A Study of Actor and Action Semantic Retention in Video Supervoxel Segmentation

Existing methods in the semantic computer vision community seem unable to deal with the explosion and richness of modern, open-source and social video content. Although sophisticated methods such as object detection or bag-of-words models have been well studied, they typically operate on low level features and ultimately suffer from either scalability issues or a lack of semantic meaning. On the other hand, video supervoxel segmentation has recently been established and applied to large scale data processing, which potentially serves as an intermediate representation to high level video semantic extraction. The supervoxels are rich decompositions of the video content: they capture object shape and motion well. However, it is not yet known if the supervoxel segmentation retains the semantics of the underlying video content. In this paper, we conduct a systematic study of how well the actor and action semantics are retained in video supervoxel segmentation. Our study has human observers watching supervoxel segmentation videos and trying to discriminate both actor (human or animal) and action (one of eight everyday actions). We gather and analyze a large set of 640 human perceptions over 96 videos in 3 different supervoxel scales. Furthermore, we conduct machine recognition experiments on a feature defined on supervoxel segmentation, called supervoxel shape context, which is inspired by the higher order processes in human perception. Our ultimate findings suggest that a significant amount of semantics have been well retained in the video supervoxel segmentation and can be used for further video analysis.Comment: This article is in review at the International Journal of Semantic Computin

2004 MICHIGAN LAND VALUES

Land Economics/Use,

Predicting E-Cigarette Use Among Emerging Adults Using Perceived Social Norms and Outcome Expectancies

Despite low rates of combustible tobacco use rates among adolescents and young adults, e-cigarettes continue to gain popularity. A few factors have been shown to be related to e-cigarette use based on prior research. One such example is social enhancement expectancies. Additionally, greater perceptions of harm have been found to be inversely related to e-cigarette use such that those that expect increased risk to their health are less likely to report using e-cigarettes. I hypothesized that social enhancement expectancies would mediate the relationship between perceptions of social norms and e-cigarette dependence. I also hypothesized that perceived harm, such as greater perceived health risks, would moderate the indirect effect of perceived social norms and e-cigarette dependence. The same analyses were also examined with a dichotomous e-cigarette user status outcome variable. E-cigarette use status was determined based on past 30- day use of e-cigarettes. Analyses revealed that injunctive norms emerged as a significant predictor of both positive social outcome expectancies and e-cigarette user status. Perceived harm was also found to be a significant predictor of e-cigarette dependence. Further exploration of within group differences among e-cigarette users may be warranted in order to develop an intervention strategy tailored to this group

Dense Sample Deep Learning

Deep Learning (DL) , a variant of the neural network algorithms originally proposed in the 1980s, has made surprising progress in Artificial Intelligence (AI), ranging from language translation, protein folding, autonomous cars, and more recently human-like language models (CHATbots), all that seemed intractable until very recently. Despite the growing use of Deep Learning (DL) networks, little is actually understood about the learning mechanisms and representations that makes these networks effective across such a diverse range of applications. Part of the answer must be the huge scale of the architecture and of course the large scale of the data, since not much has changed since 1987. But the nature of deep learned representations remain largely unknown. Unfortunately training sets with millions or billions of tokens have unknown combinatorics and Networks with millions or billions of hidden units cannot easily be visualized and their mechanisms cannot be easily revealed. In this paper, we explore these questions with a large (1.24M weights; VGG) DL in a novel high density sample task (5 unique tokens with at minimum 500 exemplars per token) which allows us to more carefully follow the emergence of category structure and feature construction. We use various visualization methods for following the emergence of the classification and the development of the coupling of feature detectors and structures that provide a type of graphical bootstrapping, From these results we harvest some basic observations of the learning dynamics of DL and propose a new theory of complex feature construction based on our results

Ethical Aspects of COVID-19 Antibody Testing

One common COVID-19 test is the test for one or more of the antibodies the body creates when it encounters the COVID-19 virus. Because these tests are often Point-of-Care, rapid tests that require only a blood sample they may appear to patients as an easily accessible and useful tool for guiding their actions in the pandemic. However, serologic antibody tests should not be offered to patients in normal practice under nearly all circumstances. They are useful in narrow diagnostic settings in later stage infections and they serve an important public health function, but they are not of benefit to patients and may in fact give false and potentially harmful information to patients of moderate health literacy