Introduction : photography between art history and philosophy

The essays collected in this special issue of Critical Inquiry are devoted to reflection on the shifts in photographically based art practice, exhibition, and reception in recent years and to the changes brought about by these shifts in our understanding of photographic art. Although initiated in the 1960s, photography as a mainstream artistic practice has accelerated over the last two decades. No longer confined to specialist galleries, books, journals, and other distribution networks, contemporary art photographers are now regularly the subject of major retrospectives in mainstream fine-art museums on the same terms as any other artist. One could cite, for example, Thomas Struth at the Metropolitan Museum in New York (2003), Thomas Demand at the Museum of Modern Art (MoMa) (2005), or Jeff Wall at Tate Modern and MoMA (2006–7). Indeed, Wall’s most recent museum show, at the time of writing, The Crooked Path at Bozar, Brussels (2011), situated his photography in relation to the work of a range of contemporary photographers, painters, sculptors, performance artists, and filmmakers with whose work Wall considers his own to be in dialogue, irrespective of differences of media. All this goes to show that photographic art is no longer regarded as a subgenre apart. The situation in the United Kingdom is perhaps emblematic of both photography’s increasing prominence and its increased centrality in the contemporary art world over recent years. Tate hosted its first ever photography survey, Cruel and Tender, as recently as 2003, and since then photography surveys have become a regular biannual staple of its exhibition programming, culminating in the appointment of Tate’s first dedicated curator of photography in 2010. A major shift in the perception of photography as art is clearly well under way

Modeling Empathy and Distress in Reaction to News Stories

Computational detection and understanding of empathy is an important factor in advancing human-computer interaction. Yet to date, text-based empathy prediction has the following major limitations: It underestimates the psychological complexity of the phenomenon, adheres to a weak notion of ground truth where empathic states are ascribed by third parties, and lacks a shared corpus. In contrast, this contribution presents the first publicly available gold standard for empathy prediction. It is constructed using a novel annotation methodology which reliably captures empathy assessments by the writer of a statement using multi-item scales. This is also the first computational work distinguishing between multiple forms of empathy, empathic concern, and personal distress, as recognized throughout psychology. Finally, we present experimental results for three different predictive models, of which a CNN performs the best.Comment: To appear at EMNLP 201

AUGUR: Forecasting the Emergence of New Research Topics

Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. The literature presents several approaches to identifying the emergence of new research topics, which rely on the assumption that the topic is already exhibiting a certain degree of popularity and consistently referred to by a community of researchers. However, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. We address this issue by introducing Augur, a novel approach to the early detection of research topics. Augur analyses the diachronic relationships between research areas and is able to detect clusters of topics that exhibit dynamics correlated with the emergence of new research topics. Here we also present the Advanced Clique Percolation Method (ACPM), a new community detection algorithm developed specifically for supporting this task. Augur was evaluated on a gold standard of 1,408 debutant topics in the 2000-2011 interval and outperformed four alternative approaches in terms of both precision and recall

Magnetic-field measurements of T Tauri stars in the Orion Nebula cluster

We present an analysis of high-resolution ($R \sim 50,000$) infrared K-band echelle spectra of 14 T Tauri stars in the Orion Nebula Cluster. We model Zeeman broadening in three magnetically sensitive \ion{Ti}{1} lines near $2.2\ \mu$m and consistently detect kilogauss-level magnetic fields in the stellar photospheres. The data are consistent in each case with the entire stellar surface being covered with magnetic fields, suggesting that magnetic pressure likely dominates over gas pressure in the photospheres of these stars. These very strong magnetic fields might themselves be responsible for the underproduction of X-ray emission of T Tauri stars relative to what is expected based on main-sequence star calibrations. We combine these results with previous measurements of 14 stars in Taurus and 5 stars in the TW Hydrae association to study the potential variation of magnetic-field properties during the first 10 million years of stellar evolution, finding a steady decline in total magnetic flux with age.Comment: 34 pages, 17 figures, published in ApJ, 2011, 729, 8

Cats or CAT scans: transfer learning from natural or medical image source datasets?

Transfer learning is a widely used strategy in medical image analysis. Instead of only training a network with a limited amount of data from the target task of interest, we can first train the network with other, potentially larger source datasets, creating a more robust model. The source datasets do not have to be related to the target task. For a classification task in lung CT images, we could use both head CT images, or images of cats, as the source. While head CT images appear more similar to lung CT images, the number and diversity of cat images might lead to a better model overall. In this survey we review a number of papers that have performed similar comparisons. Although the answer to which strategy is best seems to be "it depends", we discuss a number of research directions we need to take as a community, to gain more understanding of this topic.Comment: Accepted to Current Opinion in Biomedical Engineerin

On the optimism correction of the area under the receiver operating characteristic curve in logistic prediction models

When the same data are used to fit a model and estimate its predictive performance, this estimate may be optimistic, and its correction is required. The aim of this work is to compare the behaviour of different methods proposed in the literature when correcting for the optimism of the estimated area under the receiver operating characteristic curve in logistic regression models. A simulation study (where the theoretical model is known) is conducted considering different number of covariates, sample size, prevalence and correlation among covariates. The results suggest the use of k-fold cross-validation with replication and bootstrap.Peer Reviewe