Contractile cochlear frame in the gecko Teratoscincus scincus

It is generally accepted that the cartilaginous frame of the reptilian cochlea has only a passive supportive function. In this study, a ribbon of contractile tissue was revealed within the cartilaginous frame of the cochlea of the gecko Teratoscincus scincus. It consisted of tightly packed cells and received an extensive blood supply. The cytoplasm of the cells was filled with cytoskeletal filaments 5-7 nm thick as revealed by electron microscopy. Isolated tissue permeabilized with Triton X-100 or glycerol reversibly contracted in the presence of ATP. Noradrenaline caused slow relaxation of the freshly isolated tissue placed in artificial perilymph. We suggest that slow motility of the contractile tissue may adjust passive cochlear mechanics to sounds of high intensities. J. Comp. Neurol. 461:539-547, 2003. © 2003 Wiley-Liss, Inc.

Unfreezing the frame: Functional fram analysis for journalists and journalism students

This paper presents two case studies that suggest, in different but complementary ways, that the critical tool of frame analysis (Entman, 2002) has a place not only in the analytical environments of critical media research and media studies classes, where it is commonly found, but also in the media-production oriented environments of skills-based journalism training and even the newsroom. The expectations and constraints of both the latter environments, however, necessitate forms of frame analysis that are quick and simple. While commercial pressures mean newsrooms and skills-based journalism-training environments are likely to allow only an oversimplified approach to frame analysis, we argue that even a simple understanding and analysis at the production end could help to shift framing in ways that not only improve the quality and depth of Australasian newspapers' news coverage, but increase reader satisfaction with media output.

Asian Australian Citizenship as a Frame Enactment in the Parlimentary 'First Speech'

The parliamentary first speech is a site of discursive privilege that offers each parliamentarian an opportunity to articulate the principles and aspirations that underpin her or his entry into public life. When utilised by parliamentarians of Asian Australian backgrounds, these speeches embody a unique opportunity to comprehend how ethnic identity is performed amidst the numerous, competing interests by which legislators are bound and challenged. The construction and representation of Asian Australian identity in these contexts provide a fascinating opportunity to understand the junctures between ethnicity and Australian citizenship. This essay explores how Asian Australians may be subject to forms of 'coercive mimeticism' in certain social sites, and also how these hegemonic pressures may simultaneously present 'frames of enactment' through their performance.

Knowledge maintenance and the frame problem

Knowledge maintenance is a major challenge for both knowledge management and the Semantic Web. Operating over the Semantic Web, there will be a network of collaborating agents, each with their own ontologies or knowledge bases. Change in the knowledge state of one agent may need to be propagated across a number of agents and their associated ontologies. The challenge is to decide how to propagate a change of knowledge state. The effects of a change in knowledge state cannot be known in advance, and so an agent cannot know who should be informed unless it adopts a simple ‘tell everyone – everything’ strategy. This situation is highly reminiscent of the classic Frame Problem in AI. We argue that for agent-based technologies to succeed, far greater attention must be given to creating an appropriate model for knowledge update. In a closed system, simple strategies are possible (e.g. ‘sleeping dog’ or ‘cheap test’ or even complete checking). However, in an open system where cause and effect are unpredictable, a coherent cost-benefit based model of agent interaction is essential. Otherwise, the effectiveness of every act of knowledge update/maintenance is brought into question.

Supporting interoperable interpolation: the INTAMAP approach

In many Environmental Information Systems the actual observations arise from a discrete monitoring network which might be rather heterogeneous in both location and types of measurements made. In this paper we describe the architecture and infrastructure for a system, developed as part of the EU FP6 funded INTAMAP project, to provide a service oriented solution that allows the construction of an interoperable, automatic, interpolation system. This system will be based on the Open Geospatial Consortium’s Web Feature Service (WFS) standard. The essence of our approach is to extend the GML3.1 observation feature to include information about the sensor using SensorML, and to further extend this to incorporate observation error characteristics. Our extended WFS will accept observations, and will store them in a database. The observations will be passed to our R-based interpolation server, which will use a range of methods, including a novel sparse, sequential kriging method (only briefly described here) to produce an internal representation of the interpolated field resulting from the observations currently uploaded to the system. The extended WFS will then accept queries, such as ‘What is the probability distribution of the desired variable at a given point’, ‘What is the mean value over a given region’, or ‘What is the probability of exceeding a certain threshold at a given location’. To support information-rich transfer of complex and uncertain predictions we are developing schema to represent probabilistic results in a GML3.1 (object-property) style. The system will also offer more easily accessible Web Map Service and Web Coverage Service interfaces to allow users to access the system at the level of complexity they require for their specific application. Such a system will offer a very valuable contribution to the next generation of Environmental Information Systems in the context of real time mapping for monitoring and security, particularly for systems that employ a service oriented architecture.

Parallel geostatistics for sparse and dense datasets

Very large spatially-referenced datasets, for example, those derived from satellite-based sensors which sample across the globe or large monitoring networks of individual sensors, are becoming increasingly common and more widely available for use in environmental decision making. In large or dense sensor networks, huge quantities of data can be collected over small time periods. In many applications the generation of maps, or predictions at specific locations, from the data in (near) real-time is crucial. Geostatistical operations such as interpolation are vital in this map-generation process and in emergency situations, the resulting predictions need to be available almost instantly, so that decision makers can make informed decisions and define risk and evacuation zones. It is also helpful when analysing data in less time critical applications, for example when interacting directly with the data for exploratory analysis, that the algorithms are responsive within a reasonable time frame. Performing geostatistical analysis on such large spatial datasets can present a number of problems, particularly in the case where maximum likelihood. Although the storage requirements only scale linearly with the number of observations in the dataset, the computational complexity in terms of memory and speed, scale quadratically and cubically respectively. Most modern commodity hardware has at least 2 processor cores if not more. Other mechanisms for allowing parallel computation such as Grid based systems are also becoming increasingly commonly available. However, currently there seems to be little interest in exploiting this extra processing power within the context of geostatistics. In this paper we review the existing parallel approaches for geostatistics. By recognising that diffeerent natural parallelisms exist and can be exploited depending on whether the dataset is sparsely or densely sampled with respect to the range of variation, we introduce two contrasting novel implementations of parallel algorithms based on approximating the data likelihood extending the methods of Vecchia [1988] and Tresp [2000]. Using parallel maximum likelihood variogram estimation and parallel prediction algorithms we show that computational time can be significantly reduced. We demonstrate this with both sparsely sampled data and densely sampled data on a variety of architectures ranging from the common dual core processor, found in many modern desktop computers, to large multi-node super computers. To highlight the strengths and weaknesses of the diffeerent methods we employ synthetic data sets and go on to show how the methods allow maximum likelihood based inference on the exhaustive Walker Lake data set.

Projected sequential Gaussian processes: flexible interpolation for large data sets

Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential framework for inference in such projected processes is presented, where the observations are considered one at a time. We introduce a C++ library for carrying out such projected, sequential estimation which adds several novel features. In particular we have incorporated the ability to use a generic observation operator, or sensor model, to permit data fusion. We can also cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the variogram parameters is based on maximum likelihood estimation. We illustrate the projected sequential method in application to synthetic and real data sets. We discuss the software implementation and suggest possible future extensions.