Active SLAM for autonomous underwater exploration

Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater vehicles, that faces exploration combining mapping, active localization, and view planning in a unified way. We propose an exploration framework, based on an active SLAM strategy, that combines three main elements: a view planner, an iterative closest point algorithm (ICP)-based pose-graph SLAM algorithm, and an action selection mechanism that makes use of the joint map and state entropy reduction. To demonstrate the benefits of the active SLAM strategy, several tests were conducted with the Girona 500 AUV, both in simulation and in the real world. The article shows how the proposed framework makes it possible to plan exploratory trajectories that keep the vehicle’s uncertainty bounded; thus, creating more consistent maps.Peer ReviewedPostprint (published version

Study of the Impact of the ACA Implementation in Kentucky: Semi-Annual Report

This report was produced by the State Health Access Data Assistance Center (SHADAC) at the University of Minnesota as part of a mixed-methods study, Study of the Impact of the Affordable Care Act (ACA) Implementation in Kentucky, funded by the Foundation for a Healthy Kentucky (Foundation). As part of this project, SHADAC will use semi-annual and annual reports to document the impact of the ACA in Kentucky using a set of indicators agreed upon by the Foundation and its ACA Impact Study Oversight Committee (see Appendix I for a complete list of indicators). These reports will track change in the indicators throughout the duration of this 34-month study (March 2015 through January 2018), and will include comparisons of Kentucky metrics with the U.S. and other states.The purpose of the first semi-annual report, "Baseline Data for the Implementation of the ACA in Kentucky," is to describe the baseline status of the healthcare situation in Kentucky prior to ACA implementation. The report presents baseline data for all study indicators, under the five study domains: coverage, access, cost, quality, and health outcomes. We use calendar year 2012 data as our baseline because it pre-dates the first ACA enrollment period that began in October 2013 and because the 2012 data are available for most of the indicators

Establishing the extent of malaria transmission and challenges facing pre-elimination in the Republic of Djibouti.

BACKGROUND: Countries aiming for malaria elimination require a detailed understanding of the current intensity of malaria transmission within their national borders. National household sample surveys are now being used to define infection prevalence but these are less efficient in areas of exceptionally low endemicity. Here we present the results of a national malaria indicator survey in the Republic of Djibouti, the first in sub-Saharan Africa to combine parasitological and serological markers of malaria, to evaluate the extent of transmission in the country and explore the potential for elimination. METHODS: A national cross-sectional household survey was undertaken from December 2008 to January 2009. A finger prick blood sample was taken from randomly selected participants of all ages to examine for parasitaemia using rapid diagnostic tests (RDTs) and confirmed using Polymerase Chain Reaction (PCR). Blood spots were also collected on filter paper and subsequently used to evaluate the presence of serological markers (combined AMA-1 and MSP-119) of Plasmodium falciparum exposure. Multivariate regression analysis was used to determine the risk factors for P. falciparum infection and/or exposure. The Getis-Ord G-statistic was used to assess spatial heterogeneity of combined infections and serological markers. RESULTS: A total of 7151 individuals were tested using RDTs of which only 42 (0.5%) were positive for P. falciparum infections and confirmed by PCR. Filter paper blood spots were collected for 5605 individuals. Of these 4769 showed concordant optical density results and were retained in subsequent analysis. Overall P. falciparum sero-prevalence was 9.9% (517/4769) for all ages; 6.9% (46/649) in children under the age of five years; and 14.2% (76/510) in the oldest age group (≥50 years). The combined infection and/or antibody prevalence was 10.5% (550/4769) and varied from 8.1% to 14.1% but overall regional differences were not statistically significant (χ2=33.98, p=0.3144). Increasing age (p<0.001) and decreasing household wealth status (p<0.001) were significantly associated with increasing combined P. falciparum infection and/or antibody prevalence. Significant P. falciparum hot spots were observed in Dikhil region. CONCLUSION: Malaria transmission in the Republic of Djibouti is very low across all regions with evidence of micro-epidemiological heterogeneity and limited recent transmission. It would seem that the Republic of Djibouti has a biologically feasible set of pre-conditions for elimination, however, the operational feasibility and the potential risks to elimination posed by P. vivax and human population movement across the sub-region remain to be properly established

Disease Surveillance Networks Initiative Global: Final Evaluation

In August 2009, the Rockefeller Foundation commissioned an independent external evaluation of the Disease Surveillance Networks (DSN) Initiative in Asia, Africa, and globally. This report covers the results of the global component of the summative and prospective1 evaluation, which had the following objectives:[1] Assessment of performance of the DSN Initiative, focused on its relevance, effectiveness/impact, and efficiency within the context of the Foundation's initiative support.[2] Assessment of the DSN Initiative's underlying hypothesis: robust trans-boundary, multi-sectoral/cross-disciplinary collaborative networks lead to improved disease surveillance and response.[3] Assessment of the quality of Foundation management (value for money) for the DSN Initiative.[4] Contribute to the field of philanthropy by:a. Demonstrating the use of evaluations in grantmaking, learning and knowledge management; andb. Informing the field of development evaluation about methods and models to measure complex networks

Third sector accounting and accountability in Australia: anything but a level playing field

This research report seeks to understand why some Australian not-for-profit organisations make voluntary financial disclosures beyond their basic statutory obligations. Introduction This paper surveys previous work on voluntary information disclosures in accounting reports of Australian Not-for-Profit organisations (NFPs). This is new research and is a part of a project to evolve a comprehensive explanation of why Australian NFPs disclose what they do disclose; and to capture and explain patterns of variations between NFPs between what they regard to disclose and the type of information they disclose. To accomplish this, first some background information about the NFP sector are considered. Then, the Australian NFP sector is reviewed. Third, the information needs of some key stakeholders are briefly discussed. Next, the research methodology where a literature survey which looks at not just disclosures to NFPs but to the commercial sector that are plausibly &nbsp

Mitigating Gender Bias in Machine Learning Data Sets

Artificial Intelligence has the capacity to amplify and perpetuate societal biases and presents profound ethical implications for society. Gender bias has been identified in the context of employment advertising and recruitment tools, due to their reliance on underlying language processing and recommendation algorithms. Attempts to address such issues have involved testing learned associations, integrating concepts of fairness to machine learning and performing more rigorous analysis of training data. Mitigating bias when algorithms are trained on textual data is particularly challenging given the complex way gender ideology is embedded in language. This paper proposes a framework for the identification of gender bias in training data for machine learning.The work draws upon gender theory and sociolinguistics to systematically indicate levels of bias in textual training data and associated neural word embedding models, thus highlighting pathways for both removing bias from training data and critically assessing its impact.Comment: 10 pages, 5 figures, 5 Tables, Presented as Bias2020 workshop (as part of the ECIR Conference) - http://bias.disim.univaq.i