Feature overlap slows lexical selection: Evidence from the picture–word interference paradigm

How does the presence of a categorically related word influence picture naming latencies? In order to test competitive and noncompetitive accounts of lexical selection in spoken word production, we employed the picture–word interference (PWI) paradigm to investigate how conceptual feature overlap influences naming latencies when distractors are category coordinates of the target picture. Mahon et al. (2007. Lexical selection is not by competition: A reinterpretation of semantic interference and facilitation effects in the picture-word interference paradigm. Journal of Experimental Psychology. Learning, Memory, and Cognition, 33(3), 503–535. doi:10.1037/0278-7393.33.3.503) reported that semantically close distractors (e.g., zebra) facilitated target picture naming latencies (e.g., HORSE) compared to far distractors (e.g., whale). We failed to replicate a facilitation effect for within-category close versus far target–distractor pairings using near-identical materials based on feature production norms, instead obtaining reliably larger interference effects (Experiments 1 and 2). The interference effect did not show a monotonic increase across multiple levels of within-category semantic distance, although there was evidence of a linear trend when unrelated distractors were included in analyses (Experiment 2). Our results show that semantic interference in PWI is greater for semantically close than for far category coordinate relations, reflecting the extent of conceptual feature overlap between target and distractor. These findings are consistent with the assumptions of prominent competitive lexical selection models of speech production.

Working the story : News curation in social media as a second save of citizen journalism

For the first decade of its existence, the concept of citizen journalism has described an approach which was seen as a broadening of the participant base in journalistic processes, but still involved only a comparatively small subset of overall society – for the most part, citizen journalists were news enthusiasts and “political junkies” (Coleman, 2006) who, as some exasperated professional journalists put it, “wouldn’t get a job at a real newspaper” (The Australian, 2007), but nonetheless followed many of the same journalistic principles. The investment – if not of money, then at least of time and effort – involved in setting up a blog or participating in a citizen journalism Website remained substantial enough to prevent the majority of Internet users from engaging in citizen journalist activities to any significant extent; what emerged in the form of news blogs and citizen journalism sites was a new online elite which for some time challenged the hegemony of the existing journalistic elite, but gradually also merged with it. The mass adoption of next-generation social media platforms such as Facebook and Twitter, however, has led to the emergence of a new wave of quasi-journalistic user activities which now much more closely resemble the “random acts of journalism” which JD Lasica envisaged in 2003. Social media are not exclusively or even predominantly used for citizen journalism; instead, citizen journalism is now simply a by-product of user communities engaging in exchanges about the topics which interest them, or tracking emerging stories and events as they happen. Such platforms – and especially Twitter with its system of ad hoc hashtags that enable the rapid exchange of information about issues of interest – provide spaces for users to come together to “work the story” through a process of collaborative gatewatching (Bruns, 2005), content curation, and information evaluation which takes place in real time and brings together everyday users, domain experts, journalists, and potentially even the subjects of the story themselves. Compared to the spaces of news blogs and citizen journalism sites, but also of conventional online news Websites, which are controlled by their respective operators and inherently position user engagement as a secondary activity to content publication, these social media spaces are centred around user interaction, providing a third-party space in which everyday as well as institutional users, laypeople as well as experts converge without being able to control the exchange. Drawing on a number of recent examples, this article will argue that this results in a new dynamic of interaction and enables the emergence of a more broadly-based, decentralised, second wave of citizen engagement in journalistic processes.

Creative adaptations: Integrating Feldenkrais principles in contemporary dance technique to facilitate the transition into tertiary dance education

As conservatoire-style dance teaching has traditionally utilised a hierarchical approach through which the student must conform to the ideal requirements of the conventional technique, current discourse is beginning to question how dance training can develop technical acuity without stifling students' ability to engage creatively. In recent years, there has been growing interest in the field of somatics and its relationship to tertiary dance training due to the understanding that this approach supports creative autonomy by radically repositioning the student's relationship to embodied learning, skill acquisition, enquiry and performance. This research addresses an observable disjuncture between the skills of dancers graduating from tertiary training and Australian dance industry needs, which increasingly demand the co-creative input of the dancer in choreographic practice. Drawing from Action Research, this paper will discuss a project which introduces somatic learning approaches, primarily from Feldenkrais Method and Hanna Somatics, to first-year dance students in their transition into tertiary education. This paper acknowledges previous research undertaken, most specifically the Somdance Manual by the University of Western Sydney, while directing focus to the first-year student transition from private dance studio training into the pre-professional arena.

