Graph-based concept weighting for medical information retrieval

This paper presents a graph-based method to weight medical concepts in documents for the purposes of information retrieval. Medical concepts are extracted from free-text documents using a state-of-the-art technique that maps n-grams to concepts from the SNOMED CT medical ontology. In our graph-based concept representation, concepts are vertices in a graph built from a document, edges represent associations between concepts. This representation naturally captures dependencies between concepts, an important requirement for interpreting medical text, and a feature lacking in bag-of-words representations. We apply existing graph-based term weighting methods to weight medical concepts. Using concepts rather than terms addresses vocabulary mismatch as well as encapsulates terms belonging to a single medical entity into a single concept. In addition, we further extend previous graph-based approaches by injecting domain knowledge that estimates the importance of a concept within the global medical domain. Retrieval experiments on the TREC Medical Records collection show our method outperforms both term and concept baselines. More generally, this work provides a means of integrating background knowledge contained in medical ontologies into data-driven information retrieval approaches.

Robots move : bootstrapping the development of object representations using sensorimotor coordination

This paper is concerned with the unsupervised learning of object representations by fusing visual and motor information. The problem is posed for a mobile robot that develops its representations as it incrementally gathers data. The scenario is problematic as the robot only has limited information at each time step with which it must generate and update its representations. Object representations are refined as multiple instances of sensory data are presented; however, it is uncertain whether two data instances are synonymous with the same object. This process can easily diverge from stability. The premise of the presented work is that a robot's motor information instigates successful generation of visual representations. An understanding of self-motion enables a prediction to be made before performing an action, resulting in a stronger belief of data association. The system is implemented as a data-driven partially observable semi-Markov decision process. Object representations are formed as the process's hidden states and are coordinated with motor commands through state transitions. Experiments show the prediction process is essential in enabling the unsupervised learning method to converge to a solution - improving precision and recall over using sensory data alone.

Does legal heritage affect obesity? The channel of motor vehicle dependence

We find a robust relationship between motor vehicle ownership, its interaction with legal heritage and obesity in OECD countries. Our estimates indicate that an increase of 100 motor vehicles per thousand residents is associated with about a 6% point increase in obesity in common law countries, whereas it has a much smaller or insignificant impact in civil law countries. These relations hold whether we examine trend data and simple correlations, or conduct cross-section or panel data regression analysis. Our results suggest that obesity rises with motor vehicle ownership in countries following a common law tradition where individual liberty is encouraged, whereas the link is small or statistically non-existent in countries with a civil law background where the rights of the individual tend to be circumscribed by the power of the state.

The moderating effect of knowledge sharing on the relationship between manufacturing activities and business performance

This paper investigates the critical role of knowledge sharing (KS) in leveraging manufacturing activities, namely integrated supplier management (ISM) and new product development (NPD) to improve business performance (BP) within the context of Taiwanese electronic manufacturing companies. The research adopted a sequential mixed method research design, which provided both quantitative empirical evidence as well as qualitative insights, into the moderating effect of KS on the relationships between these two core manufacturing activities and BP. First, a questionnaire survey was administered, which resulted in a sample of 170 managerial and technical professionals providing their opinions on KS, NPD and ISM activities and the BP level within their respective companies. On the basis of the collected data, factor analysis was used to verify the measurement model, followed by correlation analysis to explore factor interrelationships, and finally moderated regression analyses to extract the moderating effects of KS on the relationships of NPD and ISM with BP. Following the quantitative study, six semi-structured interviews were conducted to provide qualitative in-depth insights into the value added from KS practices to the targeted manufacturing activities and the extent of its leveraging power. Results from quantitative statistical analysis indicated that KS, NPD and ISM all have a significant positive impact on BP. Specifically, IT infrastructure and open communication were identified as the two types of KS practices that could facilitate enriched supplier evaluation and selection, empower active employee involvement in the design process, and provide support for product simplification and the modular design process, thereby improving manufacturing performance and strengthening company competitiveness. The interviews authenticated many of the empirical findings, suggesting that in the contemporary manufacturing context KS has become an integral part of many ISM and NPD activities and when embedded properly can lead to an improvement in BP. The paper also highlights a number of useful implications for manufacturing companies seeking to leverage their BP through innovative and sustained KS practices.

