Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain

Intelligent computer applications need to adapt their behaviour to contexts and users, but conventional classifier adaptation methods require long data collection and/or training times. Therefore classifier adaptation is often performed as follows: at design time application developers define typical usage contexts and provide reasoning models for each of these contexts, and then at runtime an appropriate model is selected from available ones. Typically, definition of usage contexts and reasoning models heavily relies on domain knowledge. However, in practice many applications are used in so diverse situations that no developer can predict them all and collect for each situation adequate training and test databases. Such applications have to adapt to a new user or unknown context at runtime just from interaction with the user, preferably in fairly lightweight ways, that is, requiring limited user effort to collect training data and limited time of performing the adaptation. This paper analyses adaptation trends in several emerging domains and outlines promising ideas, proposed for making multimodal classifiers user-specific and context-specific without significant user efforts, detailed domain knowledge, and/or complete retraining of the classifiers. Based on this analysis, this paper identifies important application characteristics and presents guidelines to consider these characteristics in adaptation design

Inorganic particulate matter in the lung tissue of idiopathic pulmonary fibrosis patients reflects population density and fine particle levels

Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease with a dismal prognosis and an unknown etiology. Inorganic dust is a known risk factor, and air pollution seems to affect disease progression. We aimed to investigate inorganic particulate matter in IPF lung tissue samples. Using polarizing light microscopy, we examined coal dust pigment and inorganic particulate matter in 73 lung tissue samples from the FinnishIPF registry. We scored the amount of coal dust pigment and particulate matter from 0 to 5. Using energy dispersive spectrometry with a scanning electron microscope, we conducted an elemental analysis of six IPF lung tissue samples. We compared the results to the registry data, and to the population density and air quality data. To compare categorical data, we used Fisher's exact test; we estimated the survival of the patients with Kaplan-Meier curves. We found inorganic particulate matter in all samples in varying amounts. Samples from the southern regions of Finland, where population density and fine particle levels are high, more often had particulate matter scores from 3 to 5 than samples from the northern regions (31/50, 62.0% vs. 7/23, 30.4%, p = 0.02). The highest particulate matter scores of 4 and 5 (n = 15) associated with a known exposure to inorganic dust (p = 0.004). An association between particulate matter in the lung tissue of IPF patients and exposure to air pollution may exist.Peer reviewe