Semantic Graph Convolutional Networks for 3D Human Pose Regression

In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for regression. Current architectures of GCNs are limited to the small receptive field of convolution filters and shared transformation matrix for each node. To address these limitations, we propose Semantic Graph Convolutional Networks (SemGCN), a novel neural network architecture that operates on regression tasks with graph-structured data. SemGCN learns to capture semantic information such as local and global node relationships, which is not explicitly represented in the graph. These semantic relationships can be learned through end-to-end training from the ground truth without additional supervision or hand-crafted rules. We further investigate applying SemGCN to 3D human pose regression. Our formulation is intuitive and sufficient since both 2D and 3D human poses can be represented as a structured graph encoding the relationships between joints in the skeleton of a human body. We carry out comprehensive studies to validate our method. The results prove that SemGCN outperforms state of the art while using 90% fewer parameters.Comment: In CVPR 2019 (13 pages including supplementary material). The code can be found at https://github.com/garyzhao/SemGC

Effects of thrombocytopenia in pregnancy

Background: Thrombocytopenia defined as platelet count of less than 1,50,000/cu.mm. Thrombocytopenia is divided according to severity into mild moderate and severe types. Multiple factors are responsible.Methods: This is a retrospective study of one-year period including 120 pregnant patients irrespective of their gestational age at civil hospital Ahmedabad. Etiology of this condition are identified and analyzed.Results: Gestational Thrombocytopenia is the most common etiology. This condition is self-limiting usually.Conclusions: Platelet count estimation should be a routine at first antenatal visit for timely diagnosis and to achieve favorable fetomaternal outcome.

Authoring Multi-Actor Behaviors in Crowds With Diverse Personalities

Multi-actor simulation is critical to cinematic content creation, disaster and security simulation, and interactive entertainment. A key challenge is providing an appropriate interface for authoring high-fidelity virtual actors with featurerich control mechanisms capable of complex interactions with the environment and other actors. In this chapter, we present work that addresses the problem of behavior authoring at three levels: Individual and group interactions are conducted in an event-centric manner using parameterized behavior trees, social crowd dynamics are captured using the OCEAN personality model, and a centralized automated planner is used to enforce global narrative constraints on the scale of the entire simulation. We demonstrate the benefits and limitations of each of these approaches and propose the need for a single unifying construct capable of authoring functional, purposeful, autonomous actors which conform to a global narrative in an interactive simulation

Environmental effect on egress simulation

Abstract. Evacuation and egress simulations can be a useful tool for studying the effect of design decisions on the flow of agent movement. This type of simulation can be used to determine before hand the effect of design decisions and enable exploration of potential improvements. In this work, we study at how agent egress is affected by the environment in real world and large scale virtual environments and investigate metrics to analyze the flow. Our work differs from many evacuation systems in that we support grouping restrictions between agents (e.g., families or other social groups traveling together), and model scenarios with multiple modes of transportation with physically realistic dynamics (e.g., individuals walk from a building to their own cars and leave only when all people in the group arrive).

Understanding ethnic inequalities in hearing health in the UK: a cross-sectional study of the link between language proficiency and performance on the Digit Triplet Test

Introduction Research using the UK Biobank data has shown ethnic inequalities in hearing health; however, the hearing test used may exhibit a disadvantage for non-native language speakers. Objectives To validate the results of the UK Biobank hearing test (Digit Triplet Test, DTT) against self-reported measures of hearing in the dataset and create classifications of hearing health. To observe if language proficiency and migration age have the same effect on hearing health classification as on the DTT in isolation. Our hypothesis is that language proficiency acts differently on the DTT, demonstrating that the DTT is biased for non-native speakers of English. Design Latent classes representing profiles of hearing health were identified from the available hearing measures. Factors associated with class membership were tested using multinomial logistic regression models. Ethnicity was defined as (1) White, native English-speaking, (2) ethnic minority, arrived in the UK aged 12. Participants The UK Biobank participants with valid hearing test results and associated covariates (N=151 268). Outcome measures DTT score, self-reported hearing difficulty, self-reported hearing difficulty in noise and hearing aid use. Results Three classes of hearing health were found: ‘normal’, ‘generally poor’ and ‘only subjectively poor’. In a model adjusting for known confounders of hearing loss, a poor or insufficient hearing test result was less likely for those with better language (OR 0.69, 95% CI 0.65 to 0.74) or numerical ability (OR 0.71, 95% CI 0.67 to 0.75) but more likely for those having migrated aged >12 (OR 3.85, 95% CI 3.64 to 4.07). Conclusions The DTT showed evidence of bias, having greater dependence on language ability and migration age than other hearing indicators. Designers of future surveys and hearing screening applications may wish to consider the limitations of speech-in-noise tests in evaluating hearing acuity for populations that include non-native speakers