The digital transformation of 21st century news journalism

This paper deals with the transformations that have occurred in news journalism worldwide in the early 21st century. I argue that they havebeen the most significant changes to the profession for 100 years, and the challenges facing the news media industry in responding to them are substantial, as are those facing journalism education. This argument is developed in relation to the crisis of the newspaper business model, and why social media, blogging and citizen journalism have not filled the gap left by the withdrawal of resources from traditional journalism. It also draws upon Wikileaks as a case study in debates about computational and data-driven journalism, and whether large-scale "leaks" of electronic documents may be the future of investigative journalism.

A note on the ‘Linsanity’ of measuring the relative efficiency of National Basketball Association guards

This note examines the productive efficiency of 62 starting guards during the 2011/12 National Basketball Association (NBA) season. This period coincides with the phenomenal and largely unanticipated performance of New York Knicks’ starting point guard Jeremy Lin and the attendant public and media hype known as Linsanity. We employ a data envelopment analysis (DEA) approach that includes allowance for an undesirable output, here turnovers per game, with the desirable outputs of points, rebounds, assists, steals and blocks per game and an input of minutes per game. The results indicate that depending upon the specification, between 29% and 42% of NBA guards are fully efficient, including Jeremy Lin, with a mean inefficiency of 3.7% and 19.2%. However, while Jeremy Lin is technically efficient, he seldom serves as a benchmark for inefficient players, at least when compared with established players such as Chris Paul and Dwayne Wade. This suggests the uniqueness of Jeremy Lin's productive solution and may explain why his unique style of play, encompassing individual brilliance, unselfish play and team leadership, is of such broad public appeal.

Nonparametric semantic segmentation for 3D street scenes

In this paper we propose a method to generate a large scale and accurate dense 3D semantic map of street scenes. A dense 3D semantic model of the environment can significantly improve a number of robotic applications such as autonomous driving, navigation or localisation. Instead of using offline trained classifiers for semantic segmentation, our approach employs a data-driven, nonparametric method to parse scenes which easily scale to a large environment and generalise to different scenes. We use stereo image pairs collected from cameras mounted on a moving car to produce dense depth maps which are combined into a global 3D reconstruction using camera poses from stereo visual odometry. Simultaneously, 2D automatic semantic segmentation using a nonparametric scene parsing method is fused into the 3D model. Furthermore, the resultant 3D semantic model is improved with the consideration of moving objects in the scene. We demonstrate our method on the publicly available KITTI dataset and evaluate the performance against manually generated ground truth.

Reconceptualizing statistical learning in the early years

This chapter argues for the need to restructure children’s statistical experiences from the beginning years of formal schooling. The ability to understand and apply statistical reasoning is paramount across all walks of life, as seen in the variety of graphs, tables, diagrams, and other data representations requiring interpretation. Young children are immersed in our data-driven society, with early access to computer technology and daily exposure to the mass media. With the rate of data proliferation have come increased calls for advancing children’s statistical reasoning abilities, commencing with the earliest years of schooling (e.g., Langrall et al. 2008; Lehrer and Schauble 2005; Shaughnessy 2010; Whitin and Whitin 2011). Several articles (e.g., Franklin and Garfield 2006; Langrall et al. 2008) and policy documents (e.g., National Council of Teachers ofMathematics 2006) have highlighted the need for a renewed focus on this component of early mathematics learning, with children working mathematically and scientifically in dealing with realworld data. One approach to this component in the beginning school years is through data modelling (English 2010; Lehrer and Romberg 1996; Lehrer and Schauble 2000, 2007)...