Principles of taxation law 2015 [8th edition]

"Taxation law can be an incredibly complex subject to absorb, particularly when time is limited. Written specifically for students, Principles of Taxation Law 2014 brings much needed clarity to this area of law. Utilising many methods to make this often daunting subject achievable, particular features of the 2014 edition include: seven parts: overview and structure, principles of income, deductions and offsets, timing issues, investment and business entities, tax avoidance and administration, and indirect taxes; clearly structured chapters within those parts grouped under helpful headings;flowcharts, diagrams and tables, end of chapter practice questions, and case summaries; an appendix containing all of the up to date and relevant rates; and the online self-testing component mentor, which provides questions for students of both business and law. Every major aspect of the Australian tax system is covered, with chapters on topics such as goods and services tax, superannuation, offsets, partnerships, capital gains tax, trusts, company tax and tax administration.All chapters have been thoroughly revised"-- Publishers website

A multi-criteria analysis approach for ranking and selection of microorganisms for the production of oils for biodiesel production

Oleaginous microorganisms have potential to be used to produce oils as alternative feedstock for biodiesel production. Microalgae (Chlorella protothecoides and Chlorella zofingiensis), yeasts (Cryptococcus albidus and Rhodotorula mucilaginosa), and fungi (Aspergillus oryzae and Mucor plumbeus) were investigated for their ability to produce oil from glucose, xylose and glycerol. Multi-criteria analysis (MCA) using analytic hierarchy process (AHP) and preference ranking organization method for the enrichment of evaluations (PROMETHEE) with graphical analysis for interactive aid (GAIA), was used to rank and select the preferred microorganisms for oil production for biodiesel application. This was based on a number of criteria viz., oil concentration, content, production rate and yield, substrate consumption rate, fatty acids composition, biomass harvesting and nutrient costs. PROMETHEE selected A. oryzae, M. plumbeus and R. mucilaginosa as the most prospective species for oil production. However, further analysis by GAIA Webs identified A. oryzae and M. plumbeus as the best performing microorganisms.

Quality-aware review selection based on product feature taxonomy

As of today, user-generated information such as online reviews has become increasingly significant for customers in decision making process. Meanwhile, as the volume of online reviews proliferates, there is an insistent demand to help the users tackle the information overload problem. In order to extract useful information from overwhelming reviews, considerable work has been proposed such as review summarization and review selection. Particularly, to avoid the redundant information, researchers attempt to select a small set of reviews to represent the entire review corpus by preserving its statistical properties (e.g., opinion distribution). However, one significant drawback of the existing works is that they only measure the utility of the extracted reviews as a whole without considering the quality of each individual review. As a result, the set of chosen reviews may consist of low-quality ones even its statistical property is close to that of the original review corpus, which is not preferred by the users. In this paper, we proposed a review selection method which takes review quality into consideration during the selection process. Specifically, we examine the relationships between product features based upon a domain ontology to capture the review characteristics based on which to select reviews that have good quality and preserve the opinion distribution as well. Our experimental results based on real world review datasets demonstrate that our proposed approach is feasible and able to improve the performance of the review selection effectively.

Genome-wide association study using extreme truncate selection identifies novel genes affecting bone mineral density and fracture risk

