Artificial intelligence for art investigation: Meeting the challenge of separating x-ray images of the Ghent Altarpiece

X-ray images of polyptych wings, or other artworks painted on both sides of their support, contain in one image content from both paintings, making them difficult for experts to “read.” To improve the utility of these x-ray images in studying these artworks, it is desirable to separate the content into two images, each pertaining to only one side. This is a difficult task for which previous approaches have been only partially successful. Deep neural network algorithms have recently achieved remarkable progress in a wide range of image analysis and other challenging tasks. We, therefore, propose a new self-supervised approach to this x-ray separation, leveraging an available convolutional neural network architecture; results obtained for details from the Adam and Eve panels of the Ghent Altarpiece spectacularly improve on previous attempts

Culture and psychopathology: an attempt at reconsidering the role of social learning

This paper proposes a model for developmental psychopathology that is informed by recent research suggestive of a single model of mental health disorder (the p factor) and seeks to integrate the role of the wider social and cultural environment into our model, which has previously been more narrowly focused on the role of the immediate caregiving context. Informed by recently emerging thinking on the social and culturally driven nature of human cognitive development, the ways in which humans are primed to learn and communicate culture, and a mentalizing perspective on the highly intersubjective nature of our capacity for affect regulation and social functioning, we set out a cultural-developmental approach to psychopathology

Non-destructive characterisation of iron gall ink drawings: not such a galling problem

Iron gall inks are of extraordinary historical significance considering their widespread use for over a millennium. Due to their corrosiveness, which is a consequence of their acidity and content of transition metals, iron gall inks accelerate the degradation of the writing or drawing support, which in this study is rag paper. Characterisation of acidity (pH) and degree of polymerisation (DP) of cellulose in paper is thus of high interest as it enables the estimation of material stability and assessment of risks associated with its handling. Based on a well-characterised set of samples with iron gall ink from the 18th and 19th centuries, we developed a near infrared spectroscopic method with partial least squares calibration for non-destructive determination of pH and DP of both inked areas and paper. Using this method, 27 18th and 19th century iron gall ink drawings from the British Museum collection were analysed and in all cases, inked areas turned out to be more acidic and degraded than the surrounding paper. Based on the obtained DP data, we were able to estimate the time needed for the inked areas to degrade to the point when they become at risk of damage due to handling. Using the average uncertainty of the calculated lifetime, we propose a quantitative stability classification method which could contribute to the curatorial and conservation decision-making process

Current benzodiazepine issues

This article deals with some of the recent evidence bearing on the issues of the liability of benzodiazepines to lead to abuse, dependence, and adverse behavioral effects. Reviews of epidemiological, clinical and experimental literature indicated that the previous conclusion about abuse of these drugs still holds: the vast majority of the use of benzodiazepines is appropriate. Problems of nonmedical use arise nearly exclusively among people who abuse other drugs. Nevertheless, there are reasons for concern about patients who take benzodiazepines regularly for long periods of time. These drugs can produce physiological dependence when taken chronicaly, and although this does not appear to result in dose escalation or other evidence of “psychological dependence,” physiological dependence can result in patient discomfort if drug use is abruptly discontiniued. Also, physicians are currently prescribing shorter-acting benzodiazepines in preference to longer-acting benzodiazepines. The shorter-acting drugs can produce a more intense withdrawal syndrome following chronic administration. Furthermore, rates of use of benzodiazepines increase with age, and elderly patients are more likely than younger ones to take the drug chronically. The clearest adverse effect of benzodiazepines is impairment of memory. This, too, may be particular concern in older patients whose recall in the absence of drug is typically impaired relative to younger individuals, and who are more compromised following drug administration.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46347/1/213_2005_Article_BF02245824.pd

When de Prony Met Leonardo: An Automatic Algorithm for Chemical Element Extraction from Macro X-Ray Fluorescence Data

Macro X-ray Fluorescence (MA-XRF) scanning is an increasingly widely used technique for analytical imaging of paintings and other artworks. The datasets acquired must be processed to produce maps showing the distribution of the chemical elements that are present in the painting. Existing approaches require varying degrees of expert user intervention, in particular to select a list of target elements against which to fit the data. In this paper, we propose a novel approach that can automatically extract and identify chemical elements and their distributions from MA-XRF datasets. The proposed approach consists of three parts: 1) pre-processing steps, 2) pulse detection and model order selection based on Finite Rate of Innovation theory, and 3) chemical element estimation based on Cramér-Rao bounding techniques. The performance of our approach is assessed using MA-XRF datasets acquired from paintings in the collection of the National Gallery, London. The results presented show the ability of our approach to detect elements with weak X-ray fluorescence intensity and from noisy XRF spectra, to separate overlapping elemental signals and, excitingly, to aid visualisation of hidden underdrawing in a masterpiece by Leonardo da Vinci