapping in areas where other sources of spatial information are scarce

Risk Prediction Models for Colorectal Cancer: A Systematic Review.

Colorectal cancer is the second leading cause of cancer-related death in Europe and the United States. Survival is strongly related to stage at diagnosis and population-based screening reduces colorectal cancer incidence and mortality. Stratifying the population by risk offers the potential to improve the efficiency of screening. In this systematic review we searched Medline, EMBASE, and the Cochrane Library for primary research studies reporting or validating models to predict future risk of primary colorectal cancer for asymptomatic individuals. A total of 12,808 papers were identified from the literature search and nine through citation searching. Fifty-two risk models were included. Where reported (n = 37), half the models had acceptable-to-good discrimination (the area under the receiver operating characteristic curve, AUROC >0.7) in the derivation sample. Calibration was less commonly assessed (n = 21), but overall acceptable. In external validation studies, 10 models showed acceptable discrimination (AUROC 0.71-0.78). These include two with only three variables (age, gender, and BMI; age, gender, and family history of colorectal cancer). A small number of prediction models developed from case-control studies of genetic biomarkers also show some promise but require further external validation using population-based samples. Further research should focus on the feasibility and impact of incorporating such models into stratified screening programmes.J Usher-Smith is funded by a National Institute of Health Research (NIHR) Clinical Lectureship and F Walter by an NIHR Clinician Scientist award. J Emery is funded by an Australian National Health and Medical Research Council (NHMRC) Practitioner Fellowship. A Wong has an NHMRC Early Career Fellowship. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.This is the author accepted manuscript. The final version is available from American Association for Cancer Research via http://dx.doi.org/10.1158/1940-6207.CAPR-15-027