Augmenting virtual reality telepresence experience using self-avatar

Abstract. Telepresence refers to a set of techniques that creates the illusion of being present at a remote location to a person. Telepresence may also include the ability to interact with the remote environment, including communication with people physically present at the remote location. In this research, the introduction of a virtual body, which mirrors the user’s own movement in real-time, in a telepresence scenario and its effect on the illusion of presence is studied. Earlier research works have shown the effectiveness of having a virtual body in simulated environments, for example, games. In this study, the user embodies a virtual body that is present in a simulated environment, surrounded by a sphere where footage streamed from a 360-degree camera, mounted at a different spot, is being projected. This gives the user a sense of being present in a real location and having a body which they can control. The study is conducted on 20 participants, where the participants put on a Head-Mounted Display showing live footage from a 360-degree camera while having a real-time conversation with a confederate present near the camera. They are then surveyed about their experience, both with and without a virtual body to determine if having a virtual body yielded any improvement on the illusion of presence. Although 18 of the 20 participants preferred the experience with a body, it did not necessarily increase their sense of presence when compared with the scores given when there is no visible body. These results implicate a low sample size, not enough to draw any meaningful conclusions

New Approximability Results for the Robust k-Median Problem

We consider a robust variant of the classical $k$-median problem, introduced by Anthony et al. \cite{AnthonyGGN10}. In the \emph{Robust $k$-Median problem}, we are given an $n$-vertex metric space $(V,d)$ and $m$ client sets $\set{S_i \subseteq V}_{i=1}^m$. The objective is to open a set $F \subseteq V$ of $k$ facilities such that the worst case connection cost over all client sets is minimized; in other words, minimize $\max_{i} \sum_{v \in S_i} d(F,v)$. Anthony et al.\ showed an $O(\log m)$ approximation algorithm for any metric and APX-hardness even in the case of uniform metric. In this paper, we show that their algorithm is nearly tight by providing $\Omega(\log m/ \log \log m)$ approximation hardness, unless ${\sf NP} \subseteq \bigcap_{\delta >0} {\sf DTIME}(2^{n^{\delta}})$. This hardness result holds even for uniform and line metrics. To our knowledge, this is one of the rare cases in which a problem on a line metric is hard to approximate to within logarithmic factor. We complement the hardness result by an experimental evaluation of different heuristics that shows that very simple heuristics achieve good approximations for realistic classes of instances.Comment: 19 page

A system for production of defective interfering particles in the absence of infectious influenza A virus

<div><p>Influenza A virus (IAV) infection poses a serious health threat and novel antiviral strategies are needed. Defective interfering particles (DIPs) can be generated in IAV infected cells due to errors of the viral polymerase and may suppress spread of wild type (wt) virus. The antiviral activity of DIPs is exerted by a DI genomic RNA segment that usually contains a large deletion and suppresses amplification of wt segments, potentially by competing for cellular and viral resources. DI-244 is a naturally occurring prototypic segment 1-derived DI RNA in which most of the PB2 open reading frame has been deleted and which is currently developed for antiviral therapy. At present, coinfection with wt virus is required for production of DI-244 particles which raises concerns regarding biosafety and may complicate interpretation of research results. Here, we show that cocultures of 293T and MDCK cell lines stably expressing codon optimized PB2 allow production of DI-244 particles solely from plasmids and in the absence of helper virus. Moreover, we demonstrate that infectivity of these particles can be quantified using MDCK-PB2 cells. Finally, we report that the DI-244 particles produced in this novel system exert potent antiviral activity against H1N1 and H3N2 IAV but not against the unrelated vesicular stomatitis virus. This is the first report of DIP production in the absence of infectious IAV and may spur efforts to develop DIPs for antiviral therapy.</p></div

Application of Mixture Models for Doubly Inflated Count Data

In health and social science and other fields where count data analysis is important, zero-inflated models have been employed when the frequency of zero count is high (inflated). Due to multiple reasons, there are scenarios in which an additional count value of k \u3e 0 occurs with high frequency. The zero- and k-inflated Poisson distribution model (ZkIP) is more appropriate for such situations. The ZkIP model is a mixture distribution with three components: degenerate distributions at 0 and k count and a Poisson distribution. In this article, we propose an alternative and computationally fast expectation–maximization (EM) algorithm to obtain the parameter estimates for grouped zero and k-inflated count data. The asymptotic standard errors are derived using the complete data approach. We compare the zero- and k-inflated Poisson model with its zero-inflated and non-inflated counterparts. The best model is selected based on commonly used criteria. The theoretical results are supplemented with the analysis of two real-life datasets from health sciences

EM Estimation for Zero- and \u3ci\u3ek\u3c/i\u3e-Inflated Poisson Regression Model

Count data with excessive zeros are ubiquitous in healthcare, medical, and scientific studies. There are numerous articles that show how to fit Poisson and other models which account for the excessive zeros. However, in many situations, besides zero, the frequency of another count k tends to be higher in the data. The zero- and k-inflated Poisson distribution model (ZkIP) is appropriate in such situations The ZkIP distribution essentially is a mixture distribution of Poisson and degenerate distributions at points zero and k. In this article, we study the fundamental properties of this mixture distribution. Using stochastic representation, we provide details for obtaining parameter estimates of the ZkIP regression model using the Expectation-Maximization (EM) algorithm for a given data. We derive the standard errors of the EM estimates by computing the complete, missing, and observed data information matrices. We present the analysis of two real-life data using the methods outlined in the paper

Software Cost Estimation using Single Layer Artificial Neural Network

The most challenging task of software project management is the cost estimation. Cost estimation is to accurately assess required assets and schedules for software improvement ventures and it includes a number of things under its wide umbrella, for example, estimation of the size of the software product to be produced, estimation of the effort required, and last but not the least estimating the cost of the project. The overall project life cycle is impacted by the accurate prediction of the software development cost. The COCOMO model makes employments of single layer feed forward neural system while being actualized and prepared to utilize the perceptron learning algorithm. To test and prepare the system the COCOMO dataset is actualized. This paper has the goal of creating the quantitative measure in not only the current model but also in our proposed model