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Affect of your patient safety management system on head nurse practitioners as well as clinical nurses: any quasi-experimental research.
g. bounding-box of body parts, objects, etc.) for learning and prediction. Moreover, the proposed keypoints-driven attention mechanism can be easily integrated into the existing CNN models. The framework is evaluated on six diverse benchmark datasets. The model outperforms the state-of-the-art approaches by a considerable margin using Distracted Driver V1 (Acc 3.39%), Distracted Driver V2 (Acc 6.58%), Stanford-40 Actions (mAP 2.15%), People Playing Musical Instruments (mAP 16.05%), Food-101 (Acc 6.30%) and Caltech-256 (Acc 2.59%) datasets.Photometric stereo recovers three-dimensional (3D) object surface normal from multiple images under different illumination directions. Traditional photometric stereo methods suffer from the problem of non-Lambertian surfaces with general reflectance. By leveraging deep neural networks, learning-based methods are capable of improving the surface normal estimation under general non-Lambertian surfaces. These state-of-the-art learning-based methods however do not associate surface normal with reconstructed images and, therefore, they cannot explore the beneficial effect of such association on the estimation of the surface normal. In this paper, we specifically exploit the positive impact of this association and propose a novel dual regression network for both fine surface normals and arbitrary reconstructed images in calibrated photometric stereo. Our work unifies the 3D reconstruction and rendering tasks in a deep learning framework, with the explorations including 1. generating specified reconstructed images under arbitrary illumination directions, which provides more intuitive perception of the reflectance and is extremely useful for visual applications, such as virtual reality, and 2. our dual regression scheme introduces an additional constraint on observed images and reconstructed images, which forms a closed-loop to provide additional supervision. Experiments show that our proposed method achieves accurate reconstructed images under arbitrarily specified illumination directions and it significantly outperforms the state-of-the-art learning-based single regression methods in calibrated photometric stereo.Connected filters and multi-scale tools are region-based operators acting on the connected components of an image. Component trees are image representations to efficiently perform these operations as they represent the inclusion relationship of the connected components hierarchically. This paper presents disccofan (DIStributed Connected COmponent Filtering and ANalysis), a new method that extends the previous 2D implementation of the Distributed Component Forests (DCFs) to handle 3D processing and higher dynamic range data sets. disccofan combines shared and distributed memory techniques to efficiently compute component trees, user-defined attributes filters, and multi-scale analysis. Compared to similar methods, disccofan is faster and scales better on low and moderate dynamic range images, and is the only method with a speed-up larger than 1 on a realistic, astronomical floating-point data set. It achieves a speed-up of 11.20 using 48 processes to compute the DCF of a 162 Gigapixels, single-precision floating-point 3D data set, while reducing the memory used by a factor of 22. This approach is suitable to perform attribute filtering and multi-scale analysis on very large 2D and 3D data sets, up to single-precision floating-point value.Blind image quality assessment (BIQA) is a useful but challenging task. It is a promising idea to design BIQA methods by mimicking the working mechanism of human visual system (HVS). The internal generative mechanism (IGM) indicates that the HVS actively infers the primary content (i.e., meaningful information) of an image for better understanding. Inspired by that, this paper presents a novel BIQA metric by mimicking the active inference process of IGM. Firstly, an active inference module based on the generative adversarial network (GAN) is established to predict the primary content, in which the semantic similarity and the structural dissimilarity (i.e., semantic consistency and structural completeness) are both considered during the optimization. Then, the image quality is measured on the basis of its primary content. Generally, the image quality is highly related to three aspects, i.e., the scene information (content-dependency), the distortion type (distortion-dependency), and the content degradation (degradation-dependency). According to the correlation between the distorted image and its primary content, the three aspects are analyzed and calculated respectively with a multi-stream convolutional neural network (CNN) based quality evaluator. As a result, with the help of the primary content obtained from the active inference and the comprehensive quality degradation measurement from the multi-stream CNN, our method achieves competitive performance on five popular IQA databases. Especially in cross-database evaluations, our method achieves significant improvements.Sparse representation has achieved great success across various fields including signal processing, machine learning and computer vision. However, most existing sparse representation methods are confined to the real valued data. This largely limit their applicability to the quaternion valued data, which has been widely used in numerous applications such as color image processing. Another critical issue is that their performance may be severely hampered due to the data noise or outliers in practice. To tackle the problems above, in this work we propose a robust quaternion valued sparse representation (RQVSR) method in a fully quaternion valued setting. To handle the quaternion noises, we first define a new robust estimator referred as quaternion Welsch estimator to measure the quaternion residual error. Compared to the conventional quaternion mean square error, it can largely suppress the impact of large data corruption and outliers. To implement RQVSR, we have overcome the difficulties raised by the noncommutativity of quaternion multiplication and developed an effective algorithm by leveraging the half-quadratic theory and the alternating direction method of multipliers framework. The