Young children’s use of a delayed video representation to solve a retrieval problem pertaining to self

The experiments reported here were concerned with the development of delayed self-recognition. Children were videotaped playing a game and were marked covertly with a sticker on their forehead while doing so. The findings, of both a cross-sectional sample and a prospective longitudinal one, revealed that 3- but not 2.5-year-old children reached to remove this sticker reliably during video playback only after they had been trained to use the video to guide their search for an object that was not directly visible to the unaided eye. It appears that by 3 years of age children understand that their briefly delayed self video-representation is related to their present self. In contrast, while 2.5-year-olds can use delayed vid of information to locate objects in space that cannot be seen by the unaided eye, they cannot use this type of information to locate an object that pertains to a part of self that is not directly visible, such as a sticker on one’s hair. The findings are discussed in terms of the emergence of an extended
sense of self.

Representation of female offender types within the pathways model of assault

Research into the heterogeneity of female violent offending is sparse, even though females constitute a growing part of the prisoner population. There is evidence that the majority of female violent offenders display over-controlled traits related to uncharacteristic offending. This hypothesis was explored through a multivariate model, the Pathways Model of Assault, with interview data from 17 female assault offenders. Five different offence types were displayed, reflecting the same offence styles as male offenders. However, only two participants displayed an over-controlled offence type. Qualitative differences within the data suggested that offender rehabilitation should be tailored specifically for different gender needs in addition to the needs suggested by the different offence types.

Diabetes MILES - Australia (Management and Impact for Long-Term Empowerment and Success) : methods and sample characteristics of a national survey of the psychological aspects of living with type 1 or type 2 diabetes in Australian adults

Background Successful management of diabetes requires attention to the behavioural, psychological and social aspects of this progressive condition. The Diabetes MILES (Management and Impact for Long-term Empowerment and Success) Study is an international collaborative. Diabetes MILES-Australia, the first Diabetes MILES initiative to be undertaken, was a national survey of adults living with type 1 or type 2 diabetes in Australia. The aim of this study was to gather data that will provide insights into how Australians manage their diabetes, the support they receive and the impact of diabetes on their lives, as well as to use the data to validate new diabetes outcome measures.

Methods The survey was designed to include a core set of self-report measures, as well as modules specific to diabetes type or management regimens. Other measures or items were included in only half of the surveys. Cognitive debriefing interviews with 20 participants ensured the survey content was relevant and easily understood. In July 2011, the survey was posted to 15,000 adults (aged 18-70 years) with type 1 or type 2 diabetes selected randomly from the National Diabetes Services Scheme (NDSS) database. An online version of the survey was advertised nationally. A total of 3,338 eligible Australians took part; most (70.4%) completed the postal survey. Respondents of both diabetes types and genders, and of all ages, were adequately represented in both the postal and online survey sub-samples. More people with type 2 diabetes than type 1 diabetes took part in Diabetes MILES-Australia (58.8% versus 41.2%). Most respondents spoke English as their main language, were married/in a de facto relationship, had at least a high school education, were occupied in paid work, had an annual household income > $AUS40,000, and lived in metropolitan areas.

Discussion A potential limitation of the study is the under-representation of respondents from culturally and linguistically diverse backgrounds (including Aboriginal and Torres Strait Islander origin). Diabetes MILES-Australia represents a major achievement in the study of diabetes in Australia, where for the first time, the focus is on psychosocial and behavioural aspects of this condition at a national level.

Symbolic representation and distributed matching strategies for schematics

This paper describes object-centered symbolic representation and distributed matching strategies of 3D objects in a schematic form which occur in engineering drawings and maps. The object-centered representation has a hierarchical structure and is constructed from symbolic representations of schematics. With this representation, two independent schematics representing the same object can be matched. We also consider matching strategies using distributed algorithms. The object recognition is carried out with two matching methods: (1) matching between an object model and observed data at the lowest level of the hierarchy, and (2) constraints propagation. The first is carried out with symbolic Hopfield-type neural networks and the second is achieved via hierarchical winner-takes-all algorithms

Generalised damage identification scheme via sparse representation

Structures inevitably deteriorate during their service lives. Therefore, the methods capable of identifying and assessing various damages in a structure timely and accurately have drawn increasing attention. From a broader perspective, structural damage identification problem can be regarded as a pattern recognition problem by using sparse representation techniques. The unknown signal/feature from a damaged structure can be associated to a known type of signal/feature in a “dictionary”, leading to damage identification. From this new angle, an innovative damage identification scheme has been proposed by the authors. In this paper, two important techniques of this scheme are further discussed, namely the construction of dictionary and the choice of parameters. The numerical simulated soil-pipe system is used for verifying the performance of the proposed method. The results demonstrate that this damage identification scheme will be a promising tool for structural health monitoring.

Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)

Electronic medical record (EMR) offers promises for novel analytics. However, manual feature engineering from EMR is labor intensive because EMR is complex - it contains temporal, mixed-type and multimodal data packed in irregular episodes. We present a computational framework to harness EMR with minimal human supervision via restricted Boltzmann machine (RBM). The framework derives a new representation of medical objects by embedding them in a low-dimensional vector space. This new representation facilitates algebraic and statistical manipulations such as projection onto 2D plane (thereby offering intuitive visualization), object grouping (hence enabling automated phenotyping), and risk stratification. To enhance model interpretability, we introduced two constraints into model parameters: (a) nonnegative coefficients, and (b) structural smoothness. These result in a novel model called eNRBM (EMR-driven nonnegative RBM). We demonstrate the capability of the eNRBM on a cohort of 7578 mental health patients under suicide risk assessment. The derived representation not only shows clinically meaningful feature grouping but also facilitates short-term risk stratification. The F-scores, 0.21 for moderate-risk and 0.36 for high-risk, are significantly higher than those obtained by clinicians and competitive with the results obtained by support vector machines.