Barrett Drew (bikemiddle61)

Insomnia affects millions of people worldwide, and non-pharmacological treatment options are limited. A bed excited with multiple vibration sources was used to explore beat frequency vibration (BFV) as a non-pharmacological treatment for insomnia. A repeated measures design pilot study of 14 participants with mild-moderate insomnia symptom severity (self-reported on the Insomnia Severity Index) was conducted to determine the effects of BFV, and traditional standing wave vibration (SWV) on sleep latency and sleep electrocortical activity. Participants were monitored using high-density electroencephalography (HD-EEG). Sleep latency was compared between treatment conditions. A trend of decreasing sleep latency due to BFV was found for unequivocal sleep latency (p ≤ 0.068). Neural complexity during wake, N1, and N2 stages were compared using Multi-Scale Sample Entropy (MSE), which demonstrated significantly lower MSE between wake and N2 stages (p ≤ 0.002). During N2 sleep, BFV showed lower MSE than the control session in the left frontoparietal region. As a measure of information integration, reduced entropy may indicate that BFV decreases conscious awareness during deeper stages of sleep. SWV caused reduced alpha activity and increased delta activity during wake. BFV caused increased delta activity during N2 sleep. These preliminary results suggest that BFV may help decrease sleep latency, reduce conscious awareness, and increase sleep drive expression during deeper stages of sleep. SWV may be beneficial for decreasing expression of arousal and increasing expression of sleep drive during wake, implying that beat frequency vibration may be beneficial to sleep.Restoring the clean background from the superimposed images containing a noisy layer is the common crux of a classical category of tasks on image restoration such as image reflection removal, image deraining and image dehazing. These tasks are typically formulated and tackled individually due to diverse and complicated appearance patterns of noise layers within the image. In this work we present the Deep-Masking Generative Network (DMGN), which is a unified framework for background restoration from the superimposed images and is able to cope with different types of noise. Our proposed DMGN follows a coarse-to-fine generative process a coarse background image and a noise image are first generated in parallel, then the noise image is further leveraged to refine the background image to achieve a higher-quality background image. TAE226 ic50 In particular, we design the novel Residual Deep-Masking Cell as the core operating unit for our DMGN to enhance the effective information and suppress the negative information during image generation via learning a gating mask to control the information flow. By iteratively employing this Residual Deep-Masking Cell, our proposed DMGN is able to generate both high-quality background image and noisy image progressively. Furthermore, we propose a two-pronged strategy to effectively leverage the generated noise image as contrasting cues to facilitate the refinement of the background image. Extensive experiments across three typical tasks for image background restoration, including image reflection removal, image rain steak removal and image dehazing, show that our DMGN consistently outperforms state-of-the-art methods specifically designed for each single task.Vision-language research has become very popular, which focuses on understanding of visual contents, language semantics and relationships between them. Video question answering (Video QA) is one of the typical tasks. Recently, several BERT style pre-training methods have been proposed and shown effectiveness on various vision-language tasks. In this work, we leverage the successful vision-language transformer structure to solve the Video QA problem. However, we do not pre-train it with any video data, because video pre-training requires massive computing resources and is hard to perform