Transparenting Transparency: Intial Empirics and Policy Applications

Major conceptual contributions of a number of Nobel-laureates in putting forth a framework linking the citizenry's right to know and access to information with development, have already had a major influence in various fields. However, implementation of transparency-related reforms on the ground remains checkered around the globe. Further, in contrast with other dimensions of governance -such as rule of law and regulatory burden-, there is a gap between the extent of the conceptual contributions in the transparency field and the progress on its measurement and empirical analysis, which has been wanting. Our paper is a contribution attempting to partly fill these empirical and policy-related gaps. We contribute to empirics by undertaking an initial construction of a transparency index for 194 countries based on over twenty 20 independent sources. An Unobserved Component Model (UCM) was used to generate the country ratings and the margins of error. The indices comprise an aggregate transparency index with two sub-components: economic/institutional transparency, and political transparency. The results emphasize variance. Exemplary transparency is not the exclusive domain of a particular region, and there are transparency-related challenges in countries in each region and income categories. Further, there is significant within-country variation, with large differences in performance between economic/institutional and political dimensions of transparency. Mindful of the challenges in inferring causality, we also find that transparency is associated with better socio-economic and human development indicators, as well as with higher competitiveness and lower corruption. Much progress can be attained without requiring inordinate amount of resources, since transparency reforms can be substantial net 'savers' of public resources, and often can serve as a more efficient and less financially costly substitute to creating additional regulations and/or regulatory or governance bodies. We provide a number of concrete examples of specific transparency-related reforms within a strategic framework, as well as a brief country illustration - the case of Chile.

\u3cem\u3eTranspeninsular\u3c/em\u3e de Federico Campbell: el desierto de Baja California y la crisis de la (pos)modernidad en el México del nuevo milenio

Roll 166. Basketball Faculty Night (gym). Image 16 of 17. (14 November, 1954) [PHO 1.166.33]The Boleslaus Lukaszewski (Father Luke) Photographs contain more than 28,000 images of Saint Louis University people, activities, and events between 1951 and 1970. The photographs were taken by Boleslaus Lukaszewski (Father Luke), a Jesuit priest and member of the University's Philosophy Department faculty

Budget-aware Semi-Supervised Semantic and Instance Segmentation

Methods that move towards less supervised scenarios are key for image segmentation, as dense labels demand significant human intervention. Generally, the annotation burden is mitigated by labeling datasets with weaker forms of supervision, e.g. image-level labels or bounding boxes. Another option are semi-supervised settings, that commonly leverage a few strong annotations and a huge number of unlabeled/weakly-labeled data. In this paper, we revisit semi-supervised segmentation schemes and narrow down significantly the annotation budget (in terms of total labeling time of the training set) compared to previous approaches. With a very simple pipeline, we demonstrate that at low annotation budgets, semi-supervised methods outperform by a wide margin weakly-supervised ones for both semantic and instance segmentation. Our approach also outperforms previous semi-supervised works at a much reduced labeling cost. We present results for the Pascal VOC benchmark and unify weakly and semi-supervised approaches by considering the total annotation budget, thus allowing a fairer comparison between methods.Comment: To appear in CVPR-W 2019 (DeepVision workshop

Estudio comparativo de los criterios estéticos y juicio artístico en los alumnos de Bellas Artes.

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Hierarchical object detection with deep reinforcement learning

We present a method for performing hierarchical object detection in images guided by a deep reinforcement learning agent. The key idea is to focus on those parts of the image that contain richer information and zoom on them. We train an intelligent agent that, given an image window, is capable of deciding where to focus the attention among five different predefined region candidates (smaller windows). This procedure is iterated providing a hierarchical image analysis. We compare two different candidate proposal strategies to guide the object search: with and without overlap. Moreover, our work compares two different strategies to extract features from a convolutional neural network for each region proposal: a first one that computes new feature maps for each region proposal, and a second one that computes the feature maps for the whole image to later generate crops for each region proposal. Experiments indicate better results for the overlapping candidate proposal strategy and a loss of performance for the cropped image features due to the loss of spatial resolution. We argue that, while this loss seems unavoidable when working with large amounts of object candidates, the much more reduced amount of region proposals generated by our reinforcement learning agent allows considering to extract features for each location without sharing convolutional computation among regions.Postprint (published version

Medidas de valoración estética en alumnos de Bellas Artes .

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