237 resultados para Analytic Reproducing Kernel


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Previous studies on lay theories of anorexia nervosa have examined the ‘accuracy’ of lay knowledge, and the identification of factors by family and friends that would encourage early interventions (Huon, Brown, & Morris, 1988, 7, 239–252; Murray, Touyz, & Beumont, 1990, 9, 87–93). In contrast to these approaches, we examine lay theories of anorexia nervosa using a critical psychology perspective. We argue that the use of a discourse analysis methodology enables the examination of the construction of lay theories through dominant concepts and ideas. Ten semi-structured interviews with five women and five men aged between 15 and 25 years were carried out. Participants were asked questions about three main aspects of anorexia nervosa: aetiology, treatment and relationship to gender. Each interview was analysed in terms of the structure, function and variability of discourse. Three discourses: sociocultural, individual and femininity, are discussed in relation to the interview questions. We conclude that, in this study, lay theories of anorexia nervosa were structured through key discourses that maintained a separation between sociocultural aspects of anorexia nervosa and individual psychology. This separation exists in dominant psychomedical conceptualizations of anorexia nervosa, reinforcing the concept that it is a form of psychopathology.

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Objectives: To identify the variety of versions of bulimia constructed by participants, to suggest functions and consequences of these constructions, and to examine the sociocultural ideologies evident in participants' discourse. Methods: Ten women and one man were interviewed about their experiences of bulimia. Transcribed interviews were analyzed using a discourse analytic approach. Results: Five dominant ways of talking about bulimia were identified: Individuals were constructed as victims of bulimia, women were constructed as victims of social stereotypes, bulimia was constructed as a damaging action one performs on oneself, bulimia was constructed as a personality trait of individuals, and bulimia was marginalized as abnormal and disgusting. Discussion: Sociocultural ideologies evident in participants' accounts included the valuing of individual will-power and self-mastery and the construction of a mind-body dichotomy entailing the need to control the latter. The analysis emphasizes the importance of considering the sociocultural context within which psychological problems occur.

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The absence of qualitative analysis in mainstream research on eating disorders is discussed in the following article as being a weakness in developing theory and clinical practice. This article includes an analysis of interviews with British healthcare workers who manage anorexic patients. This analysis presents an example of qualitative methodology in the form of discourse analysis, which is argued to provide a systematic, yet flexible approach to research on eating disorders. The overwhelming prevalence of anorexia nervosa in women is specifically examined within the context of the identification of the "discourse of femininity. " The research findings are discussed in relation to the use of discursive practices that contribute to the maintenance and reproduction of clinical processes and their relative efficacy.

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This project was a step forward in developing intrusion detection systems in distributed environments such as web services. It investigates a new approach of detection based on so-called "taint-marking" techniques and introduces a theoretical framework along with its implementation in the Linux kernel.

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Traumatic experiences can have a powerful impact on individuals and communities but the relationship between perceptions of beneficial and pathological outcomes are not known. Therefore, this meta-analysis examined both the strength and the linearity of the relationship between symptoms of posttraumatic stress disorder (PTSD) and perceptions of posttraumatic growth (PTG) as well as identifying the potential moderating roles of trauma type and age. Literature searches of all languages were conducted using the ProQuest, Wiley Interscience, ScienceDirect, Informaworld and Web of Science databases. Linear and quadratic (curvilinear) rs as well as βs were analysed. Forty-two studies (N=11, 469) that examined both PTG and symptoms of PTSD were included in meta-analytic calculations. The combined studies yielded a significant linear relationship between PTG and PTSD symptoms (r=.315, CI = 0.299, 0.331), but also a significantly stronger (as tested by Fisher’s transformation) curvilinear relationship (r=.372, CI = 0.353, 0.391). The strength and linearity of these relationships differed according to trauma type and age. The results remind those working with traumatised people that positive and negative post-trauma outcomes can co-occur. A focus only on PTSD symptoms only may limit or slow recovery and mask the potential for growth.

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A critical requirement for safe autonomous navigation of a planetary rover is the ability to accurately estimate the traversability of the terrain. This work considers the problem of predicting the attitude and configuration angles of the platform from terrain representations that are often incomplete due to occlusions and sensor limitations. Using Gaussian Processes (GP) and exteroceptive data as training input, we can provide a continuous and complete representation of terrain traversability, with uncertainty in the output estimates. In this paper, we propose a novel method that focuses on exploiting the explicit correlation in vehicle attitude and configuration during operation by learning a kernel function from vehicle experience to perform GP regression. We provide an extensive experimental validation of the proposed method on a planetary rover. We show significant improvement in the accuracy of our estimation compared with results obtained using standard kernels (Squared Exponential and Neural Network), and compared to traversability estimation made over terrain models built using state-of-the-art GP techniques.

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Identifying appropriate decision criteria and making optimal decisions in a structured way is a complex process. This paper presents an approach for doing this in the form of a hybrid Quality Function Deployment (QFD) and Cybernetic Analytic Network Process (CANP) model for project manager selection. This involves the use of QFD to translate the owner's project management expectations into selection criteria and the CANP to weight the expectations and selection criteria. The supermatrix approach then prioritises the candidates with respect to the overall decision-making goal. A case study is used to demonstrate the use of the model in selecting a renovation project manager. This involves the development of 18 selection criteria in response to the owner's three main expectations of time, cost and quality.

