Sexual decision making in the absence of choice: The African American female dating experience.

Although links between low mate availability and increased HIV and STI risk for African American women have been documented in the literature, we know little about the impact of limited mate choices on the quality of relationships between Black men and women and how these relationship dynamics impact risk for young Black women. We conducted a qualitative study with African American female young adults (N=12) to explore the perceived impact of structural forces on African American female young adults’ dating and sexual behavior. Participants reported (1) perceptions of Black men as untrustworthy and manipulative, (2) the limited and often negative roles for Black men in the larger Black community, and (3) heterosexual relationships in the Black community as increasingly influenced by economics and commerce. Recommendations for HIV prevention interventions that include micro and macro level approaches are discussed

The State, Democratic Transition and Employment Relations in Indonesia

Indonesia’s transition since 1998 from authoritarian developmentalism to democracy has had a fundamental effect on employment relations. Although the basic structure of the economy has not changed, the twin processes of democratisation and decentralisation have seen the return of a degree of political space not available in Indonesia since the 1950s. This transformation was underpinned by a shift in the balance between the primary logics of the state that has seen an enhanced emphasis on legitimation. It has reshaped expectations of workplace-level employment relations practice in the country’s small formal sector and of trade unions’ engagement with policy-making and electoral politics. This article traces the processes through which this transformation occurred and analyses both its successes and the ongoing challenges to more robust implementation of the country’s industrial relations framework

Full Resolution Image Compression with Recurrent Neural Networks

This paper presents a set of full-resolution lossy image compression methods based on neural networks. Each of the architectures we describe can provide variable compression rates during deployment without requiring retraining of the network: each network need only be trained once. All of our architectures consist of a recurrent neural network (RNN)-based encoder and decoder, a binarizer, and a neural network for entropy coding. We compare RNN types (LSTM, associative LSTM) and introduce a new hybrid of GRU and ResNet. We also study "one-shot" versus additive reconstruction architectures and introduce a new scaled-additive framework. We compare to previous work, showing improvements of 4.3%-8.8% AUC (area under the rate-distortion curve), depending on the perceptual metric used. As far as we know, this is the first neural network architecture that is able to outperform JPEG at image compression across most bitrates on the rate-distortion curve on the Kodak dataset images, with and without the aid of entropy coding.Comment: Updated with content for CVPR and removed supplemental material to an external link for size limitation

Examining the Capability of Supervised Machine Learning Classifiers in Extracting Flooded Areas from Landsat TM Imagery: A Case Study from a Mediterranean Flood

This study explored the capability of Support Vector Machines (SVMs) and regularised kernel Fisher’s discriminant analysis (rkFDA) machine learning supervised classifiers in extracting flooded area from optical Landsat TM imagery. The ability of both techniques was evaluated using a case study of a riverine flood event in 2010 in a heterogeneous Mediterranean region, for which TM imagery acquired shortly after the flood event was available. For the two classifiers, both linear and non-linear (kernel) versions were utilised in their implementation. The ability of the different classifiers to map the flooded area extent was assessed on the basis of classification accuracy assessment metrics. Results showed that rkFDA outperformed SVMs in terms of accurate flooded pixels detection, also producing fewer missed detections of the flooded area. Yet, SVMs showed less false flooded area detections. Overall, the non-linear rkFDA classification method was the more accurate of the two techniques (OA = 96.23%, K = 0.877). Both methods outperformed the standard Normalized Difference Water Index (NDWI) thresholding (OA = 94.63, K = 0.818) by roughly 0.06 K points. Although overall accuracy results for the rkFDA and SVMs classifications only showed a somewhat minor improvement on the overall accuracy exhibited by the NDWI thresholding, notably both classifiers considerably outperformed the thresholding algorithm in other specific accuracy measures (e.g. producer accuracy for the “not flooded” class was ~10.5% less accurate for the NDWI thresholding algorithm in comparison to the classifiers, and average per-class accuracy was ~5% less accurate than the machine learning models). This study provides evidence of the successful application of supervised machine learning for classifying flooded areas in Landsat imagery, where few studies so far exist in this direction. Considering that Landsat data is open access and has global coverage, the results of this study offers important information towards exploring the possibilities of the use of such data to map other significant flood events from space in an economically viable way