University of Salford

About: University of Salford is a education organization based out in Salford, Manchester, United Kingdom. It is known for research contribution in the topics: Population & Thin film. The organization has 13049 authors who have published 22957 publications receiving 537330 citations. The organization is also known as: University of Salford Manchester & The University of Salford Manchester.

Knowledge of famous faces and names in semantic dementia.

Abstract: Semantic dementia is a focal clinical syndrome, resulting from degeneration of the temporal lobes and characterized by progressive loss of conceptual knowledge about the world. Because of the highly circumscribed nature of the disorder it is a natural model for improving understanding of how semantic information is cerebrally represented. There is currently a lack of consensus. One view proposes the existence of modality specific meaning systems, in which visual and verbal information are stored separately. An opposing view assumes that information is represented by a unitary, amodal semantic system. The present study explores these alternatives in an examination of famous face and name knowledge in 15 patients with semantic dementia. The study of face recognition in patients with an established semantic disorder also permits an examination of the relationship between semantic dementia and the focal clinical syndrome of progressive prosopagnosia. The semantic dementia patients were profoundly impaired on both face and name identification and familiarity judgement tasks compared with amnesic patients with Alzheimer's disease and healthy controls. However, whereas the two reference groups performed better for names than faces, the semantic group showed the opposite pattern. This overall profile masked individual differences: semantic dementia patients with predominant left temporal lobe atrophy showed better recognition of names than faces, whereas patients with right temporal predominance showed the reverse pattern. Relative superiority for names or faces was mirrored by corresponding superiority for words or pictures on a standard semantic test. We interpret the findings as inconsistent with a unitary, amodal model of semantic memory. However, the data are not wholly compatible with a strict multiple system account. The data favour a model of semantic memory comprising a single interconnected network, with dedicated brain regions representing modality specific information. The data emphasize the importance of the anterior, inferolateral parts of the left temporal lobe for the representation of names and the corresponding parts of the right temporal lobe for faces. Dissociations between face and name knowledge provide a challenge for existing models of face processing. Moreover, they lead us to argue that the focal syndrome of progressive prosopagnosia is one of the clinical presentations of semantic dementia and not a separate clinical entity.

On the categorization of demand patterns.

Abstract: The categorization of alternative demand patterns facilitates the selection of a forecasting method and it is an essential element of many inventory control software packages. The common practice in the inventory control software industry is to arbitrarily categorize those demand patterns and then proceed to select an estimation procedure and optimize the forecast parameters. Alternatively, forecasting methods can be directly compared, based on some theoretically quantified error measure, for the purpose of establishing regions of superior performance and then define the demand patterns based on the results. It is this approach that is discussed in this paper and its application is demonstrated by considering EWMA, Croston's method and an alternative to Croston's estimator developed by the first two authors of this paper. Comparison results are based on a theoretical analysis of the mean square error due to its mathematically tractable nature. The categorization rules proposed are expressed in terms of the average inter-demand interval and the squared coefficient of variation of demand sizes. The validity of the results is tested on 3000 real-intermittent demand data series coming from the automotive industry.

Text mining and ontologies in biomedicine: Making sense of raw text

Abstract: The volume of biomedical literature is increasing at such a rate that it is becoming difficult to locate, retrieve and manage the reported information without text mining, which aims to automatically distill information, extract facts, discover implicit links and generate hypotheses relevant to user needs. Ontologies, as conceptual models, provide the necessary framework for semantic representation of textual information. The principal link between text and an ontology is terminology, which maps terms to domain-specific concepts. This paper summarises different approaches in which ontologies have been used for text-mining applications in biomedicine.