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This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all multicategory support vector machines (OAA MCSVMs). Finally, each support vector machine is individually trained with its own feature vector that includes the most discriminative fault features, offering the highest classification performance. In this study, the effectiveness of the proposed GA-based kernel discriminative feature analysis and the classification ability of individually trained OAA MCSVMs are addressed in terms of average classification accuracy. In addition, the proposedGA- based kernel discriminative feature analysis is compared with four other state-of-the-art feature analysis approaches. Experimental results indicate that the proposed approach is superior to other feature analysis methodologies, yielding an average classification accuracy of 98.06% and 94.49% under rotational speeds of 50 revolutions-per-minute (RPM) and 80 RPM, respectively. Furthermore, the individually trained MCSVMs with their own optimal fault features based on the proposed GA-based kernel discriminative feature analysis outperform the standard OAA MCSVMs, showing an average accuracy of 98.66% and 95.01% for bearings under rotational speeds of 50 RPM and 80 RPM, respectively.

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Meta-analyses estimate a statistical effect size for a test or an analysis by combining results from multiple studies without necessarily having access to each individual study's raw data. Multi-site meta-analysis is crucial for imaging genetics, as single sites rarely have a sample size large enough to pick up effects of single genetic variants associated with brain measures. However, if raw data can be shared, combining data in a "mega-analysis" is thought to improve power and precision in estimating global effects. As part of an ENIGMA-DTI investigation, we use fractional anisotropy (FA) maps from 5 studies (total N=2, 203 subjects, aged 9-85) to estimate heritability. We combine the studies through meta-and mega-analyses as well as a mixture of the two - combining some cohorts with mega-analysis and meta-analyzing the results with those of the remaining sites. A combination of mega-and meta-approaches may boost power compared to meta-analysis alone.

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Objective This study explores the spatiotemporal variations of suicide across Australia from 1986 to 2005, discusses the reasons for dynamic changes, and considers future suicide research and prevention strategies. Design Suicide (1986–2005) and population data were obtained from the Australian Bureau of Statistics. A series of analyses were conducted to examine the suicide pattern by sex, method and age group over time and geography. Results Differences in suicide rates across sex, age groups and suicide methods were found across geographical areas. Male suicides were mainly completed by hanging, firearms, gases and self-poisoning. Female suicides were primarily completed by hanging and self-poisoning. Suicide rates were higher in rural areas than in urban areas (capital cities and regional centres). Suicide rates by firearms were higher in rural areas than in urban areas, while the pattern for self-poisoning showed the reverse trend. Suicide rates had relatively stable trend for the total population and those aged between 15 and 54, while suicide decreased among 55 years and over during the study period. There was a decrease in suicides by firearms during the study period especially after 1996 when a new firearm control law was implemented, while suicide by hanging continued to increase. Areas with a high proportion of indigenous population (eg, northwest of Queensland and top north of the Northern Territory) had shown a substantial increase in suicide incidence after 1995. Conclusions Suicide rates varied over time and space and across sexes, age groups and suicide methods. This study provides detailed patterns of suicide to inform suicide control and prevention strategies for specific subgroups and areas of high and increased risk.

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In this paper, we aim at predicting protein structural classes for low-homology data sets based on predicted secondary structures. We propose a new and simple kernel method, named as SSEAKSVM, to predict protein structural classes. The secondary structures of all protein sequences are obtained by using the tool PSIPRED and then a linear kernel on the basis of secondary structure element alignment scores is constructed for training a support vector machine classifier without parameter adjusting. Our method SSEAKSVM was evaluated on two low-homology datasets 25PDB and 1189 with sequence homology being 25% and 40%, respectively. The jackknife test is used to test and compare our method with other existing methods. The overall accuracies on these two data sets are 86.3% and 84.5%, respectively, which are higher than those obtained by other existing methods. Especially, our method achieves higher accuracies (88.1% and 88.5%) for differentiating the α + β class and the α/β class compared to other methods. This suggests that our method is valuable to predict protein structural classes particularly for low-homology protein sequences. The source code of the method in this paper can be downloaded at http://math.xtu.edu.cn/myphp/math/research/source/SSEAK_source_code.rar.

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This paper examines Initial Teacher Education students’ experiences of participation in health and physical education (HPE) subject department offices and the impact on their understandings and identity formation. Pierre Bourdieu’s concepts of habitus, field, and practice along with Wenger’s communities of practice form the theoretical frame used in the paper. Data were collected using surveys and interviews with student‐teachers following their teaching practicum and analysed using coding and constant comparison. Emergent themes revealed students’ participation in masculine‐dominated sports, gendered body constructions, and repertoires of masculine domination. Findings are discussed in relation to their impact on student‐teachers’ learning, identity formation, and marginalizing practices in the department offices. Implications for teacher education and HPE are explored.

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Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

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Fleck and Johnson (Int. J. Mech. Sci. 29 (1987) 507) and Fleck et al. (Proc. Inst. Mech. Eng. 206 (1992) 119) have developed foil rolling models which allow for large deformations in the roll profile, including the possibility that the rolls flatten completely. However, these models require computationally expensive iterative solution techniques. A new approach to the approximate solution of the Fleck et al. (1992) Influence Function Model has been developed using both analytic and approximation techniques. The numerical difficulties arising from solving an integral equation in the flattened region have been reduced by applying an Inverse Hilbert Transform to get an analytic expression for the pressure. The method described in this paper is applicable to cases where there is or there is not a flat region.