experimental results show the effectiveness and robustness of the proposed method for quaternion sparse signal recovery and color image reconstruction.Shape completion for 3-D point clouds is an important issue in the literature of computer graphics and computer vision. We propose an end-to-end shape-preserving point completion network through encoder-decoder architecture, which works directly on incomplete 3-D point clouds and can restore their overall shapes and fine-scale structures. To achieve this task, we design a novel encoder that encodes information from neighboring points in different orientations and scales, as well as a decoder that outputs dense and uniform complete point clouds. We augment a 3-D object dataset based on ModelNet40 and validate the effectiveness of our shape-preserving completion network. Experimental results demonstrate that the recovered point clouds lie close to ground truth points. Our method outperforms state-of-the-art approaches in terms of Chamfer distance (CD) error and earth mover's distance (EMD) error. Furthermore, our end-to-end completion network is robust to model noise, the different levels of incomplete data, and can also generalize well to unseen objects and real-world data.Virtual reality (VR) is a powerful medium for 360 storytelling, yet content creators are still in the process of developing cinematographic rules for effectively communicating stories in VR. Traditional cinematography has relied for over a century in well-established techniques for editing, and one of the most recurrent resources for this are cinematic cuts that allow content creators to seamlessly transition between scenes. One fundamental assumption of these techniques is that the content creator can control the camera, however, this assumption breaks in VR users are free to explore the 360 around them. Recent works have studied the effectiveness of different cuts in 360 content, but the effect of directional sound cues while experiencing these cuts has been less explored. In this work, we provide the first systematic analysis of the influence of directional sound cues in users behavior across 360 movie cuts, providing insights that can have an impact on deriving conventions for VR storytelling.While most classical approaches to Granger causality detection assume linear dynamics, many interactions in applied domains, like neuroscience and genomics, are inherently nonlinear. In these cases, using linear models may lead to inconsistent estimation of Granger causal interactions. We propose a class of nonlinear methods by applying structured multilayer perceptrons (MLPs) or recurrent neural networks (RNNs) combined with sparsity-inducing penalties on the weights. By encouraging specific sets of weights to be zero---in particular through the use of convex group-lasso penalties---we can extract the Granger causal structure. To further contrast with traditional approaches, our framework naturally enables us to efficiently capture long-range dependencies between series either via our RNNs or through an automatic lag selection in the MLP. We show that our neural Granger causality methods outperform state-of-the-art nonlinear Granger causality methods on the DREAM3 challenge data. This data consists of nonlinear gene expression and regulation time courses with only a limited number of time points. The successes we show in this challenging dataset provide a powerful example of how deep learning can be useful in cases that go beyond prediction on large datasets. We likewise illustrate our methods in detecting nonlinear interactions in a human motion capture dataset.Image compression is one of the most fundamental techniques in the image and video processing field. Earlier methods built a well-designed pipeline, and efforts were made to improve all modules of the pipeline by handcrafted tuning. Later, tremendous contributions were made in incorporating newly designed learned modules and constraints. Despite great progress, a systematic benchmark and comprehensive analysis of end-to-end learned image compression methods are lacking. In this paper, we first conduct a comprehensive literature survey of learned image compression methods. We describe milestones in cutting-edge learned image-compression methods, review a broad range of existing works, and provide insights into their historical development routes. With this survey, the main challenges of image compression methods are revealed, along with opportunities to address the related issues with recent advanced learning methods. This analysis provides an opportunity to take a further step towards higher-efficiency image compression. By introducing a coarse-to-fine hyperprior model for entropy estimation and signal reconstruction, we achieve improved rate-distortion performance, especially on high-resolution images. Extensive benchmark experiments demonstrate the superiority of our model in rate-distortion performance and time complexity on multi-core CPUs and GPUs.Electrodermal activity (EDA) is a measure of sweat gland activation due to sympathetic arousal, and it has been widely used to assess human response to stressful stimuli, including pain. Tonic and phasic components are typically obtained from EDA to assess sympathetic arousal. More recently, spectral analysis of EDA has been found to be more sensitive and reproducible than tonic and phasic components. However, none of the aforementioned analyses incorporate the differential characteristics of EDA, which could be more sensitive to capturing fast-changing dynamics associated with pain responses. We have tested the feasibility of using the derivative of phasic EDA and the modified time-varying spectral analysis of EDA. Sixteen subjects underwent four levels of pain stimulation using electric stimulation. Telacebec Five-second segments of EDA were used for each level of stimulation, and pre-stimulation segments were considered stimulation level 0. We used support vector machines with the radial basis function kernel and multi-layer perceptron for three different scenarios of stimulation-level classification tasks five stimulation levels (four levels of stimulation plus no stimulation); low, medium, and high pain stimulation (stimulation levels 0-1, 2, and 3-4, respectively); and high stimulation levels (stimulation levels 3-4) vs.
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