A scatterometer neural network sensor model with input noise

The ERS-1 satellite carries a scatterometer which measures the amount of radiation scattered back toward the satellite by the ocean's surface. These measurements can be used to infer wind vectors. The implementation of a neural network based forward model which maps wind vectors to radar backscatter is addressed. Input noise cannot be neglected. To account for this noise, a Bayesian framework is adopted. However, Markov Chain Monte Carlo sampling is too computationally expensive. Instead, gradient information is used with a non-linear optimisation algorithm to find the maximum em a posteriori probability values of the unknown variables. The resulting models are shown to compare well with the current operational model when visualised in the target space.

Neural network regression with input uncertainty

It is generally assumed when using Bayesian inference methods for neural networks that the input data contains no noise or corruption. For real-world (errors in variable) problems this is clearly an unsafe assumption. This paper presents a Bayesian neural network framework which allows for input noise given that some model of the noise process exists. In the limit where this noise process is small and symmetric it is shown, using the Laplace approximation, that there is an additional term to the usual Bayesian error bar which depends on the variance of the input noise process. Further, by treating the true (noiseless) input as a hidden variable and sampling this jointly with the network's weights, using Markov Chain Monte Carlo methods, it is demonstrated that it is possible to infer the unbiassed regression over the noiseless input.

A scatterometer neural network sensor model with input noise

The ERS-1 satellite carries a scatterometer which measures the amount of radiation scattered back toward the satellite by the ocean's surface. These measurements can be used to infer wind vectors. The implementation of a neural network based forward model which maps wind vectors to radar backscatter is addressed. Input noise cannot be neglected. To account for this noise, a Bayesian framework is adopted. However, Markov Chain Monte Carlo sampling is too computationally expensive. Instead, gradient information is used with a non-linear optimisation algorithm to find the maximum em a posteriori probability values of the unknown variables. The resulting models are shown to compare well with the current operational model when visualised in the target space.

A linguistic resurrection? Aller + infinitive in narrative past contexts

This article studies the go periphrasis emerging in Contemporary French narrations and compares it with the narrative go periphrasis found in Middle French.

Author identification, idiolect and linguistic uniqueness

For forty years linguists have talked about idiolect and the uniqueness of individual utterances. This article explores how far these two concepts can be used to answer certain questions about the authorship of written documents—for instance how similar can two student essays be before one begins to suspect plagiarism? The article examines two ways of measuring similarity: the proportion of shared vocabulary and the number and length of shared phrases, and illustrates with examples drawn from both actual criminal court cases and incidents of student plagiarism. The article ends by engaging with Solan and Tiersma's contribution to this volume and considering whether such forensic linguistic evidence would be acceptable in American courts as well as how it might successfully be presented to a lay audience.

Foreign direct investment and local input linkages in Malaysia

This article examines variations in local input linkages in foreign transnational corporations in Malaysia. The extent to which transnational corporations foster such linkages, particularly in a developing host economy, has become an important issue for policy makers and others concerned with the long-term benefits associated with foreign direct investment. This article employs a unique data set, covering inward investors in the electrical and electronics industry, and analyzes in detail the determinants of variations in local input uses. The article develops a model of local input linkages, based on a transaction-cost framework using firm-specific factors, such as nationality of ownership, the age of the plant and its technology, and the extent to which firms employ locally recruited managers and engineers. In addition, the impacts of various policy measures on local input levels are discussed, and also the importance of the original motivation for investing in Malaysia. The article demonstrates that policy initiatives that target particular outcomes, such as stimulating exports or technology transfer, will result in a greater beneficial impact on the host country economy than more generic subsidies.

Using a group support system to aid input-output identification in DEA

In data envelopment analysis (DEA), operating units are compared on their outputs relative to their inputs. The identification of an appropriate input-output set is of decisive significance if assessment of the relative performance of the units is not to be biased. This paper reports on a novel approach used for identifying a suitable input-output set for assessing central administrative services at universities. A computer-supported group support system was used with an advisory board to enable the analysts to extract information pertaining to the boundaries of the unit of assessment and the corresponding input-output variables. The approach provides for a more comprehensive and less inhibited discussion of input-output variables to inform the DEA model. © 2005 Operational Research Society Ltd. All rights reserved.

A sensory-linguistic approach to normal and impaired reading development

In this chapter we outline a sensory-linguistic approach to the, study of reading skill development. We call this a sensory-linguistic approach because the focus of interest is on the relationship between basic sensory processing skills and the ability to extract efficiently the orthographic and phonological information available in text during reading. Our review discusses how basic sensory processing deficits are associated with developmental dyslexia, and how these impairments may degrade word-decoding skills. We then review studies that demonstrate a more direct relationship between sensitivity to particular types of auditory and visual stimuli and the normal development of literacy skills. Specifically, we suggest that the phonological and orthographic skills engaged while reading are constrained by the ability to detect and discriminate dynamic stimuli in the auditory and visual systems respectively.

Constructions of active womanhood and new femininities: from a feminist linguistic perspective, is Sex and the City a modernist or a post-modernist TV text?

Sex and the City has been the subject of close scrutiny within feminist scholarship in terms of whether it is considered to be a reactionary or progressive text. While this debate is valuable within a modernist feminist paradigm, it makes less sense from a post-modernist feminist perspective. Alternately using semiotic and feminist post-structuralist methods of textual analysis, this paper shows that Sex and the City can be viewed as reactionary according to a modernist reading, but is altogether more challenging and complex according to a post-modernist reading.

A statistical method for the identification and aggregation of regional linguistic variation

This paper introduces a method for the analysis of regional linguistic variation. The method identifies individual and common patterns of spatial clustering in a set of linguistic variables measured over a set of locations based on a combination of three statistical techniques: spatial autocorrelation, factor analysis, and cluster analysis. To demonstrate how to apply this method, it is used to analyze regional variation in the values of 40 continuously measured, high-frequency lexical alternation variables in a 26-million-word corpus of letters to the editor representing 206 cities from across the United States.