A hybrid MLP-PNN architecture for fast image superresolution

Proceedings of Joint International Conference ICANN/ICONIP 2003 Istanbul, Turkey, June 26–29, 2003The final publication is available at Springer via http://dx.doi.org/10.1007/3-540-44989-2_50Image superresolution methods process an input image sequence of a scene to obtain a still image with increased resolution. Classical approaches to this problem involve complex iterative minimization procedures, typically with high computational costs. In this paper is proposed a novel algorithm for superresolution that enables a substantial decrease in computer load. First, a probabilistic neural network architecture is used to perform a scattered-point interpolation of the image sequence data. The network kernel function is optimally determined for this problem by a multi-layer perceptron trained on synthetic data. Network parameters dependence on sequence noise level is quantitatively analyzed. This super-sampled image is spatially filtered to correct finite pixel size effects, to yield the final high-resolution estimate. Results on a real outdoor sequence are presented, showing the quality of the proposed method.This work has been partially supported by TIC2001-0572-C02-02 gran

Combining Substrate Specificity Analysis with Support Vector Classifiers Reveals Feruloyl Esterase as a Phylogenetically Informative Protein Group

Our understanding of how fungi evolved to develop a variety of ecological niches, is limited but of fundamental biological importance. Specifically, the evolution of enzymes affects how well species can adapt to new environmental conditions. Feruloyl esterases (FAEs) are enzymes able to hydrolyze the ester bonds linking ferulic acid to plant cell wall polysaccharides. The diversity of substrate specificities found in the FAE family shows that this family is old enough to have experienced the emergence and loss of many activities. In this study we evaluate the relative activity of FAEs against a variety of model substrates as a novel predictive tool for Ascomycota taxonomic classification. Our approach consists of two analytical steps; (1) an initial unsupervised analysis to cluster the FAEs substrate specificity data which were generated by cultivation of 34 Ascomycota strains and then an analysis of the produced enzyme cocktail against 10 substituted cinnamate and phenylalkanoate methyl esters, (2) a second, supervised analysis for training a predictor built on these substrate activities. By applying both linear and non-linear models we were able to correctly predict the taxonomic Class (∼86% correct classification), Order (∼88% correct classification) and Family (∼88% correct classification) that the 34 Ascomycota belong to, using the activity profiles of the FAEs. The good correlation with the FAEs substrate specificities that we have defined via our phylogenetic analysis not only suggests that FAEs are phylogenetically informative proteins but it is also a considerable step towards improved FAEs functional prediction.published_or_final_versio

Older Adults' Training Preferences for Learning to Use Technology

Older adults may benefit from using technology in their everyday lives. However, adults over 65 may need more training than their younger counterparts given they have had less experience with technology. In this study, 113 adults between the ages of 65 and 85 participated in focus group interviews discussing their training needs and preferences for technology items used in the home. Participants expressed an interest in receiving additional training, particularly for specific tasks. Participants also discussed preferences for various characteristics of training, such as who should conduct the training and for their preferred method of training. One of the most frequently discussed preferences was for self-training using text materials, such as a manual

Older adults' training preferences for learning to use technology

Older adults may benefit from using technology in their everyday lives. However, adults over 65 may need more training than their younger counterparts given they have had less experience with technology. In this study, 113 adults between the ages of 65 and 85 participated in focus group interviews discussing their training needs and preferences for technology items used in the home. Participants expressed an interest in receiving additional training, particularly for specific tasks. Participants also discussed preferences for various characteristics of training, such as who should conduct the training and for their preferred method of training. One of the most frequently discussed preferences was for self-training using text materials, such as a manual

Focus group script and coding instructions for the CREATE cross-site focus group study of technology use.

Research funded by: National Institutes of Health (National Institute on Aging) Grant P01 AG17211 under the auspices of the Center for Research and Education on Aging and Technology Enhancement (CREATE)

Older adults talk technology: Technology usage and attitudes

Older adults ( n = 113) participated in focus groups discussing their use of and attitudes about technology in the context of their home, work, and healthcare. Participants reported using a wide variety of technology items, particularly in their homes. Positive attitudes (i.e., likes) outnumbered negative attitudes (i.e., dislikes), suggesting that older adults perceive the benefits of technology use to outweigh the costs of such use. Positive attitudes were most frequently related to how the technology supported activities, enhanced convenience, and contained useful features. Negative attitudes were most frequently associated with technology creating inconveniences, unhelpful features, as well as security and reliability concerns. Given that older adults reported more positive than negative attitudes about the technologies they use, these results contradict stereotypes that older adults are afraid or unwilling to use technology. These findings also highlight the importance of perceived benefits of use and ease of use for models of technology acceptance. Emphasizing the benefits of technology in education and training programs may increase future technology adoption