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Around the Biomedical Qualities involving Endocannabinoid Destruction and Reuptake Inhibitors: Pre-clinical along with Medical Facts.
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. 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. no stimulation. The maximum balanced accuracies were 44% (five stimulation levels), 63% (for low, medium, and high pain stimulation), and 87% (sensitivity 83% and specificity 89%, for high stimulation vs. buy AT-527 no stimulation). The differential characteristics of EDA contributed highly to the accuracy of pain stimulation level detection of the classifiers. The external validity dataset was not considered in the study.
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