Religion in the Media: A Study of Student Perception of Media Bias in Georgia

Georgia is fighting to make the step from developing to developed and the influence of the Georgian Orthodox Church has been an identified barricade for European Union leadership to accept Georgia into the supranational organization. This research investigates the relationship between religiosity and the perception of media bias among college students in Tbilisi, Georgia. It was hypothesized that the relationship between religiosity and perception of media bias will be negative, as measured by survey administered to the students. This paper proves the more religious a student is, the less likely he or she will recognize a media bias towards the Georgian Orthodox Church. Similarly, students who are more religious use and trust domestic news sources than those who are less religious. The implications are that religiosity plays a role in how students are viewing the news and that religious affiliation can alter how someone critically analyzes the information put forth

Comments and Suggestions for Improvement of the Archon Genomics X PRIZE Validation Protocol

This document is a comment on the X PRIZE validation protocol written by Kedes et al. (2011). We propose several modifications which we think will improve the fairness and transparency of the contest while keeping the cost of the validation process under control

Antiprotozoals based on the inhibition of N-Myristoyltransferase

The research performed a protozoal structure activity investigation based on the transition state of N-Myristoyltransferase. This initial investigation synthesised a simplified transition state mimetic based from myristic acid and included alkene and phenoxy variation of the myristoyl chain. A series of Inhibitors were developed using a coenzyme-A fragment. Structures were targeted that were based on the statins (HMG-CoA inhibitors). Some of the compounds developed showed micro molar activities towards T. brucei and P. falciparum

Learning to Navigate Cloth using Haptics

We present a controller that allows an arm-like manipulator to navigate deformable cloth garments in simulation through the use of haptic information. The main challenge of such a controller is to avoid getting tangled in, tearing or punching through the deforming cloth. Our controller aggregates force information from a number of haptic-sensing spheres all along the manipulator for guidance. Based on haptic forces, each individual sphere updates its target location, and the conflicts that arise between this set of desired positions is resolved by solving an inverse kinematic problem with constraints. Reinforcement learning is used to train the controller for a single haptic-sensing sphere, where a training run is terminated (and thus penalized) when large forces are detected due to contact between the sphere and a simplified model of the cloth. In simulation, we demonstrate successful navigation of a robotic arm through a variety of garments, including an isolated sleeve, a jacket, a shirt, and shorts. Our controller out-performs two baseline controllers: one without haptics and another that was trained based on large forces between the sphere and cloth, but without early termination.Comment: Supplementary video available at https://youtu.be/iHqwZPKVd4A. Related publications http://www.cc.gatech.edu/~karenliu/Robotic_dressing.htm

The use of genetic algorithms to maximize the performance of a partially lined screened room

This paper shows that it is possible to use genetic algorithms to optimize the layout of ferrite tile absorber in a partially lined screened enclosure to produce a "best" performance. The enclosure and absorber are modeled using TLM modeling techniques and the performance is determined by comparison with theoretical normalized site attenuation of free space. The results show that it is possible to cover just 80% of the surface of the enclosure with ferrite absorber and obtain a response which is within +/-4 dB of the free space response between 40 and 200 MHz

ACE: Adversarial Correspondence Embedding for Cross Morphology Motion Retargeting from Human to Nonhuman Characters

Motion retargeting is a promising approach for generating natural and compelling animations for nonhuman characters. However, it is challenging to translate human movements into semantically equivalent motions for target characters with different morphologies due to the ambiguous nature of the problem. This work presents a novel learning-based motion retargeting framework, Adversarial Correspondence Embedding (ACE), to retarget human motions onto target characters with different body dimensions and structures. Our framework is designed to produce natural and feasible robot motions by leveraging generative-adversarial networks (GANs) while preserving high-level motion semantics by introducing an additional feature loss. In addition, we pretrain a robot motion prior that can be controlled in a latent embedding space and seek to establish a compact correspondence. We demonstrate that the proposed framework can produce retargeted motions for three different characters -- a quadrupedal robot with a manipulator, a crab character, and a wheeled manipulator. We further validate the design choices of our framework by conducting baseline comparisons and a user study. We also showcase sim-to-real transfer of the retargeted motions by transferring them to a real Spot robot

Vascular changes in diabetic retinopathy-a longitudinal study in the Nile rat.

Diabetic retinopathy is the most common microvascular complication of diabetes and is a major cause of blindness, but an understanding of the pathogenesis of the disease has been hampered by a lack of accurate animal models. Here, we explore the dynamics of retinal cellular changes in the Nile rat (Arvicanthis niloticus), a carbohydrate-sensitive model for type 2 diabetes. The early retinal changes in diabetic Nile rats included increased acellular capillaries and loss of pericytes that correlated linearly with the duration of diabetes. These vascular changes occurred in the presence of microglial infiltration but in the absence of retinal ganglion cell loss. After a prolonged duration of diabetes, the Nile rat also exhibits a spectrum of retinal lesions commonly seen in the human condition including vascular leakage, capillary non-perfusion, and neovascularization. Our longitudinal study documents a range and progression of retinal lesions in the diabetic Nile rat remarkably similar to those observed in human diabetic retinopathy, and suggests that this model will be valuable in identifying new therapeutic strategies

iSDF: Real-Time Neural Signed Distance Fields for Robot Perception

We present iSDF, a continual learning system for real-time signed distance field (SDF) reconstruction. Given a stream of posed depth images from a moving camera, it trains a randomly initialised neural network to map input 3D coordinate to approximate signed distance. The model is self-supervised by minimising a loss that bounds the predicted signed distance using the distance to the closest sampled point in a batch of query points that are actively sampled. In contrast to prior work based on voxel grids, our neural method is able to provide adaptive levels of detail with plausible filling in of partially observed regions and denoising of observations, all while having a more compact representation. In evaluations against alternative methods on real and synthetic datasets of indoor environments, we find that iSDF produces more accurate reconstructions, and better approximations of collision costs and gradients useful for downstream planners in domains from navigation to manipulation. Code and video results can be found at our project page: https://joeaortiz.github.io/iSDF/ .Comment: Project page: https://joeaortiz.github.io/iSDF

Rheological Behavior and in Situ Confocal Imaging of Bijels Made by Mixing

International audienc