Health Information Services Available for People Living With HIV/AIDS: Perspectives of Library and Information Professionals

There is an urgent need for availability of life-saving health information services as well as adequate marketing, advertising, and dissemination strategies to people living with HIV/AIDS (PLWHAs), and to the broader public at large, especially in the context of a recent UNAIDS estimation that the number of people living with HIV in the United States, at the end of 2003, exceeded one million for the first time. This study explores the HIV/AIDS health information services that are available within the local community of Knoxville, Tennessee, and presents focus group perspectives of nine library and information professionals about awareness and use of these services by PLWHAs. The study forms part of a larger plan to apply a community informatics (CI) approach to examine the provision of health information services for PLWHAs in terms of how PLWHAs and other stakeholders including health care service providers, academic community at the University of Tennessee, community leaders and activists, and faith-based organizations, use and apply information and communication technologies (ICTs) to empower and enable PLWHAs to meet their information needs, goals, and aspirations. Here we report findings from the project’s first phase of documenting perspectives of library and information professionals about existing HIV/AIDS information services, users of these services, barriers and challenges to effective use, and the role of health information professionals in the context of developing ideal information support services for PLWHAs

An Analysis of Scale Invariance in Object Detection - SNIP

An analysis of different techniques for recognizing and detecting objects under extreme scale variation is presented. Scale specific and scale invariant design of detectors are compared by training them with different configurations of input data. By evaluating the performance of different network architectures for classifying small objects on ImageNet, we show that CNNs are not robust to changes in scale. Based on this analysis, we propose to train and test detectors on the same scales of an image-pyramid. Since small and large objects are difficult to recognize at smaller and larger scales respectively, we present a novel training scheme called Scale Normalization for Image Pyramids (SNIP) which selectively back-propagates the gradients of object instances of different sizes as a function of the image scale. On the COCO dataset, our single model performance is 45.7% and an ensemble of 3 networks obtains an mAP of 48.3%. We use off-the-shelf ImageNet-1000 pre-trained models and only train with bounding box supervision. Our submission won the Best Student Entry in the COCO 2017 challenge. Code will be made available at \url{http://bit.ly/2yXVg4c}.Comment: CVPR 2018, camera ready versio

Fast-AT: Fast Automatic Thumbnail Generation using Deep Neural Networks

Fast-AT is an automatic thumbnail generation system based on deep neural networks. It is a fully-convolutional deep neural network, which learns specific filters for thumbnails of different sizes and aspect ratios. During inference, the appropriate filter is selected depending on the dimensions of the target thumbnail. Unlike most previous work, Fast-AT does not utilize saliency but addresses the problem directly. In addition, it eliminates the need to conduct region search on the saliency map. The model generalizes to thumbnails of different sizes including those with extreme aspect ratios and can generate thumbnails in real time. A data set of more than 70,000 thumbnail annotations was collected to train Fast-AT. We show competitive results in comparison to existing techniques