Theoretical foundations of neural networks

Neural networks have often been motivated by superficial analogy with biological nervous systems. Recently, however, it has become widely recognised that the effective application of neural networks requires instead a deeper understanding of the theoretical foundations of these models. Insight into neural networks comes from a number of fields including statistical pattern recognition, computational learning theory, statistics, information geometry and statistical mechanics. As an illustration of the importance of understanding the theoretical basis for neural network models, we consider their application to the solution of multi-valued inverse problems. We show how a naive application of the standard least-squares approach can lead to very poor results, and how an appreciation of the underlying statistical goals of the modelling process allows the development of a more general and more powerful formalism which can tackle the problem of multi-modality.

Hardware gimmick or cultural innovation? Technological, cultural, and social foundations of the Japanese video game industry

This paper examines the role of creative resources in the emergence of the Japanese video game industry. We argue that creative resources nurtured by popular cartoons and animation sector, combined with technological knowledge accumulated in the consumer electronics industry, facilitated the emergence of successful video game industry in Japan. First we trace the development of the industry from its origin to the rise of platform developers and software publishers. Then, knowledge and creative foundations that influenced the developmental trajectory of this industry are analyzed, with links to consumer electronics and in regards to cartoons and animation industry.

Response-based segmentation using Finite Mixture Partial Least Squares: theoretical foundations and an application to American Customer Satisfaction Index Data

When applying multivariate analysis techniques in information systems and social science disciplines, such as management information systems (MIS) and marketing, the assumption that the empirical data originate from a single homogeneous population is often unrealistic. When applying a causal modeling approach, such as partial least squares (PLS) path modeling, segmentation is a key issue in coping with the problem of heterogeneity in estimated cause-and-effect relationships. This chapter presents a new PLS path modeling approach which classifies units on the basis of the heterogeneity of the estimates in the inner model. If unobserved heterogeneity significantly affects the estimated path model relationships on the aggregate data level, the methodology will allow homogenous groups of observations to be created that exhibit distinctive path model estimates. The approach will, thus, provide differentiated analytical outcomes that permit more precise interpretations of each segment formed. An application on a large data set in an example of the American customer satisfaction index (ACSI) substantiates the methodology’s effectiveness in evaluating PLS path modeling results.

Knowledge:the foundations of knowledge

Knowledge is of crucial, and growing importance in social, political and economic relations in modern society. The range and variety of available knowledge dramatically enlarges the available options of social action. This five volume collection brings together a broad array of contributions from a variety of disciplines. Featuring essays from philosophers who have investigated the foundations of knowledge, and addressing different forms of knowledge in society such as common sense and practical knowledge, this collection also discusses the role of knowledge in economic process and gives attention to the role of expert knowledge in political decision making. Including a collection of articles from the sociology of knowledge and science, the set also provides a new introduction by the editors, making it a unique and invaluable research resource for both student and scholar.

The foundations of knowledge:a cross-cultural perspective

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Foundations of an intervention package to improve communication in residential care settings:a mixed methods study

Background: The prevalence of hearing loss is considerably higher in individuals in residential care than in people within the community-dwelling population, and yet hearing aids and hearing services are relatively underused. Care staff have a key role in supporting access to services. Objectives: This study identifies staff perspectives on hearing loss and their views about potential hearing service improvements. Study design: A four-stage mixed methods study was used, made up of qualitative interviews, observation, a survey and a stakeholder involvement meeting. Results: The qualitative stages indicated that staff were concerned about their levels of interaction with residents. Staff considered maximizing communication as part of their professional role. The quantitative survey indicated that these views were widely held by staff, and the stakeholder stage identified the need for social support and dedicated staff training opportunities. Conclusion: Care home staff regard communication as a shared issue. Future interventions could enhance access to hearing services and provide care home staff with training in hearing loss and hearing aid management. © 2013 Informa Healthcare.

Models@run.time:foundations, applications, and roadmaps

Traditionally, research on model-driven engineering (MDE) has mainly focused on the use of models at the design, implementation, and verification stages of development. This work has produced relatively mature techniques and tools that are currently being used in industry and academia. However, software models also have the potential to be used at runtime, to monitor and verify particular aspects of runtime behavior, and to implement self-* capabilities (e.g., adaptation technologies used in self-healing, self-managing, self-optimizing systems). A key benefit of using models at runtime is that they can provide a richer semantic base for runtime decision-making related to runtime system concerns associated with autonomic and adaptive systems. This book is one of the outcomes of the Dagstuhl Seminar 11481 on models@run.time held in November/December 2011, discussing foundations, techniques, mechanisms, state of the art, research challenges, and applications for the use of runtime models. The book comprises four research roadmaps, written by the original participants of the Dagstuhl Seminar over the course of two years following the seminar, and seven research papers from experts in the area. The roadmap papers provide insights to key features of the use of runtime models and identify the following research challenges: the need for a reference architecture, uncertainty tackled by runtime models, mechanisms for leveraging runtime models for self-adaptive software, and the use of models at runtime to address assurance for self-adaptive systems.