Learning to Generate Images with Perceptual Similarity Metrics

Deep networks are increasingly being applied to problems involving image synthesis, e.g., generating images from textual descriptions and reconstructing an input image from a compact representation. Supervised training of image-synthesis networks typically uses a pixel-wise loss (PL) to indicate the mismatch between a generated image and its corresponding target image. We propose instead to use a loss function that is better calibrated to human perceptual judgments of image quality: the multiscale structural-similarity score (MS-SSIM). Because MS-SSIM is differentiable, it is easily incorporated into gradient-descent learning. We compare the consequences of using MS-SSIM versus PL loss on training deterministic and stochastic autoencoders. For three different architectures, we collected human judgments of the quality of image reconstructions. Observers reliably prefer images synthesized by MS-SSIM-optimized models over those synthesized by PL-optimized models, for two distinct PL measures ($\ell_1$ and $\ell_2$ distances). We also explore the effect of training objective on image encoding and analyze conditions under which perceptually-optimized representations yield better performance on image classification. Finally, we demonstrate the superiority of perceptually-optimized networks for super-resolution imaging. Just as computer vision has advanced through the use of convolutional architectures that mimic the structure of the mammalian visual system, we argue that significant additional advances can be made in modeling images through the use of training objectives that are well aligned to characteristics of human perception

Why is low waist-to-chest ratio attractive in males? The mediating roles of perceived dominance, fitness, and protection ability

Past research suggests that a lower waist-to-chest ratio (WCR) in men (i.e., narrower waist and broader chest) is viewed as attractive by women. However, little work has directly examined why low WCRs are preferred. The current work merged insights from theory and past research to develop a model examining perceived dominance, fitness, and protection ability as mediators of to WCR-attractiveness relationship. These mediators and their link to both short-term (sexual) and long-term (relational) attractiveness were simultaneously tested by having 151 women rate one of 15 avatars, created from 3D body scans. Men with lower WCR were perceived as more physically dominant, physically fit, and better able to protect loved ones; these characteristics differentially mediated the effect of WCR on short-term, long-term, and general attractiveness ratings. Greater understanding of the judgments women form regarding WCR may yield insights into motivations by men to manipulate their body image

A 180 Kpc Tidal Tail in the Luminous Infrared Merger Arp 299

We present VLA HI observations and UH88 deep optical B- and R-band observations of the IR luminous merger Arp 299 (= NGC 3690 + IC 694). These data reveal a gas-rich, optically faint tidal tail with a length of over 180 kpc. The size of this tidal feature necessitates an old interaction age for the merger (~750 Myr since first periapse), which is currently experiencing a very young star burst (~20 Myr). The observations reveal a most remarkable structure within the tidal tail: it appears to be composed of two parallel filaments separated by ~20 kpc. One of the filaments is gas rich with little if any starlight, while the other is gas poor. We believe that this bifurcation results from a warped disk in one of the progenitors. The quantities and kinematics of the tidal HI suggest that Arp 299 results from the collision of a retrograde Sab-Sb galaxy (IC 694) and a prograde Sbc-Sc galaxy (NGC 3690) that occurred 750 Myr ago and which will merge into a single object in ~60 Myr. We suggest that the present IR luminous phase in this system is due in part to the retrograde spin of IC 694. Finally, we discuss the apparent lack of tidal dwarf galaxies within the tail.Comment: LaTex, 14 pages, 11 figures, 4 tables, uses emulateapj.sty. Accepted to AJ for July 1999. For version with full-resolution images see http://www.cv.nrao.edu/~jhibbard/a299/HIpaper/a299HI.htm

Kernel density classification and boosting: an L2 sub analysis

Kernel density estimation is a commonly used approach to classification. However, most of the theoretical results for kernel methods apply to estimation per se and not necessarily to classification. In this paper we show that when estimating the difference between two densities, the optimal smoothing parameters are increasing functions of the sample size of the complementary group, and we provide a small simluation study which examines the relative performance of kernel density methods when the final goal is classification. A relative newcomer to the classification portfolio is “boosting”, and this paper proposes an algorithm for boosting kernel density classifiers. We note that boosting is closely linked to a previously proposed method of bias reduction in kernel density estimation and indicate how it will enjoy similar properties for classification. We show that boosting kernel classifiers reduces the bias whilst only slightly increasing the variance, with an overall reduction in error. Numerical examples and simulations are used to illustrate the findings, and we also suggest further areas of research

Recent ASA presidents and ‘top’ journals: observed publication patterns, alleged cartels and varying careers

It has been common for studies presented as about American sociology as a whole to rely on data compiled from leading journals (American Sociological Review [ASR] and American Journal of Sociology [AJS]), or about presidents of the American Sociological Association [ASA], to represent it. Clearly those are important, but neither can be regarded as providing a representative sample of American sociology. Recently, Stephen Turner has suggested that dominance in the ASA rests with a ‘cartel’ initially formed in graduate school, and that it favors work in a style associated with the leading journals. The adequacy of these ideas is examined in the light of available data on the last 20 years, which show that very few of the presidents were in the same graduate schools at the same time. All presidents have had distinguished academic records, but it is shown that their publication strategies have varied considerably. Some have had no ASR publications except their presidential addresses, while books and large numbers of other journals not normally mentioned in this context have figured in their contributions, as well as being more prominent in citations. It seems clear that articles in the leading journals have not been as closely tied to prestigious careers as has sometimes been suggested, and that if there is a cartel it has not included all the presidents

Exploring the pastiche hegemony of men

In this article I explore the continued hegemony of certain men. I use interview extracts to help think through the notion of pastiche hegemony as a means of understanding how men, and narratives about them, have changed, but unequal power relations persist. In particular, I explore this process within men’s understandings of how they were able to gain and maintain influence and power at work. Through their reflexive reading of the changing shape of late modern Western society, these men believed they were able to craft selves and employ social scripts to produce social influence and power in situational and contingent forms. I argue that it is within this interactional process that the increasingly undermined ideological and material legacy of patriarchy might still be reified. As such, while there is clear evidence highlighting the undermining of men’s ability to assume power, within this article I theoretically unpack how certain men might be able to produce a localized, pastiche hegemony. This article is published as part of a thematic collection on gender studies

Sequential updating of a new dynamic pharmacokinetic model for caffeine in premature neonates

International audienceCaffeine treatment is widely used in nursing care to reduce the risk of apnoea in premature neonates. To check the therapeutic efficacy of the treatment against apnoea, caffeine concentration in blood is an important indicator. The present study was aimed at building a pharmacokinetic model as a basis for a medical decision support tool. In the proposed model, time dependence of physiological parameters is introduced to describe rapid growth of neonates. To take into account the large variability in the population, the Pharmacokinetic model is embedded in a population structure. The whole model is inferred within a Bayesian framework. To update caffeine concentration predictions as data of an incoming patient are collected, we propose a fast method that can be used in a medical context. This involves the sequential updating of model parameters (at individual and population levels) via a stochastic particle algorithm. Our model provides better predictions than the ones obtained with models previously published. We show, through an example, that sequential updating improves predictions of caffeine concentration in blood (reduce bias and length of credibility intervals). The update of the pharmacokinetic model using body mass and caffeine concentration data is studied. It shows how informative caffeine concentration data are in contrast to body mass data. This study provides the methodological basis to predict caffeine concentration in blood, after a given treatment if data are collected on the treated neonate