Learning Visual Attributes

We present a probabilistic generative model of visual attributes, together with an efficient learning algorithm. Attributes are visual qualities of objects, such as ‘red’, ‘striped’, or ‘spotted’. The model sees attributes as patterns of image segments, repeatedly sharing some characteristic properties. These can be any combination of appearance, shape, or the layout of segments within the pattern. Moreover, attributes with general appearance are taken into account, such as the pattern of alternation of any two colors which is characteristic for stripes. To enable learning from unsegmented training images, the model is learnt discriminatively, by optimizing a likelihood ratio. As demonstrated in the experimental evaluation, our model can learn in a weakly supervised setting and encompasses a broad range of attributes. We show that attributes can be learnt starting from a text query to Google image search, and can then be used to recognize the attribute and determine its spatial extent in novel real-world images.

God's Nature and Attributes

In Western theism, different attributes have classically been ascribed to God, such as omnipotence, omniscience, wisdom, goodness, freedom and so on. But these ascriptions have also raised many conceptual difficulties: are these attributes internally coherent? Are they really compossible? Are they compatible with what we know about the world (e.g. the existence of evil, human freedom, the laws of nature etc.). These traditional questions are part of the inquiry on God’s nature as it is carried out in contemporary philosophy of religion. Another part of this inquiry is constituted by theological and philosophical questions raised by more precise or particular religious conceptions of God – e.g. the doctrine of Trinity in Christianity, or other specific credentials about the right way to understand God’s perfection and absolute transcendence in Judaism, Christianity or Islam. In this issue, we propose to follow these two directions of the inquiry about God’s nature and attributes through historical and systematic studies, in the perspective of contemporary philosophy of religion and analytical theology. While the three papers specifically dedicated to the problem of the Trinity pertain mainly to the second part of the examination (the conceptual analysis of specific credentials and theological doctrines), the three others offer new perspectives and arguments on traditional questions about God, like the problem of evil, perfect goodness, or the problem of divine perfection and God’s freedom

Learning Multimodal Latent Attributes

Abstract—The rapid development of social media sharing has created a huge demand for automatic media classification and annotation techniques. Attribute learning has emerged as a promising paradigm for bridging the semantic gap and addressing data sparsity via transferring attribute knowledge in object recognition and relatively simple action classification. In this paper, we address the task of attribute learning for understanding multimedia data with sparse and incomplete labels. In particular we focus on videos of social group activities, which are particularly challenging and topical examples of this task because of their multi-modal content and complex and unstructured nature relative to the density of annotations. To solve this problem, we (1) introduce a concept of semi-latent attribute space, expressing user-defined and latent attributes in a unified framework, and (2) propose a novel scalable probabilistic topic model for learning multi-modal semi-latent attributes, which dramatically reduces requirements for an exhaustive accurate attribute ontology and expensive annotation effort. We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multimedia sparse data learning tasks including: multi-task learning, learning with label noise, N-shot transfer learning and importantly zero-shot learning

Attributes of age-identity

Chronological age can be an unsatisfactory method of discriminating between older people. The lay concept of how old people actually feel may be more useful. The aim of the analyses reported in this paper was to investigate indicators of age-identity (or subjective age) among a national random sample of people aged 65 or more years living at home in Britain. Information was initially collected by home interview and a follow-up postal questionnaire 12-18 months later. The age that respondents felt was a more sensitive indicator than chronological age of many indicators of the respondents' health, psychological and social characteristics. Multiple regression analysis showed that baseline health and functional status, and reported changes in these at follow-up, explained 20.4 per cent of the variance in self-perceived age. Adding baseline mental health (anxiety/depression), feelings and fears about ageing at follow-up explained a further 0.8 per cent of the variance, making the total variance explained 21.2 per cent. It is concluded that measures of physical health and functional status and their interactions influenced age-identity. Mental health status and psychological perceptions made a small but significant additional contribution

Sampling networks by nodal attributes

In a social network individuals or nodes connect to other nodes by choosing one of the channels of communication at a time to re-establish the existing social links. Since available data sets are usually restricted to a limited number of channels or layers, these autonomous decision making processes by the nodes constitute the sampling of a multiplex network leading to just one (though very important) example of sampling bias caused by the behavior of the nodes. We develop a general setting to get insight and understand the class of network sampling models, where the probability of sampling a link in the original network depends on the attributes $h$ of its adjacent nodes. Assuming that the nodal attributes are independently drawn from an arbitrary distribution $\rho(h)$ and that the sampling probability $r(h_i , h_j)$ for a link $ij$ of nodal attributes $h_i$ and $h_j$ is also arbitrary, we derive exact analytic expressions of the sampled network for such network characteristics as the degree distribution, degree correlation, and clustering spectrum. The properties of the sampled network turn out to be sums of quantities for the original network topology weighted by the factors stemming from the sampling. Based on our analysis, we find that the sampled network may have sampling-induced network properties that are absent in the original network, which implies the potential risk of a naive generalization of the results of the sample to the entire original network. We also consider the case, when neighboring nodes have correlated attributes to show how to generalize our formalism for such sampling bias and we get good agreement between the analytic results and the numerical simulations.Comment: 11 pages, 5 figure