Applying Suggestibility Research to the Real World: The Case of Repeated Questions

One can discern two parallel trends in the law and the psychology of child witnesses. In the law, appellate courts are beginning to stem the once powerful movement to increase the acceptance of children\u27s testimony and the admissibility of children\u27s out-of-court statements. Lyon analyzes particular strands of each trend

MODEL SELECTION WITH TEMPORAL AND SPATIAL AGGREGATION: ALTERNATIVE MARKETING MARGIN MODELS

Marketing,

Mercury in the environment

Problems in assessing mercury concentrations in environmental materials are discussed. Data for situations involving air, water, rocks, soils, sediments, sludges, fossil fuels, plants, animals, foods, and man are drawn together and briefly evaluated. Details are provided regarding the toxicity of mercury along with tentative standards and guidelines for mercury in air, drinking water, and food

Hellinger Distance Trees for Imbalanced Streams

Classifiers trained on data sets possessing an imbalanced class distribution are known to exhibit poor generalisation performance. This is known as the imbalanced learning problem. The problem becomes particularly acute when we consider incremental classifiers operating on imbalanced data streams, especially when the learning objective is rare class identification. As accuracy may provide a misleading impression of performance on imbalanced data, existing stream classifiers based on accuracy can suffer poor minority class performance on imbalanced streams, with the result being low minority class recall rates. In this paper we address this deficiency by proposing the use of the Hellinger distance measure, as a very fast decision tree split criterion. We demonstrate that by using Hellinger a statistically significant improvement in recall rates on imbalanced data streams can be achieved, with an acceptable increase in the false positive rate.Comment: 6 Pages, 2 figures, to be published in Proceedings 22nd International Conference on Pattern Recognition (ICPR) 201

DRASTIC—INSIGHTS:querying information in a plant gene expression database

DRASTIC––Database Resource for the Analysis of Signal Transduction In Cells (http://www.drastic.org.uk/) has been created as a first step towards a data-based approach for constructing signal transduction pathways. DRASTIC is a relational database of plant expressed sequence tags and genes up- or down-regulated in response to various pathogens, chemical exposure or other treatments such as drought, salt and low temperature. More than 17700 records have been obtained from 306 treatments affecting 73 plant species from 512 peer-reviewed publications with most emphasis being placed on data from Arabidopsis thaliana. DRASTIC has been developed by the Scottish Crop Research Institute and the Abertay University and allows rapid identification of plant genes that are up- or down-regulated by multiple treatments and those that are regulated by a very limited (or perhaps a single) treatment. The INSIGHTS (INference of cell SIGnaling HypoTheseS) suite of web-based tools allows intelligent data mining and extraction of information from the DRASTIC database. Potential response pathways can be visualized and comparisons made between gene expression patterns in response to various treatments. The knowledge gained informs plant signalling pathways and systems biology investigations