African American Students\u27 Perceptions of Influential Factors for Attendance in Doctoral Psychology

This study explores African American undergraduate students’ perceptions of factors influencing their decision to attend doctoral programs in psychology. There is a scarcity of literature examining perceptions held by specific minority groups in regard to influential factors used to make a significant step toward their career development. Eight undergraduate students interested in pursuing a doctoral degree in psychology were interviewed. A semi-structured interview and two paper-pencil measures were used. Interviews were analyzed utilizing the consensual qualitative research (CQR) method. The following themes emerged: reasons for pursuing a doctoral degree, navigating the application process, factors influencing interest in psychology, perception of a program’s commitment to diversity, importance of ethnic minority representation in a program, financial concerns, family view of psychology, most important factor for attendance, and prior school experiences outside of psychology. The study found that issues related to African American representation and research, as well as the presence of financial aid, are highly relevant in students’ evaluation of which doctoral programs they prefer to attend. This information will pave the way for further studies focusing on how to increase the number of African American students in doctoral programs around the country

Object Part Localization Using Exemplar-based Models

​Object part localization is a fundamental problem in computer vision, which aims to let machines understand object in an image as a configuration of parts. As the visual features at parts are usually weak and misleading, spatial models are needed to constrain the part configuration, ensuring that the estimated part locations respect both image cue and shape prior. Unlike most of the state-of-the-art techniques that employ parametric spatial models, we turn to non-parametric exemplars of part configurations. The benefit is twofold: instead of assuming any parametric yet imprecise distributions on the spatial relations of parts, exemplars literally encode such relations present in the training samples; exemplars allow us to prune the search space of part configurations with high confidence. This thesis consists of two parts: fine-grained classification and object part localization. We first verify the efficacy of parts in fine-grained classification, where we build working systems that automatically identify dog breeds, fish species, and bird species using localized parts on the object. Then we explore multiple ways to enhance exemplar-based models, such that they can be well applied to deformable objects such as bird and human body. Specifically, we propose to enforce pose and subcategory consistency in exemplar matching, thus obtaining more reliable hypotheses of configuration. We also propose part-pair representation that features novel shape composing with multiple promising hypotheses. In the end, we adapt exemplars to hierarchical representation, and design a principled formulation to predict the part configuration based on multi-scale image cues and multi-level exemplars. These efforts consistently improve the accuracy of object part localization

Technology assessment of advanced automation for space missions

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology

Media coverage of climate change mitigation in the spanish press

This article analyzes how the Spanish press covers the mitigation of climate change. We have used the search engine MyNews to study in El País and El Mundo, the newspapers with the largest circulation in Spain during the years 2016 and 2017, the news that includes the words "mitigacion" o "reducción de emisiones", y "cambio climatico” o “calentamiento global" in the most circulation newspapers in Spain in 2016 and 2017: El País and El Mundo. To explain how mitigation is covered by the Spanish press, we have used a series of categories and variables. As a result, we find an important difference between the urgency expressed by the scientific community and the reduced presence of this topic in the Spanish press

BlogForever: D3.1 Preservation Strategy Report

This report describes preservation planning approaches and strategies recommended by the BlogForever project as a core component of a weblog repository design. More specifically, we start by discussing why we would want to preserve weblogs in the first place and what it is exactly that we are trying to preserve. We further present a review of past and present work and highlight why current practices in web archiving do not address the needs of weblog preservation adequately. We make three distinctive contributions in this volume: a) we propose transferable practical workflows for applying a combination of established metadata and repository standards in developing a weblog repository, b) we provide an automated approach to identifying significant properties of weblog content that uses the notion of communities and how this affects previous strategies, c) we propose a sustainability plan that draws upon community knowledge through innovative repository design

