Raving

https://digitalcommons.library.umaine.edu/mmb-vp/6485/thumbnail.jp

SelectionConv: Convolutional Neural Networks for Non-rectilinear Image Data

Convolutional Neural Networks have revolutionized vision applications. There are image domains and representations, however, that cannot be handled by standard CNNs (e.g., spherical images, superpixels). Such data are usually processed using networks and algorithms specialized for each type. In this work, we show that it may not always be necessary to use specialized neural networks to operate on such spaces. Instead, we introduce a new structured graph convolution operator that can copy 2D convolution weights, transferring the capabilities of already trained traditional CNNs to our new graph network. This network can then operate on any data that can be represented as a positional graph. By converting non-rectilinear data to a graph, we can apply these convolutions on these irregular image domains without requiring training on large domain-specific datasets. Results of transferring pre-trained image networks for segmentation, stylization, and depth prediction are demonstrated for a variety of such data forms.Comment: To be presented at ECCV 202

Managing sleep and wakefulness in a 24 hour world

This article contributes to literature on the sociology of sleep by exploring the sleeping practices and subjective sleep experiences of two social groups: shift workers and students. It draws on data, collected in the UK from 25 semi-structured interviews, to discuss the complex ways in which working patterns and social activities impact upon experiences and expectations of sleep in our wired awake world. The data show that, typically, sleep is valued and considered to be important for health, general wellbeing, appearance and physical and cognitive functioning. However, sleep time is often cut back on in favour of work demands and social activities. While shift workers described their efforts to fit in an adequate amount of sleep per 24-hour period, for students, the adoption of a flexible sleep routine was thought to be favourable for maintaining a work–social life balance. Collectively, respondents reported using a wide range of strategies, techniques, technologies and practices to encourage, overcome or delay sleep(iness) and boost, promote or enhance wakefulness/alertness at socially desirable times. The analysis demonstrates how social context impacts not only on how we come to think about sleep and understand it, but also how we manage or self-regulate our sleeping patterns

Reassessing the cardiac box: A comprehensive evaluation of the relationship between thoracic gunshot wounds and cardiac injury

Background: High energy missiles can cause cardiac injury regardless of entrance site. This study assesses the adequacy of the anatomic borders of the current “cardiac box” to predict cardiac injury. Methods: Retrospective autopsy review was performed to identify patients with penetrating torso gunshot wounds 2011-2013. Using a circumferential grid system around the thorax, logistic regression analysis was performed to detect differences in rates of cardiac injury from entrance/exit wounds in the “cardiac box” vs. the same for entrance/exit wounds outside the box. Analysis was repeated to identify regions to compare risk of cardiac injury between the current cardiac box and other regions of the thorax. Results: Over the study period, 263 patients (89% male, mean age = 34 years, median injuries/person = 2) sustained 735 wounds [80% gunshot wounds (GSWs], and 239 patients with 620 GSWs were identified for study. Of these, 95 (34%) injured the heart. Of the 257 GSWs entering the cardiac box, 31% caused cardiac injury while 21% GSWs outside the cardiac box (n = 67) penetrated the heart, suggesting that the current “cardiac box” is a poor predictor of cardiac injury relative to the thoracic non-"cardiac box" regions [Relative Risk (RR) 0.96; p=0.82]. The regions from the anterior to posterior midline of the left thorax provided the highest positive predictive value (41%) with high sensitivity (90%) while minimizing false positives making this region the most statistically significant discriminator of cardiac injury (RR 2.9; p=0.01). Conclusion: For GSWs, the current cardiac box is inadequate to discriminate whether a gunshot wound will cause a cardiac injury. As expected, entrance wounds nearest to the heart are the most likely to result in cardiac injury, but, from a clinical standpoint, it is best to think outside the “box” for GSWs to the thorax