Monitoring the price and affordability of foods and diets globally

Food prices and food affordability are important determinants of food choices, obesity and non-communicable diseases. As governments around the world consider policies to promote the consumption of healthier foods, data on the relative price and affordability of foods, with a particular focus on the difference between ‘less healthy’ and ‘healthy’ foods and diets, are urgently needed. This paper briefly reviews past and current approaches to monitoring food prices, and identifies key issues affecting the development of practical tools and methods for food price data collection, analysis and reporting. A step-wise monitoring framework, including measurement indicators, is proposed. ‘Minimal’ data collection will assess the differential price of ‘healthy’ and ‘less healthy’ foods; ‘expanded’ monitoring will assess the differential price of ‘healthy’ and ‘less healthy’ diets; and the ‘optimal’ approach will also monitor food affordability, by taking into account household income. The monitoring of the price and affordability of ‘healthy’ and ‘less healthy’ foods and diets globally will provide robust data and benchmarks to inform economic and fiscal policy responses. Given the range of methodological, cultural and logistical challenges in this area, it is imperative that all aspects of the proposed monitoring framework are tested rigorously before implementation.

Transparency tools in gene patenting for informing policy and practice

In the recent decision Association for Molecular Pathology v. Myriad Genetics1, the US Supreme Court held that naturally occurring sequences from human genomic DNA are not patentable subject matter. Only certain complementary DNAs (cDNA), modified sequences and methods to use sequences are potentially patentable. It is likely that this distinction will hold for all DNA sequences, whether animal, plant or microbial2. However, it is not clear whether this means that other naturally occurring informational molecules, such as polypeptides (proteins) or polysaccharides, will also be excluded from patents. The decision underscores a pressing need for precise analysis of patents that disclose and reference genetic sequences, especially in the claims. Similarly, data sets, standards compliance and analytical tools must be improved—in particular, data sets and analytical tools must be made openly accessible—in order to provide a basis for effective decision making and policy setting to support biological innovation. Here, we present a web-based platform that allows such data aggregation, analysis and visualization in an open, shareable facility. To demonstrate the potential for the extension of this platform to global patent jurisdictions, we discuss the results of a global survey of patent offices that shows that much progress is still needed in making these data freely available for aggregation in the first place.

Cost and environmental efficiency of rice farms in South Korea

We examine cost and nutrient use efficiency of farms and determine the cost to move farms to nutrient-efficient operation using Data Envelopment Analysis (DEA) with a dataset of 96 rice farms in Gangwon province of South Korea from 2003 to 2007. Our findings show that improvements in technical efficiency would result in both lower production costs and better environmental performance. It is, however, not costless for farms to move from their current operation to the environmentally efficient operation. On average, this movement would increase production costs by 119% but benefit the water system through an approximately 69% reduction in eutrofying power (EP). The average estimated cost of each EP kg of aggregate nutrient reduction is approximately one thousand two hundred won. For technically efficient farms, there is a trade-off between cost and environmental efficiency. We also find that the environmental performance of farms varies across farms and regions. We suggest that agri-environmental policies should be (re)designed to improve both cost and environmental performance of rice farms.

Study of the effect of M&A on real estate industry firms' performance

This paper proposes to adopt data envelopment analysis (DEA) based Malmquist total factor productivity (TFP) indices methods to evaluate the effect of mergers and acquisitions (M&As) on acquirers in short-term and long-term window. Based on analyzing 32 M&A deals conducted by Chinese real estate firms from 2000-2011, the study result demonstrate that the effect of M&A on developers’ performance is positive. Through M&A, the developers’ Malmquist TFP experienced a steady growth; their technology has got noticeable progress immediately after acquisition; and their technical efficiency has suffered a slight decrease in short-term after acquisition, but then achieved marked increase in the long-term when realization of integration and synergy. However, there is no evidence that the real estate firms have achieved scale efficiency improvement after M&A in either short-term or long-term.