The neural coding of properties shared by faces, bodies and objects

Previous studies have identified relatively separated regions of the brain that respond strongly when participants view images of either faces, bodies or objects. The aim of this thesis was to investigate how and where in the brain shared properties of faces, bodies and objects are processed. We selected three properties that are shared by faces and bodies, shared categories (sex and weight), shared identity and shared orientation (i.e. facing direction). We also investigated one property shared by faces and objects, the tendency to process a face or object as a whole rather than by its parts, which is known as holistic processing. We hypothesized that these shared properties might be encoded separately for faces, bodies and objects in the previously defined domain-specific regions, or alternatively that they might be encoded in an overlapping or shared code in those or other regions. In all of studies in this thesis, we used fMRI to record the brain activity of participants viewing images of faces and bodies or objects that showed differences in the shared properties of interest. We then investigated the neural responses these stimuli elicited in a variety of specifically localized brain regions responsive to faces, bodies or objects, as well as across the whole-brain. Our results showed evidence for a mix of overlapping coding, shared coding and domain-specific coding, depending on the particular property and the level of abstraction of its neural coding. We found we could decode face and body categories, identities and orientations from both face- and body-responsive regions showing that these properties are encoded in overlapping brain regions. We also found that non-domain specific brain regions are involved in holistic face processing. We identified shared coding of orientation and weight in the occipital cortex and shared coding of identity in the early visual cortex, right inferior occipital cortex, right parahippocampal cortex and right superior parietal cortex, demonstrating that a variety of brain regions combine face and body information into a common code. In contrast to these findings, we found evidence that high-level visual transformations may be predominantly processed in domain-specific regions, as we could most consistently decode body categories across image-size and body identity across viewpoint from body-responsive regions. In conclusion, this thesis furthers our understanding of the neural coding of face, body and object properties and gives new insights into the functional organisation of occipitotemporal cortex

Self-tuned Visual Subclass Learning with Shared Samples An Incremental Approach

Computer vision tasks are traditionally defined and evaluated using semantic categories. However, it is known to the field that semantic classes do not necessarily correspond to a unique visual class (e.g. inside and outside of a car). Furthermore, many of the feasible learning techniques at hand cannot model a visual class which appears consistent to the human eye. These problems have motivated the use of 1) Unsupervised or supervised clustering as a preprocessing step to identify the visual subclasses to be used in a mixture-of-experts learning regime. 2) Felzenszwalb et al. part model and other works model mixture assignment with latent variables which is optimized during learning 3) Highly non-linear classifiers which are inherently capable of modelling multi-modal input space but are inefficient at the test time. In this work, we promote an incremental view over the recognition of semantic classes with varied appearances. We propose an optimization technique which incrementally finds maximal visual subclasses in a regularized risk minimization framework. Our proposed approach unifies the clustering and classification steps in a single algorithm. The importance of this approach is its compliance with the classification via the fact that it does not need to know about the number of clusters, the representation and similarity measures used in pre-processing clustering methods a priori. Following this approach we show both qualitatively and quantitatively significant results. We show that the visual subclasses demonstrate a long tail distribution. Finally, we show that state of the art object detection methods (e.g. DPM) are unable to use the tails of this distribution comprising 50\% of the training samples. In fact we show that DPM performance slightly increases on average by the removal of this half of the data.Comment: Updated ICCV 2013 submissio

Action Recognition in Video Using Sparse Coding and Relative Features

This work presents an approach to category-based action recognition in video using sparse coding techniques. The proposed approach includes two main contributions: i) A new method to handle intra-class variations by decomposing each video into a reduced set of representative atomic action acts or key-sequences, and ii) A new video descriptor, ITRA: Inter-Temporal Relational Act Descriptor, that exploits the power of comparative reasoning to capture relative similarity relations among key-sequences. In terms of the method to obtain key-sequences, we introduce a loss function that, for each video, leads to the identification of a sparse set of representative key-frames capturing both, relevant particularities arising in the input video, as well as relevant generalities arising in the complete class collection. In terms of the method to obtain the ITRA descriptor, we introduce a novel scheme to quantify relative intra and inter-class similarities among local temporal patterns arising in the videos. The resulting ITRA descriptor demonstrates to be highly effective to discriminate among action categories. As a result, the proposed approach reaches remarkable action recognition performance on several popular benchmark datasets, outperforming alternative state-of-the-art techniques by a large margin.Comment: Accepted to CVPR 201