To examine the relationship between registered nurse (RN) staffing mix and quality of nursing home care measured by regulatory violations.A retrospective panel data study (1999-2003) of 2 groups of California freestanding nursing homes. One group was 201 nursing homes that consistently met the state's minimum standard for total nurse staffing level over the 5-year period. The other was 210 nursing homes that consistently failed to meet the standard over the period. All facility and market variables were drawn from California's cost report data and state licensing and certification data, as well as 3 other databases.The RN to total nurse staffing ratio was negatively related to serious deficiencies in nursing homes that consistently met the staffing standard, whereas the ratio was negatively associated with total deficiencies in nursing homes that consistently failed to meet the standard over the 5-year period. As the RN to licensed vocational nurse ratios increased, total deficiencies and serious deficiencies decreased in both groups of nursing homes.A higher RN mix is positively related to quality of care, but the relationship is affected by overall nurse staffing levels in nursing homes. Further studies are necessary for a better understanding of RNs' unique contributions to the quality of care in nursing homes.
Achieving accurate material segmentation for 3-channel RGB images is challenging due to the considerable variation in a material's appearance. Hyperspectral images, which are sets of spectral measurements sampled at multiple wavelengths, theoretically offer distinct information for material identification, as variations in intensity of electromagnetic radiation reflected by a surface depend on the material composition of a scene. However, existing hyperspectral datasets are impoverished regarding the number of images and material categories for the dense material segmentation task, and collecting and annotating hyperspectral images with a spectral camera is prohibitively expensive. To address this, we propose a new model, the MatSpectNet to segment materials with recovered hyperspectral images from RGB images. The network leverages the principles of colour perception in modern cameras to constrain the reconstructed hyperspectral images and employs the domain adaptation method to generalise the hyperspectral reconstruction capability from a spectral recovery dataset to material segmentation datasets. The reconstructed hyperspectral images are further filtered using learned response curves and enhanced with human perception. The performance of MatSpectNet is evaluated on the LMD dataset as well as the OpenSurfaces dataset. Our experiments demonstrate that MatSpectNet attains a 1.60% increase in average pixel accuracy and a 3.42% improvement in mean class accuracy compared with the most recent publication. The project code is attached to the supplementary material and will be published on GitHub.
We propose a hierarchical architecture designed for scalable real-time Model Predictive Control (MPC) in complex, multi-modal traffic scenarios. This architecture comprises two key components: 1) RAID-Net, a novel attention-based Recurrent Neural Network that predicts relevant interactions along the MPC prediction horizon between the autonomous vehicle and the surrounding vehicles using Lagrangian duality, and 2) a reduced Stochastic MPC problem that eliminates irrelevant collision avoidance constraints, enhancing computational efficiency. Our approach is demonstrated in a simulated traffic intersection with interactive surrounding vehicles, showcasing a 12x speed-up in solving the motion planning problem. A video demonstrating the proposed architecture in multiple complex traffic scenarios can be found here: https://youtu.be/-TcMeolCLWc
We present a method for optimizing the reconstruction and rendering of 3D objects from multiple images by utilizing the latest features of consumer-level graphics hardware based on shader model 4.0. We accelerate visual hull reconstruction by rewriting a shape-from-silhouette algorithm to execute on the GPU's parallel architecture. Rendering is optimized through the application of geometry shaders to generate billboarding micr facets textured with captured images. We also present a method for handling occlusion in the camera selection process that is optimized for execution on the GPU. Execution time is further improved by rendering intermediate results directly to texture to minimize the number of data transfers between graphics and main memory. We show our GPU based system to be significantly more efficient than a purely CPUbased approach, due to the parallel nature of the GPU, while maintaining graphical quality.
Learning-based monocular 3D human pose estimation holds significant potential for a variety of applications, including sports, automation, and entertainment; however, not always at a cost that allows it to be scaled. This paper proposes an affordable solution to learning-based monocular 3D pose estimation from 2D videos that can be utilised outdoors and indoors. We introduce a system that leverages an omnidirectional camera and mmWave radars to estimate the 3D pose of the people in the scene in real-time. The proposed algorithm shows good pose reconstruction accuracy with the average Euclidean distance between a ground truth body joint position and its 3D reconstruction ranging from 4.5cm to 19cm within 20 meters along both the $x$ and $z$ axes of the camera.
This study examined the effects of perceived economic inequality and inequality of opportunity on individual preferences for redistributive policies among people in mainland China, Japan, South Korea and Taiwan. Using data from the 2009 International Social Survey Program, a series of regression analyses were performed. Results of the analyses indicate that perceived economic inequality is the most significant predictive factor of attitudes towards redistribution in all four states. Perceived inequality of opportunity was positively associated with favourable attitudes towards redistribution in mainland China and South Korea. Perceived socioeconomic status was not found to have a significant effect on attitudes towards redistribution in South Korea.
Modern digital film production uses large quantities of data from videos, digital photographs, LIDAR scans, spherical photography and many other sources to create the final film frames. The processing and management of this massive amount of heterogeneous data consumes enormous resources. We propose an integrated pipeline for 2D/3D data registration for film production. We present the prototype application Jigsaw, which allows users to efficiently manage and process various data from digital photographs to 3D point clouds. A key requirement in the use of multi-modal 2D/3D data for content production is the registration into a common coordinate frame. 3D geometric information is reconstructed from 2D data and registered to the reference 3D models using 3D feature matching. We provide a public multi-modal database captured with a wide variety of devices in different environments to assist further research. An order of magnitude gain in efficiency is achieved with the proposed approach.
In this paper, we propose an approach to indoor scene understanding from observation of people in single view spherical video. As input, our approach takes a centrally located spherical video capture of an indoor scene, estimating the 3D localisation of human actions performed throughout the long term capture. The central contribution of this work is a deep convolutional encoder-decoder network trained on a synthetic dataset to reconstruct regions of affordance from captured human activity. The predicted affordance segmentation is then applied to compose a reconstruction of the complete 3D scene, integrating the affordance segmentation into 3D space. The mapping learnt between human activity and affordance segmentation demonstrates that omnidirectional observation of human activity can be applied to scene understanding tasks such as 3D reconstruction. We show that our approach using only observation of people performs well against previous approaches, allowing reconstruction of occluded regions and labelling of scene affordances.
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