A New Measure to Assess Psychopathic Personality in Children: The Child Problematic Traits Inventory

Understanding the development of psychopathic personality from childhood to adulthood is crucial for understanding the development and stability of severe and long-lasting conduct problems and criminal behavior. This paper describes the development of a new teacher rated instrument to assess psychopathic personality from age three to 12, the Child Problematic Traits Inventory (CPTI). The reliability and validity of the CPTI was tested in a Swedish general population sample of 2,056 3- to 5-year-olds (mean age = 3.86; SD = .86; 53 % boys). The CPTI items loaded distinctively on three theoretically proposed factors: a Grandiose-Deceitful Factor, a Callous-Unemotional factor, and an Impulsive-Need for Stimulation factor. The three CPTI factors showed reliability in internal consistency and external validity, in terms of expected correlations with theoretically relevant constructs (e.g., fearlessness). The interaction between the three CPTI factors was a stronger predictor of concurrent conduct problems than any of the three individual CPTI factors, showing that it is important to assess all three factors of the psychopathic personality construct in early childhood. In conclusion, the CPTI seems to reliably and validly assess a constellation of traits that is similar to psychopathic personality as manifested in adolescence and adulthood

Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images.

Assessing the degree of disease severity in biomedical images is a task similar to standard classification but constrained by an underlying structure in the label space. Such a structure reflects the monotonic relationship between different disease grades. In this paper, we propose a straightforward approach to enforce this constraint for the task of predicting Diabetic Retinopathy (DR) severity from eye fundus images based on the well-known notion of Cost-Sensitive classification. We expand standard classification losses with an extra term that acts as a regularizer, imposing greater penalties on predicted grades when they are farther away from the true grade associated to a particular image. Furthermore, we show how to adapt our method to the modelling of label noise in each of the sub-problems associated to DR grading, an approach we refer to as Atomic Sub-Task modeling. This yields models that can implicitly take into account the inherent noise present in DR grade annotations. Our experimental analysis on several public datasets reveals that, when a standard Convolutional Neural Network is trained using this simple strategy, improvements of 3- 5% of quadratic-weighted kappa scores can be achieved at a negligible computational cost. Code to reproduce our results is released at github.com/agaldran/cost_sensitive_loss_classification

Summary and Recommendations from Working Group 1: model uncertainty representations in convection-permitting / shorter lead-time / limited-area ensembles

WG3 discussed both the pros and cons of existing schemes as Working group 1 considered the treatment of model uncertainty (MU) in high-resolution ensembles, at grid spacings of order 1-5 km. These systems are often run for regional weather forecasting, perhaps over a single country, and for lead times of up to 5 days. Looking ahead, ECMWF’s strategy seeks to deliver global medium-range ensemble forecasts with 3-4 km grid spacings by 2030. It is questionable for what grid spacing we should dispense with a deep convection parameterization, but it will be either switched off or damped in these systems, such that deep convection can be assumed to be dominated by explicit motions. One of the problems with limited-area ensemble systems at this scale is that spread depends not only on the modelling system itself but also on the variability inherited from the large-scale boundary conditions. There is often thought to be a lack of spread in our high-resolution EPS (ensemble prediction systems), but this could reflect a lack of diversity on larger scales. The relative importance of lateral-boundary diversity and the model uncertainty mechanisms is regime dependent. The lateral boundaries will generally be more important in midlatitude winter but less so for summertime convection in relatively weak synoptic flow

Learning embeddings into entropic Wasserstein spaces

© 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved. Euclidean embeddings of data are fundamentally limited in their ability to capture latent semantic structures, which need not conform to Euclidean spatial assumptions. Here we consider an alternative, which embeds data as discrete probability distributions in a Wasserstein space, endowed with an optimal transport metric. Wasserstein spaces are much larger and more flexible than Euclidean spaces, in that they can successfully embed a wider variety of metric structures. We exploit this flexibility by learning an embedding that captures semantic information in the Wasserstein distance between embedded distributions. We examine empirically the representational capacity of our learned Wasserstein embeddings, showing that they can embed a wide variety of metric structures with smaller distortion than an equivalent Euclidean embedding. We also investigate an application to word embedding, demonstrating a unique advantage of Wasserstein embeddings: We can visualize the high-dimensional embedding directly, since it is a probability distribution on a low-dimensional space. This obviates the need for dimensionality reduction techniques like t-SNE for visualization