BackgroundProlonged time on sedentary behavior, especially screen-based sitting time, is associated with unfavorable health indicators in children and adolescents. However, the effects of sedentary behavior on cognitive function remain to be elucidated.ObjectiveThe purpose of this systematic review was to synthesize the evidence on the associations of sedentary behavior with executive function in children and adolescents.MethodsFour electronic databases (i.e., PubMed, Web of Science, PsycINFO, and SPORTDiscus) were searched for studies examining the associations between sedentary behavior and executive function in children and adolescents. Study quality was assessed by the NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies.ResultsA total of 1,151 records were initially identified through database searches and other searches. Twelve cross-sectional and four longitudinal studies met the inclusion criteria. Of the 16 studies, seven studies found significant negative associations between sedentary behavior and executive function, and two studies presented positive associations. Eight studies measured sedentary time using accelerometers and showed varied associations between objectively measured sedentary time and executive function. Nine studies measured screen-based sedentary behavior, of which five studies found negative associations of sedentary time with executive function.ConclusionThe available evidence on the associations between sedentary behavior and executive function is not conclusive in children and adolescents. However, screen-based sedentary behavior may be negatively associated with executive function.

Despite the extraordinary applicative potentiality that dynamic graph inference may entail, its practical-physical implementation has been a topic seldom explored in literature. Although graph inference through neural networks has received plenty of algorithmic innovation, its transfer to the physical world has not found similar development. This is understandable since the most preeminent Euclidean acceleration techniques from CNN have little implication in the non-Euclidean nature of relational graphs. Instead of coping with the challenges arising from forcing naturally sparse structures into more inflexible stochastic arrangements, in DRAGON, we embrace this characteristic in order to promote acceleration. Inspired by high-performance computing approaches like Parallel Multi-moth Flame Optimization for Link Prediction (PMFO-LP), we propose and implement a novel efficient architecture, capable of producing similar speed-up and performance than baseline but at a fraction of its hardware requirements and power consumption. We leverage the hidden parallelistic capacity of our previously developed static graph convolutional processor ACE-GCN and expanded it with RNN structures, allowing the deployment of a multi-processing network referenced around a common pool of proximity-based centroids. Experimental results demonstrate outstanding acceleration. In comparison with the fastest CPU-based software implementation available in the literature, DRAGON has achieved roughly 191 × speed-up. Under the largest configuration and dataset, DRAGON was also able to overtake a more power-hungry PMFO-LP by almost 1.59 × in speed, and at around 89.59 \(\% \) in power efficiency. More importantly than raw acceleration, we demonstrate the unique functional qualities of our approach as a flexible and fault-tolerant solution that makes it an interesting alternative for an anthology of applicative scenarios.

ACE-GCN is a fast and resource/energy-efficient FPGA accelerator for graph convolutional embedding under data-driven and in-place processing conditions. Our accelerator exploits the inherent power law distribution and high sparsity commonly exhibited by real-world graphs datasets. Contrary to other hardware implementations of GCN, on which traditional optimization techniques are employed to bypass the problem of dataset sparsity, our architecture is designed to take advantage of this very same situation. We propose and implement an innovative acceleration approach supported by our “implicit-processing-by-association” concept, in conjunction with a dataset-customized convolutional operator. The computational relief and consequential acceleration effect arise from the possibility of replacing rather complex convolutional operations for a faster embedding result estimation. Based on a computationally inexpensive and super-expedited similarity calculation, our accelerator is able to decide from the automatic embedding estimation or the unavoidable direct convolution operation. Evaluations demonstrate that our approach presents excellent applicability and competitive acceleration value. Depending on the dataset and efficiency level at the target, between 23× and 4,930× PyG baseline, coming close to AWB-GCN by 46% to 81% on smaller datasets and noticeable surpassing AWB-GCN for larger datasets and with controllable accuracy loss levels. We further demonstrate the unique hardware optimization characteristics of our approach and discuss its multi-processing potentiality.

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