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.

Amelioration of carbon tetrachloride induced hepatotoxicity in rats by standardized Feronia limonia. Linn leaf extracts

The hepatoprotective potential of standardized Feronia limonia (Family, Rutaceae) methanolic extract (FL-7) and chloroform soluble fraction (FL-9) were assessed against carbon tetrachloride (CCl4) induced oxidative stress and hepatotoxicity in rats. Rats treated with CCl4 recorded significant elevation in plasma markers of hepatic injury, alteration in hepatic antioxidant status and histopathological damages. However, rats pretreated with FL-7 (200 or 400 mg/kg, p.o.) and FL-9 (100 or 200 mg/kg, p.o.) for 7 days and later administered CCl4 (0.5 ml/kg, i.p.) recorded lowered indices of the above mentioned parameters and minimal histological damage in a dose dependent manner. These results were comparable to that of CCl4+silymarin treated rats. The results obtained with FL-7 and FL-9 are attributable to their free radical scavenging potential due to high contents of polyphenols and flavonols recorded herein. Overall, this study establishes the efficacy of FL-7 and FL-9 as hepatoprotective agents against CCl4 induced hepatotoxicity in rats

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).