Opening eyes to opera: the process of translation for blind and partially-sighted audiences

In today’s rapidly developing digital age and increasingly socially-aware society, the notion of media accessibility is evolving in response to shifting audience expectations. Performing arts and media, such as opera, are called upon to include all audiences, and related audiovisual translation methods are progressing in this direction. These comprise audio description and touch tours for the blind and partially-sighted, two relatively new translation modalities which are consumer-oriented and require an original research design for the analysis of the translation processes involved. This research design follows two fundamental principles: (1) audience reception studies should be an integral part of the investigation into the translation process; and (2) the translation process is regarded as a network. Therefore, this paper explores the unique translation processes of audio description and touch tours within the context of live opera from the perspective of actor-network theory and by providing an overview of a reception project. Through discussion of the methodology and findings, this paper addresses the question of the impact of audience reception on the translation process.

Opening Eyes to Opera: the process of translation for blind and partially-sighted audiences

In today’s rapidly developing digital age and increasingly socially-aware society, the notion of media accessibility is evolving in response to shifting audience expectations. Performing arts and media, such as opera, are called upon to include all audiences, and related audiovisual translation methods are progressing in this direction. These comprise audio description and touch tours for the blind and partially-sighted, two relatively new translation modalities which are consumer-oriented and require an original research design for the analysis of the translation processes involved. This research design follows two fundamental principles: (1) audience reception studies should be an integral part of the investigation into the translation process; and (2) the translation process is regarded as a network. Therefore, this chapter explores the unique translation processes of audio description and touch tours within the context of live opera from the perspective of actor-network theory and by providing an overview of a reception project. Through discussion of the methodology and findings, this chapter addresses the question of the impact of audience reception on the translation process.

Analogies between linguistics and information theory

An analogy is established between the syntagm and paradigm from Saussurean linguistics and the message and messages for selection from the information theory initiated by Claude Shannon. The analogy is pursued both as an end itself and for its analytic value in understanding patterns of retrieval from full text systems. The multivalency of individual words when isolated from their syntagm is contrasted with the relative stability of meaning of multi-word sequences, when searching ordinary written discourse. The syntagm is understood as the linear sequence of oral and written language. Saussureâ??s understanding of the word, as a unit which compels recognition by the mind, is endorsed, although not regarded as final. The lesser multivalency of multi-word sequences is understood as the greater determination of signification by the extended syntagm. The paradigm is primarily understood as the network of associations a word acquires when considered apart from the syntagm. The restriction of information theory to expression or signals, and its focus on the combinatorial aspects of the message, is sustained. The message in the model of communication in information theory can include sequences of written language. Shannonâ??s understanding of the written word, as a cohesive group of letters, with strong internal statistical influences, is added to the Saussurean conception. Sequences of more than one word are regarded as weakly correlated concatenations of cohesive units.

Learning Bayesian networks from data: An information-theory based approach

This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requiring only polynomial numbers of conditional independence (CI) tests in typical cases. We provide precise conditions that specify when these algorithms are guaranteed to be correct as well as empirical evidence (from real world applications and simulation tests) that demonstrates that these systems work efficiently and reliably in practice.

A Hybrid Forward Algorithm for RBF Neural Network Construction

This paper proposes a novel hybrid forward algorithm (HFA) for the construction of radial basis function (RBF) neural networks with tunable nodes. The main objective is to efficiently and effectively produce a parsimonious RBF neural network that generalizes well. In this study, it is achieved through simultaneous network structure determination and parameter optimization on the continuous parameter space. This is a mixed integer hard problem and the proposed HFA tackles this problem using an integrated analytic framework, leading to significantly improved network performance and reduced memory usage for the network construction. The computational complexity analysis confirms the efficiency of the proposed algorithm, and the simulation results demonstrate its effectiveness

Genetic algorithms for local controller network construction

Local Controller Networks (LCNs) provide nonlinear control by interpolating between a set of locally valid, subcontrollers covering the operating range of the plant. Constructing such networks typically requires knowledge of valid local models. This paper describes a new genetic learning approach to the construction of LCNs directly from the dynamic equations of the plant, or from modelling data. The advantage is that a priori knowledge about valid local models is not needed. In addition to allowing simultaneous optimisation of both the controller and validation function parameters, the approach aids transparency by ensuring that each local controller acts independently of the rest at its operating point. It thus is valuable for simultaneous design of the LCNs and identification of the operating regimes of an unknown plant. Application results from a highly nonlinear pH neutralisation process and its associated neural network representation are utilised to illustrate these issues.

Improving neural network training solutions using regularisation

This paper describes the application of regularisation to the training of feedforward neural networks, as a means of improving the quality of solutions obtained. The basic principles of regularisation theory are outlined for both linear and nonlinear training and then extended to cover a new hybrid training algorithm for feedforward neural networks recently proposed by the authors. The concept of functional regularisation is also introduced and discussed in relation to MLP and RBF networks. The tendency for the hybrid training algorithm and many linear optimisation strategies to generate large magnitude weight solutions when applied to ill-conditioned neural paradigms is illustrated graphically and reasoned analytically. While such weight solutions do not generally result in poor fits, it is argued that they could be subject to numerical instability and are therefore undesirable. Using an illustrative example it is shown that, as well as being beneficial from a generalisation perspective, regularisation also provides a means for controlling the magnitude of solutions. (C) 2001 Elsevier Science B.V. All rights reserved.