In very old and/or frail older people living in long-term care facilities, physical inactivity negatively affects activities of daily living. The main reason to assess older adults' perceived fitness is to establish the relation with their beliefs about their ability to perform physical activity adjusted to daily tasks. The Self-Assessment of Physical Fitness scale was developed to address these needs. The aim of this study was to estimate the test-retest reliability and construct validity of the scale. 76 elderly people (M age = 86.0 yr., SD = 6.3) completed the test. Cronbach's a was .71. One-week test-retest reliability ICC's ranged from .66 (SAPF aerobic endurance and SAPF balance) to .70 (SAPF sum score). Concurrent validity with the Groningen Fitness Test for the Elderly was fair to moderate. Despite the limited number of participants (N = 76), results suggest that the scale may be useful as an assessment of perceived fitness in older adults.
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Studying images in social media poses specific methodological challenges, which in turn have directed scholarly attention towards the computational interpretation of visual data. When analyzing large numbers of images, both traditional content analysis as well as cultural analytics have proven valuable. However, these techniques do not take into account the circulation and contextualization of images within a socio-technical environment. As the meaning of social media images is co-created by networked publics, bound through networked practices, these visuals should be analyzed on the level of their networked contextualization. Although machine vision is increasingly adept at recognizing faces and features, its performance in grasping the meaning of social media images is limited. However, combining automated analyses of images - broken down by their compositional elements - with repurposing platform data opens up the possibility to study images in the context of their resonance within and across online discursive spaces. This paper explores the capacities of platform data - hashtag modularity and retweet counts - to complement the automated assessment of social media images; doing justice to both the visual elements of an image and the contextual elements encoded by networked publics that co-create meaning.
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We present a novel architecture for an AI system that allows a priori knowledge to combine with deep learning. In traditional neural networks, all available data is pooled at the input layer. Our alternative neural network is constructed so that partial representations (invariants) are learned in the intermediate layers, which can then be combined with a priori knowledge or with other predictive analyses of the same data. This leads to smaller training datasets due to more efficient learning. In addition, because this architecture allows inclusion of a priori knowledge and interpretable predictive models, the interpretability of the entire system increases while the data can still be used in a black box neural network. Our system makes use of networks of neurons rather than single neurons to enable the representation of approximations (invariants) of the output.
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