Health Determinants among North Americans Experiencing Homelessness and Traumatic Brain Injury: A Scoping Review.

Traumatic brain injury (TBI) in those experiencing homelessness has been described in recent literature as a contributor to increased morbidity, decreased functional independence, and early mortality. In this systematically conducted scoping review, we aimed to better delineate the health determinants-as defined by Health Canada/Centers for Disease Control and Prevention (CDC)-associated with TBI in North Americans experiencing homelessness. BIOSIS, MEDLINE, CINAHL, EMBASE, SCOPUS, and Global Health were searched from inception to December 30, 2020. Gray literature search consisted of relevant meeting proceedings. A two-step process was undertaken, assessing title/abstract and full articles, respectively, based on inclusion/exclusion criteria, leading to the final 20 articles included in the review. Data were abstracted, assessing the aims, literature quality, and bias. Five health determinants displayed strong associations with TBI in those North Americans experiencing homelessness, including male gender, poor physical environment, negative personal health behaviors, adverse childhood experiences (ACEs), and low educational attainment. In those studies displaying a comparator population experiencing homelessness without TBI, the TBI group displayed trends toward increased disparity in Health Canada and CDC defined health determinants. Most studies suffered from moderate limitations. There are associations between male gender, poor physical environment, negative personal health behaviors, ACEs, and limited education in those experiencing homelessness and TBI. The results suggest that those experiencing homelessness with TBI in North America suffer poorer health consequences than those without TBI. Future research on TBI in North Americans experiencing homelessness should focus on health determinants as potential areas for intervention, which may lead to improved outcomes for those experiencing both homelessness and TBI

Learning the Roots of Visual Domain Shift

In this paper we focus on the spatial nature of visual domain shift, attempting to learn where domain adaptation originates in each given image of the source and target set. We borrow concepts and techniques from the CNN visualization literature, and learn domainnes maps able to localize the degree of domain specificity in images. We derive from these maps features related to different domainnes levels, and we show that by considering them as a preprocessing step for a domain adaptation algorithm, the final classification performance is strongly improved. Combined with the whole image representation, these features provide state of the art results on the Office dataset.Comment: Extended Abstrac

Double facades a more sustainable solution than a optimal single facade

Facade parameters influence the energy flows coming through the facade, in order to optimize the indoor environment for the comfort of the individual building occupant with minimal energy use. How can the facade make optimal use of the free incoming energy flows to maximize the comfort level of the individual building occupant at minimal energy use? The type of façade described as a second skin façade is characterised by a single glass layer on the outside and an isolated façade layer on the inside, which often includes an insulated glass layer. The application of the single glass layer as a second skin around the insulated layer results in an air cavity between these two layers. The property that distinguishes a second skin façade from other DSF is that it relies on natural ventilation of the cavity, in comparison to other facades which use mechanical systems to induce the airflow. The advantage of merely using natural ventilation in the façade cavity is the lower energy consumption. However, it also results in some unresolved issues which require further attention. This project is concerned with the behaviour of a highly complex shaped second skin facade on a Dutch office building, and the thermal comfort impact on the building user. During 3 weeks different measurements were done to determine the main characteristics of the glass and the facade. These measurements were related to earlier measurements done by other buildings with a second skin facade. A key difference between a second skin facade, as well as other climate facades, and more traditional opaque facades is its dynamic behaviour

Double facades a more sustainable solution than a optimal single facade

Facade parameters influence the energy flows coming through the facade, in order to optimize the indoor environment for the comfort of the individual building occupant with minimal energy use. How can the facade make optimal use of the free incoming energy flows to maximize the comfort level of the individual building occupant at minimal energy use? The type of façade described as a second skin façade is characterised by a single glass layer on the outside and an isolated façade layer on the inside, which often includes an insulated glass layer. The application of the single glass layer as a second skin around the insulated layer results in an air cavity between these two layers. The property that distinguishes a second skin façade from other DSF is that it relies on natural ventilation of the cavity, in comparison to other facades which use mechanical systems to induce the airflow. The advantage of merely using natural ventilation in the façade cavity is the lower energy consumption. However, it also results in some unresolved issues which require further attention. This project is concerned with the behaviour of a highly complex shaped second skin facade on a Dutch office building, and the thermal comfort impact on the building user. During 3 weeks different measurements were done to determine the main characteristics of the glass and the facade. These measurements were related to earlier measurements done by other buildings with a second skin facade. A key difference between a second skin facade, as well as other climate facades, and more traditional opaque facades is its dynamic behaviour

