NAM: Non-Adversarial Unsupervised Domain Mapping

Several methods were recently proposed for the task of translating images between domains without prior knowledge in the form of correspondences. The existing methods apply adversarial learning to ensure that the distribution of the mapped source domain is indistinguishable from the target domain, which suffers from known stability issues. In addition, most methods rely heavily on `cycle' relationships between the domains, which enforce a one-to-one mapping. In this work, we introduce an alternative method: Non-Adversarial Mapping (NAM), which separates the task of target domain generative modeling from the cross-domain mapping task. NAM relies on a pre-trained generative model of the target domain, and aligns each source image with an image synthesized from the target domain, while jointly optimizing the domain mapping function. It has several key advantages: higher quality and resolution image translations, simpler and more stable training and reusable target models. Extensive experiments are presented validating the advantages of our method.Comment: ECCV 201

OneGAN: Simultaneous Unsupervised Learning of Conditional Image Generation, Foreground Segmentation, and Fine-Grained Clustering

We present a method for simultaneously learning, in an unsupervised manner, (i) a conditional image generator, (ii) foreground extraction and segmentation, (iii) clustering into a two-level class hierarchy, and (iv) object removal and background completion, all done without any use of annotation. The method combines a Generative Adversarial Network and a Variational Auto-Encoder, with multiple encoders, generators and discriminators, and benefits from solving all tasks at once. The input to the training scheme is a varied collection of unlabeled images from the same domain, as well as a set of background images without a foreground object. In addition, the image generator can mix the background from one image, with a foreground that is conditioned either on that of a second image or on the index of a desired cluster. The method obtains state of the art results in comparison to the literature methods, when compared to the current state of the art in each of the tasks.Comment: To be published in the European Conference on Computer Vision (ECCV) 202

Artificial intelligence approaches to predict coronary stenosis severity using non-invasive fractional flow reserve

Fractional flow reserve is the current reference standard in the assessment of the functional impact of a stenosis in coronary heart disease. In this study, three models of artificial intelligence of varying degrees of complexity were compared to fractional flow reserve measurements. The three models are the multivariate polynomial regression, which is a statistical method used primarily for correlation; the feed-forward neural network; and the long short-term memory, which is a type of recurrent neural network that is suited to modelling sequences. The models were initially trained using a virtual patient database that was generated from a validated one-dimensional physics-based model. The feed-forward neural network performed the best for all test cases considered, which were a single vessel case from a virtual patient database, a multi-vessel network from a virtual patient database, and 25 clinically invasive fractional flow reserve measurements from real patients. The feed-forward neural network model achieved around 99% diagnostic accuracy in both tests involving virtual patients, and a respectable 72% diagnostic accuracy when compared to the invasive fractional flow reserve measurements. The multivariate polynomial regression model performed well in the single vessel case, but struggled on network cases as the variation of input features was much larger. The long short-term memory performed well for the single vessel cases, but tended to have a bias towards a positive fractional flow reserve prediction for the virtual multi-vessel case, and for the patient cases. Overall, the feed-forward neural network shows promise in successfully predicting fractional flow reserve in real patients, and could be a viable option if trained using a large enough data set of real patients

Nest desertion is not predicted by cuckoldry in the Eurasian penduline tit

Engagement in extra-pair copulations is an example of the abundant conflicting interests between males and females over reproduction. Potential benefits for females and the risk of cuckoldry for males are expected to have important implications on the evolution of parental care. However, whether parents adjust parental care in response to parentage remains unclear. In Eurasian penduline tits Remiz pendulinus, which are small polygamous songbirds, parental care is carried out either by the male or by the female. In addition, one third of clutches is deserted by both male and female. Desertion takes place during the egg-laying phase. Using genotypes of nine microsatellite loci of 443 offspring and 211 adults, we test whether extra-pair paternity predicts parental care. We expect males to be more likely to desert cuckolded broods, whereas we expect females, if they obtain benefits from having multiple sires, to be more likely to care for broods with multiple paternity. Our results suggest that parental care is not adjusted to parentage on an ecological timescale. Furthermore, we found that male attractiveness does not predict cuckoldry, and we found no evidence for indirect benefits for females (i.e., increased growth rates or heterozygosity of extra-pair offspring). We argue that male Eurasian penduline tits may not be able to assess the risk of cuckoldry; thus, a direct association with parental care is unlikely to evolve. However, timing of desertion (i.e., when to desert during the egg-laying phase) may be influenced by the risk of cuckoldry. Future work applying extensive gene sequencing and quantitative genetics is likely to further our understanding of how selection may influence the association between parentage and parental care

Factors underlying the perturbation resistance of the trunk in the first part of a lifting movement

In the first part of lifting movements, the trunk movement is surprisingly resistant to perturbations. This study examined which factors contribute to this perturbation resistance of the trunk during lifting. Three possible mechanisms were studied: force-length-velocity characteristics of muscles, the momentum of the trunk as well as the effect of passive extending of the elbows. A forward dynamics modelling and simulation approach was adopted with two different input signals: (1) stimulation of Hill-type muscles versus (2) net joint moments. Experimental data collected during an unperturbed lifting movement were used as a reference, which a simulated lifting movement had to resemble. Subsequently, the simulated lifting movement was perturbed by applying 10 kg extra mass at the wrist (both before and after lift-off and with/without a fixed elbow), without modifying the input signals. The momentum of the trunk appeared to be insufficient to explain the perturbation resistance of trunk movements as found experimentally. In addition to the momentum of the trunk, the force-length-velocity characteristics of the muscles are necessary to account for the observed perturbation resistance. Initial extension of the elbow due to the mass perturbation delayed the propagation of the load to the shoulder. However, this delay is reduced due to the impedance at the elbow provided by the characteristics of muscles spanning the elbow. So, the force-length-velocity characteristics of the muscles spanning the elbow joint increase the perturbation at the trunk. © Springer-Verlag 2005

The importance of the altricial – precocial spectrum for social complexity in mammals and birds:A review

Various types of long-term stable relationships that individuals uphold, including cooperation and competition between group members, define social complexity in vertebrates. Numerous life history, physiological and cognitive traits have been shown to affect, or to be affected by, such social relationships. As such, differences in developmental modes, i.e. the ‘altricial-precocial’ spectrum, may play an important role in understanding the interspecific variation in occurrence of social interactions, but to what extent this is the case is unclear because the role of the developmental mode has not been studied directly in across-species studies of sociality. In other words, although there are studies on the effects of developmental mode on brain size, on the effects of brain size on cognition, and on the effects of cognition on social complexity, there are no studies directly investigating the link between developmental mode and social complexity. This is surprising because developmental differences play a significant role in the evolution of, for example, brain size, which is in turn considered an essential building block with respect to social complexity. Here, we compiled an overview of studies on various aspects of the complexity of social systems in altricial and precocial mammals and birds. Although systematic studies are scarce and do not allow for a quantitative comparison, we show that several forms of social relationships and cognitive abilities occur in species along the entire developmental spectrum. Based on the existing evidence it seems that differences in developmental modes play a minor role in whether or not individuals or species are able to meet the cognitive capabilities and requirements for maintaining complex social relationships. Given the scarcity of comparative studies and potential subtle differences, however, we suggest that future studies should consider developmental differences to determine whether our finding is general or whether some of the vast variation in social complexity across species can be explained by developmental mode. This would allow a more detailed assessment of the relative importance of developmental mode in the evolution of vertebrate social systems