Osteoporotic fracture is a major cause of morbidity and mortality worldwide. Low bone mineral density (BMD) is a major predisposing factor to fracture and is known to be highly heritable. Site-, gender-, and age-specific genetic effects on BMD are thought to be significant, but have largely not been considered in the design of genome-wide association studies (GWAS) of BMD to date. We report here a GWAS using a novel study design focusing on women of a specific age (postmenopausal women, age 55-85 years), with either extreme high or low hip BMD (age- and gender-adjusted BMD z-scores of +1.5 to +4.0, n = 1055, or -4.0 to -1.5, n = 900), with replication in cohorts of women drawn from the general population (n = 20,898). The study replicates 21 of 26 known BMD-associated genes. Additionally, we report suggestive association of a further six new genetic associations in or around the genes CLCN7, GALNT3, IBSP, LTBP3, RSPO3, and SOX4, with replication in two independent datasets. A novel mouse model with a loss-of-function mutation in GALNT3 is also reported, which has high bone mass, supporting the involvement of this gene in BMD determination. In addition to identifying further genes associated with BMD, this study confirms the efficiency of extreme-truncate selection designs for quantitative trait association studies. © 2011 Duncan et al.

Channel selection in the short-time modulation domain for distant speech recognition

Automatic speech recognition from multiple distant micro- phones poses significant challenges because of noise and reverberations. The quality of speech acquisition may vary between microphones because of movements of speakers and channel distortions. This paper proposes a channel selection approach for selecting reliable channels based on selection criterion operating in the short-term modulation spectrum domain. The proposed approach quantifies the relative strength of speech from each microphone and speech obtained from beamforming modulations. The new technique is compared experimentally in the real reverb conditions in terms of perceptual evaluation of speech quality (PESQ) measures and word error rate (WER). Overall improvement in recognition rate is observed using delay-sum and superdirective beamformers compared to the case when the channel is selected randomly using circular microphone arrays.

IgE-Associated IGHV Genes from Venom and Peanut Allergic Individuals Lack Mutational Evidence of Antigen Selection

Antigen selection of B cells within the germinal center reaction generally leads to the accumulation of replacement mutations in the complementarity-determining regions (CDRs) of immunoglobulin genes. Studies of mutations in IgE-associated VDJ gene sequences have cast doubt on the role of antigen selection in the evolution of the human IgE response, and it may be that selection for high affinity antibodies is a feature of some but not all allergic diseases. The severity of IgE-mediated anaphylaxis is such that it could result from higher affinity IgE antibodies. We therefore investigated IGHV mutations in IgE-associated sequences derived from ten individuals with a history of anaphylactic reactions to bee or wasp venom or peanut allergens. IgG sequences, which more certainly experience antigen selection, served as a control dataset. A total of 6025 unique IgE and 5396 unique IgG sequences were generated using high throughput 454 pyrosequencing. The proportion of replacement mutations seen in the CDRs of the IgG dataset was significantly higher than that of the IgE dataset, and the IgE sequences showed little evidence of antigen selection. To exclude the possibility that 454 errors had compromised analysis, rigorous filtering of the datasets led to datasets of 90 core IgE sequences and 411 IgG sequences. These sequences were present as both forward and reverse reads, and so were most unlikely to include sequencing errors. The filtered datasets confirmed that antigen selection plays a greater role in the evolution of IgG sequences than of IgE sequences derived from the study participants.

Artificial Neural Network Based Automatic Emergency Landing Site Selection for UAVs and Highly Automated Aircraft

In this report an artificial neural network (ANN) based automated emergency landing site selection system for unmanned aerial vehicle (UAV) and general aviation (GA) is described. The system aims increase safety of UAV operation by emulating pilot decision making in emergency landing scenarios using an ANN to select a safe landing site from available candidates. The strength of an ANN to model complex input relationships makes it a perfect system to handle the multicriteria decision making (MCDM) process of emergency landing site selection. The ANN operates by identifying the more favorable of two landing sites when provided with an input vector derived from both landing site's parameters, the aircraft's current state and wind measurements. The system consists of a feed forward ANN, a pre-processor class which produces ANN input vectors and a class in charge of creating a ranking of landing site candidates using the ANN. The system was successfully implemented in C++ using the FANN C++ library and ROS. Results obtained from ANN training and simulations using randomly generated landing sites by a site detection simulator data verify the feasibility of an ANN based automated emergency landing site selection system.

