Gifted young adolescents : voices of self

Despite the challenges that giftedness can add to self-formation during early adolescence, gifted young adolescents seldom are asked about their lives outside of counselling and educational contexts. The study considers the complexities that face gifted young adolescents in the process of self-discovery and self-representation, thereby building a case for seeking their own viewpoints. A guiding assumption for the study was that gifted young adolescents may respond positively to the opportunity to share their own perspectives and their own versions of “who they are”. The theoretical underpinnings for this study drew from Dialogical Self Theory. The study resides within an interactive view of self as a dynamic construction rather than a static state, where “who we are” is formed in everyday exchanges with self and others. Self-making as a process among gifted young adolescents is presented as an interactive network of “I” voices interpreted to reflect internal and external dialogue. In this way, self is understood within dialogical concepts of voices as multiple expressions. The study invited twelve gifted young adolescents to write freely about themselves over a six month period in an email journal project. Participants were recruited online and by word-of-mouth and they were able to negotiate their own levels of involvement. Access to the lives of individual young adolescents was sought in an out-of-school setting using narrative methods of personal writing in the form of journals sent as emails to the researcher. The role of the researcher was to act as a supportive listener who responded to participant-led emails and thereby facilitated the process of authoring that occurred across the data-gathering phase. The listening process involved responses that were affirming and designed to build trust. Data in the form of email texts were analysed using a close listening method that uncovered patterns of voices that were explicitly or subtly expressed by participants. The interpretation of voices highlighted the tensions and contradictions involved in the process of participants forming a “self” that emerged as multiple “I” voices. There were three key findings of the study. First, the gifted young adolescent participants each constructed a self around four key voices of Author, Achiever, Resistor/Co-operator and Self-Innovator. These voices were dialogical selfconstructions that showed multiplicity as a normal way of being. Second, the selfmaking processes of the gifted young adolescent participants were guided by a hierarchy of voices that were directed through self-awareness. Third, authoring in association with a responsive adult listener emerged as a dialogic space for promoting self-awareness and a language of self-expression among gifted young adolescents. The findings of the study contribute to knowledge about gifted young adolescents by presenting their own versions of “who” they are, perspectives that might differ from mainstream perceptions. Participants were shown to have highly diverse, complex and individual expressions that have implications for how well they are understood and supported by others. The use of email journals helped to create a synergy for self-disclosure and a safe space for self-expression where participants’ abilities to be themselves were encouraged. Increased self-awareness and selfknowledge among gifted young adolescents is vital to their self-formation and their management of self and others’ expectations. This study makes an original contribution to the field of self-study by highlighting the processes and complexities of young adolescents’ self-constructions. Through the innovative use of narrative methods and an inter-disciplinary approach, the voices of gifted young adolescents were privileged. At a practical level, the study can inform educators, policy-makers, parents and all those who seek to contribute to the well-being of gifted young adolescents.

Social network and aesthetic reflexivity in the creative industries

This article reviews some key critical writing about the commodification or exploitation of networked social relations in the creative industries. Through a comparative case study of networks in fashion and new media industries in the city of Manchester, UK, the article draws attention to the social, cultural and aesthetic aspects of the networks among creative practitioners. It argues that within the increasing commercialisation in the creative industries there are networked spaces within which non-instrumental values are created. The building of social networks reflects on the issue of how creatives perceive their work in these industries both economically and socially/culturally.

The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network

Sample complexity results from computational learning theory, when applied to neural network learning for pattern classification problems, suggest that for good generalization performance the number of training examples should grow at least linearly with the number of adjustable parameters in the network. Results in this paper show that if a large neural network is used for a pattern classification problem and the learning algorithm finds a network with small weights that has small squared error on the training patterns, then the generalization performance depends on the size of the weights rather than the number of weights. For example, consider a two-layer feedforward network of sigmoid units, in which the sum of the magnitudes of the weights associated with each unit is bounded by A and the input dimension is n. We show that the misclassification probability is no more than a certain error estimate (that is related to squared error on the training set) plus A3 √((log n)/m) (ignoring log A and log m factors), where m is the number of training patterns. This may explain the generalization performance of neural networks, particularly when the number of training examples is considerably smaller than the number of weights. It also supports heuristics (such as weight decay and early stopping) that attempt to keep the weights small during training. The proof techniques appear to be useful for the analysis of other pattern classifiers: when the input domain is a totally bounded metric space, we use the same approach to give upper bounds on misclassification probability for classifiers with decision boundaries that are far from the training examples.