Variations in acculturation and Australian physical education teacher education students' receptiveness to an alternative pedagogical approach to games teaching

Background The development of intelligent, thinking performers as a central theme in Physical Education curriculum documents worldwide has highlighted the need for an evolution of teaching styles from the dominant reproductive approach. This has prompted an Australian university to change the content and delivery of a games unit within their Physical Education Teacher Education (PETE) course and adopt a productive student centred approach that is compatible with current curriculum directives. The significance of prospective physical educators’ biographies on their receptiveness to this pedagogical innovation was studied to help recognise and understand potential differences and subsequently guide programme development to help improve the impact of teacher education. Purpose To investigate whether past school and sporting experiences are powerful influences on Australian PETE recruits’ initial perspectives about effective physical education teaching practice and their receptiveness to an alternative pedagogical approach. Participants and Setting 49 first year pre-service PETE students (53% male; 47% female; mean age 18.88 ± 1.57 years) undertaking a compulsory unit on games teaching at an Australian university volunteered to take part in the study and were grouped according to their highest level of representation in games, either school/club (n=13), regional (n=20), or state/national (n=16). Students experienced the constraints-led approach as learners and teachers during an 8-week games unit informed by nonlinear pedagogy and underpinned by motor learning theory. Data collection and Analysis Prior to the commencement of the unit participants completed part A of a two part mixed response questionnaire aimed at gathering data about their physical education and sporting background. The data were summarised using descriptive statistics. Pre and post intervention, participants completed part B responding, via Likert Scale with their opinion of the importance of each sub-component of the traditional reproductive style for an effective games teaching session. This resulted in a traditional reproductive games teaching belief score. For each sub-component, participants were invited to respond in more detail to justify their opinions. A one-way between groups analysis of variance (ANOVA), Tukey’s HSD Post Hoc Test and a two - tailed, paired samples t test were used to analyse the quantitative data. Content analysis was used to analyse the qualitative data. Findings The traditional, reproductive approach was the most frequently reported teaching approach used by the physical education teachers and sports coaches of participants in all groups. Prior to the commencement of the alternate games unit, participants in each representative level group held very strong custodial traditional reproductive games teaching beliefs. After experiencing the alternative games unit there were statistically significant differences in the traditional reproductive games teaching belief mean scores for each group, This combined with participants’ qualitative responses indicated a receptiveness to the alternative pedagogy. Conclusions The results of this present study show that, contrary to previous research undertaken in North America, in Australia, it is possible for PETE educators to change beliefs in order to overcome the constraint of acculturation and provide PETE students with the knowledge, understanding and belief in an alternate approach to teaching games in physical education compatible with curriculum documents.

eHealth-as-a-Service (eHaaS) : towards universal stakeholder engagement

With the introduction of the Personally Controlled Health Record (PCEHR), the Australian public is being asked to accept greater responsibility for their healthcare by taking an active role in the management of personal health information. Although well designed, constructed and intentioned, policy and privacy concerns have resulted in an eHealth model that may impact future health sharing requirements. Hence, as a case study for a consumer eHealth initative in the Australian context, eHealth-as-a-Service (eHaaS) serves as a disruptive step in in the aggregation and transformation of health information for use as real-world knowledge. The strategic value of extending the community Health Record Bank (HRB) model lies in the ability to automatically draw on a multitude of relevant data repositories and sources to create a single source of the truth and to engage market forces to create financial sustainability. The opportunity to transform the beleaguered Australian PCEHR into a realisable and sustainable technology consumption model for patient safety is explored. Moreover, the current clerical focus of healthcare practitioners acting in the role of de facto record keepers is renegotiated to establish a shared knowledge creation landscape of action for safer patient interventions. To achieve this potential however requires a platform that will facilitate efficient and trusted unification of all health information available in real-time across the continuum of care. eHaaS provides a sustainable environment and encouragement to realise this potential.

Degradation modeling and monitoring of machines using operation-specific hidden Markov models

In this paper, a novel data-driven approach to monitoring of systems operating under variable operating conditions is described. The method is based on characterizing the degradation process via a set of operation-specific hidden Markov models (HMMs), whose hidden states represent the unobservable degradation states of the monitored system while its observable symbols represent the sensor readings. Using the HMM framework, modeling, identification and monitoring methods are detailed that allow one to identify a HMM of degradation for each operation from mixed-operation data and perform operation-specific monitoring of the system. Using a large data set provided by a major manufacturer, the new methods are applied to a semiconductor manufacturing process running multiple operations in a production environment.