Implementing best practice principles in the delivery of a Learner Driver Mentor Program in rural Queensland: A case study report

Learner Driver Mentor Programs (LDMPs) assist disadvantaged learner drivers to gain supervised on-road driving experience by providing access to vehicles and volunteer mentors. In the absence of existing research investigating the implementation of Best Practice principles in LDMPs, this case study examines successful program operation in the context of a rural town setting. The study is based on an existing Best Practice model for LDMPs, and triangulation of data from a mentor focus group (n = 7), interviews with program stakeholders (n = 9), and an in-depth interview with the site-based program development officer. The data presented is based upon selected findings of the broader evaluation study. Preliminary findings regarding driving session management, support of mentors and mentees, and building and maintaining relationships with program stakeholders, are discussed. Key findings relate to the importance of relationships in engagement with the program and collaborating across sectors to achieve a range of positive outcomes for learners. The findings highlight the need for the program to be relevant and responsive to the requirements of the population and the context in which it is operating.

Influence of road traffic environment and mobile phone distraction on the speed selection behaviour of young drivers

Travel speed is one of the most critical parameters for road safety; the evidence suggests that increased vehicle speed is associated with higher crash risk and injury severity. Both naturalistic and simulator studies have reported that drivers distracted by a mobile phone select a lower driving speed. Speed decrements have been argued to be a risk compensatory behaviour of distracted drivers. Nonetheless, the extent and circumstances of the speed change among distracted drivers are still not known very well. As such, the primary objective of this study was to investigate patterns of speed variation in relation to contextual factors and distraction. Using the CARRS-Q high-fidelity Advanced Driving Simulator, the speed selection behaviour of 32 drivers aged 18-26 years was examined in two phone conditions: baseline (no phone conversation) and handheld phone operation. The simulator driving route contained five different types of road traffic complexities, including one road section with a horizontal S curve, one horizontal S curve with adjacent traffic, one straight segment of suburban road without traffic, one straight segment of suburban road with traffic interactions, and one road segment in a city environment. Speed deviations from the posted speed limit were analysed using Ward’s Hierarchical Clustering method to identify the effects of road traffic environment and cognitive distraction. The speed deviations along curved road sections formed two different clusters for the two phone conditions, implying that distracted drivers adopt a different strategy for selecting driving speed in a complex driving situation. In particular, distracted drivers selected a lower speed while driving along a horizontal curve. The speed deviation along the city road segment and other straight road segments grouped into a different cluster, and the deviations were not significantly different across phone conditions, suggesting a negligible effect of distraction on speed selection along these road sections. Future research should focus on developing a risk compensation model to explain the relationship between road traffic complexity and distraction.

Model selection with misspecified spatial covariance structure

Spatial data analysis has become more and more important in the studies of ecology and economics during the last decade. One focus of spatial data analysis is how to select predictors, variance functions and correlation functions. However, in general, the true covariance function is unknown and the working covariance structure is often misspecified. In this paper, our target is to find a good strategy to identify the best model from the candidate set using model selection criteria. This paper is to evaluate the ability of some information criteria (corrected Akaike information criterion, Bayesian information criterion (BIC) and residual information criterion (RIC)) for choosing the optimal model when the working correlation function, the working variance function and the working mean function are correct or misspecified. Simulations are carried out for small to moderate sample sizes. Four candidate covariance functions (exponential, Gaussian, Matern and rational quadratic) are used in simulation studies. With the summary in simulation results, we find that the misspecified working correlation structure can still capture some spatial correlation information in model fitting. When the sample size is large enough, BIC and RIC perform well even if the the working covariance is misspecified. Moreover, the performance of these information criteria is related to the average level of model fitting which can be indicated by the average adjusted R square ( [GRAPHICS] ), and overall RIC performs well.

Intra-cluster correlation structure in longitudinal data analysis: Selection criteria and misspecification tests

Selection criteria and misspecification tests for the intra-cluster correlation structure (ICS) in longitudinal data analysis are considered. In particular, the asymptotical distribution of the correlation information criterion (CIC) is derived and a new method for selecting a working ICS is proposed by standardizing the selection criterion as the p-value. The CIC test is found to be powerful in detecting misspecification of the working ICS structures, while with respect to the working ICS selection, the standardized CIC test is also shown to have satisfactory performance. Some simulation studies and applications to two real longitudinal datasets are made to illustrate how these criteria and tests might be useful.