Neural network learning:theoretical foundations

This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, Anthony and Bartlett develop a model of classification by real-output networks, and demonstrate the usefulness of classification with a "large margin." The authors explain the role of scale-sensitive versions of the Vapnik Chervonenkis dimension in large margin classification, and in real prediction. Key chapters also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient, constructive learning algorithms. The book is self-contained and accessible to researchers and graduate students in computer science, engineering, and mathematics

Overcoming the complexity of diagnostic problems due to sensor network architecture

In fault detection and diagnostics, limitations coming from the sensor network architecture are one of the main challenges in evaluating a system’s health status. Usually the design of the sensor network architecture is not solely based on diagnostic purposes, other factors like controls, financial constraints, and practical limitations are also involved. As a result, it quite common to have one sensor (or one set of sensors) monitoring the behaviour of two or more components. This can significantly extend the complexity of diagnostic problems. In this paper a systematic approach is presented to deal with such complexities. It is shown how the problem can be formulated as a Bayesian network based diagnostic mechanism with latent variables. The developed approach is also applied to the problem of fault diagnosis in HVAC systems, an application area with considerable modeling and measurement constraints.

Group maintenance scheduling : a case study for a pipeline network

This paper presents a group maintenance scheduling case study for a water distributed network. This water pipeline network presents the challenge of maintaining aging pipelines with the associated increases in annual maintenance costs. The case study focuses on developing an effective maintenance plan for the water utility. Current replacement planning is difficult as it needs to balance the replacement needs under limited budgets. A Maintenance Grouping Optimization (MGO) model based on a modified genetic algorithm was utilized to develop an optimum group maintenance schedule over a 20-year cycle. The adjacent geographical distribution of pipelines was used as a grouping criterion to control the searching space of the MGO model through a Judgment Matrix. Based on the optimum group maintenance schedule, the total cost was effectively reduced compared with the schedules without grouping maintenance jobs. This optimum result can be used as a guidance to optimize the current maintenance plan for the water utility.

Exploiting the 1,2,3-triazolium motif in anion-templated formation of a bromide-selective rotaxane host assembly

Stimulated by the efficacy of copper (I) catalysed Huisgen-type 1,3-dipolar cycloaddition of terminal alkynes and organic azides to generate 1,4-disubstituted 1,2,3-triazole derivatives, the importance of ‘click’ chemistry in the synthesis of organic and biological molecular systems is ever increasing.[1] The mild reaction conditions have also led to this reaction gaining favour in the construction of interlocked molecular architectures.[2-4] In the majority of cases however, the triazole group simply serves as a covalent linkage with no function in the resulting organic molecular framework. More recently a renewed interest has been shown in the transition metal coordination chemistry of triazole ligands.[3, 5, 6] In addition novel aryl macrocyclic and acyclic triazole based oligomers have been shown to recognise halide anions via cooperative triazole C5-H….anion hydrogen bonds.[7] In light of this it is surprising the potential anion binding affinity of the positively charged triazolium motif has not, with one notable exception,[8] been investigated. With the objective of manipulating the unique topological cavities of mechanically bonded molecules for anion recognition purposes, we have developed general methods of using anions to template the formation of interpenetrated and interlocked structures.[9-13] Herein we report the first examples of exploiting the 1,2,3-triazolium group in the anion templated formation of pseudorotaxane and rotaxane assemblies. In an unprecedented discovery the bromide anion is shown to be a superior templating reagent to chloride in the synthesis of a novel triazolium axle containing [2]rotaxane. Furthermore the resulting rotaxane interlocked host system exhibits the rare selectivity preference for bromide over chloride...