VConv-DAE: Deep Volumetric Shape Learning Without Object Labels

With the advent of affordable depth sensors, 3D capture becomes more and more ubiquitous and already has made its way into commercial products. Yet, capturing the geometry or complete shapes of everyday objects using scanning devices (e.g. Kinect) still comes with several challenges that result in noise or even incomplete shapes. Recent success in deep learning has shown how to learn complex shape distributions in a data-driven way from large scale 3D CAD Model collections and to utilize them for 3D processing on volumetric representations and thereby circumventing problems of topology and tessellation. Prior work has shown encouraging results on problems ranging from shape completion to recognition. We provide an analysis of such approaches and discover that training as well as the resulting representation are strongly and unnecessarily tied to the notion of object labels. Thus, we propose a full convolutional volumetric auto encoder that learns volumetric representation from noisy data by estimating the voxel occupancy grids. The proposed method outperforms prior work on challenging tasks like denoising and shape completion. We also show that the obtained deep embedding gives competitive performance when used for classification and promising results for shape interpolation

Part Detector Discovery in Deep Convolutional Neural Networks

Current fine-grained classification approaches often rely on a robust localization of object parts to extract localized feature representations suitable for discrimination. However, part localization is a challenging task due to the large variation of appearance and pose. In this paper, we show how pre-trained convolutional neural networks can be used for robust and efficient object part discovery and localization without the necessity to actually train the network on the current dataset. Our approach called "part detector discovery" (PDD) is based on analyzing the gradient maps of the network outputs and finding activation centers spatially related to annotated semantic parts or bounding boxes. This allows us not just to obtain excellent performance on the CUB200-2011 dataset, but in contrast to previous approaches also to perform detection and bird classification jointly without requiring a given bounding box annotation during testing and ground-truth parts during training. The code is available at http://www.inf-cv.uni-jena.de/part_discovery and https://github.com/cvjena/PartDetectorDisovery.Comment: Accepted for publication on Asian Conference on Computer Vision (ACCV) 201

Right for the Right Reason: Training Agnostic Networks

We consider the problem of a neural network being requested to classify images (or other inputs) without making implicit use of a "protected concept", that is a concept that should not play any role in the decision of the network. Typically these concepts include information such as gender or race, or other contextual information such as image backgrounds that might be implicitly reflected in unknown correlations with other variables, making it insufficient to simply remove them from the input features. In other words, making accurate predictions is not good enough if those predictions rely on information that should not be used: predictive performance is not the only important metric for learning systems. We apply a method developed in the context of domain adaptation to address this problem of "being right for the right reason", where we request a classifier to make a decision in a way that is entirely 'agnostic' to a given protected concept (e.g. gender, race, background etc.), even if this could be implicitly reflected in other attributes via unknown correlations. After defining the concept of an 'agnostic model', we demonstrate how the Domain-Adversarial Neural Network can remove unwanted information from a model using a gradient reversal layer.Comment: Author's original versio

Food Recognition using Fusion of Classifiers based on CNNs

With the arrival of convolutional neural networks, the complex problem of food recognition has experienced an important improvement in recent years. The best results have been obtained using methods based on very deep convolutional neural networks, which show that the deeper the model,the better the classification accuracy will be obtain. However, very deep neural networks may suffer from the overfitting problem. In this paper, we propose a combination of multiple classifiers based on different convolutional models that complement each other and thus, achieve an improvement in performance. The evaluation of our approach is done on two public datasets: Food-101 as a dataset with a wide variety of fine-grained dishes, and Food-11 as a dataset of high-level food categories, where our approach outperforms the independent CNN models

Apollo experience report: Development of guidance targeting techniques for the command module and launch vehicle

The development of the guidance targeting techniques for the Apollo command module and launch vehicle is discussed for four types of maneuvers: (1) translunar injection, (2) translunar midcourse, (3) lunar orbit insertion, and (4) return to earth. The development of real-time targeting programs for these maneuvers and the targeting procedures represented are discussed. The material is intended to convey historically the development of the targeting techniques required to meet the defined target objectives and to illustrate the solutions to problems encountered during that development