Envisioning a Next Generation Extended Reality Conferencing System with Efficient Photorealistic Human Rendering

Meeting online is becoming the new normal. Creating an immersive experience for online meetings is a necessity towards more diverse and seamless environments. Efficient photorealistic rendering of human 3D dynamics is the core of immersive meetings. Current popular applications achieve real-time conferencing but fall short in delivering photorealistic human dynamics, either due to limited 2D space or the use of avatars that lack realistic interactions between participants. Recent advances in neural rendering, such as the Neural Radiance Field (NeRF), offer the potential for greater realism in metaverse meetings. However, the slow rendering speed of NeRF poses challenges for real-time conferencing. We envision a pipeline for a future extended reality metaverse conferencing system that leverages monocular video acquisition and free-viewpoint synthesis to enhance data and hardware efficiency. Towards an immersive conferencing experience, we explore an accelerated NeRF-based free-viewpoint synthesis algorithm for rendering photorealistic human dynamics more efficiently. We show that our algorithm achieves comparable rendering quality while performing training and inference 44.5% and 213% faster than state-of-the-art methods, respectively. Our exploration provides a design basis for constructing metaverse conferencing systems that can handle complex application scenarios, including dynamic scene relighting with customized themes and multi-user conferencing that harmonizes real-world people into an extended world.Comment: Accepted to CVPR 2023 ECV Worksho

Sensitivity to Antibiotics of Bacteria Exposed to Gamma Radiation Emitted from Hot Soils of the High Background Radiation Areas of Ramsar, Northern Iran

Background: Over the past several years our laboratories have investigated different aspects of the challenging issue of the alterations in bacterial susceptibility to antibiotics induced by physical stresses. Objective: To explore the bacterial susceptibility to antibiotics in samples of Salmonella enterica subsp. enterica serovar Typhimurium (S. typhimurium), Staphylococcus aureus, and Klebsiella pneumoniae after exposure to gamma radiation emitted from the soil samples taken from the high background radiation areas of Ramsar, northern Iran. Methods: Standard Kirby-Bauer test, which evaluates the size of the zone of inhibition as an indicator of the susceptibility of different bacteria to antibiotics, was used in this study. Results: The maximum alteration of the diameter of inhibition zone was found for K. pneumoniae when tested for ciprofloxacin. In this case, the mean diameter of no growth zone in non-irradiated control samples of K. pneumoniae was 20.3 (SD 0.6) mm; it was 14.7 (SD 0.6) mm in irradiated samples. On the other hand, the minimum changes in the diameter of inhibition zone were found for S. typhimurium and S. aureus when these bacteria were tested for nitrofurantoin and cephalexin, respectively. Conclusion: Gamma rays were capable of making significant alterations in bacterial susceptibility to antibiotics. It can be hypothesized that high levels of natural background radiation can induce adaptive phenomena that help microorganisms better cope with lethal effects of antibiotics

Sets and Types: A Review

this paper

Novel approach to big data collaboration with network operators network function virtualisation (NFV)

The intersection of network function virtualisation (NFV) technologies and big data has the potential of revolutionising today\u27s telecommunication networks from deployment to operations resulting in significant reductions in capital expenditure (CAPEX) and operational expenditure, as well as cloud vendor and additional revenue growths for the operators. One of the contributions of this article is the comparisons of the requirements for big data and network virtualisation and the formulation of the key performance indicators for the distributed big data NFVs at the operator\u27s infrastructures. Big data and virtualisation are highly interdependent and their intersections and dependencies are analysed and the potential optimisation gains resulted from open interfaces between big data and carrier networks NFV functional blocks for an adaptive environment are then discussed. Another contribution of this article is a comprehensive discussion on open interface recommendations which enables global collaborative and scalable virtualised big data applications. <br /

Effect of Noise and Carbon Monoxide Exposure on Plasma Antioxidant Ability and Blood Glutathione in Rabbits

Background: Noise induced hearing loss is one of the top ten occupational diseases in worldwide and carbon monoxide (CO) can potentiate this disorder. Oxidative stress have been implicated in cochlear damage resulting from exposure to noise ,but mechanisms underlying auditory impairment following exposure to noise plus CO are not well known. This study was performed to evaluate the effect of noise and CO exposure on blood antioxidant and blood glutathione (GSH) status in rabbits. Methods: In this experimental study, 24 male adult white rabbits are divided to four groups: control group, noise exposure group, noise plus carbon monoxide exposure group and CO exposure group. Ferric reducing ability of plasma (FRAP assay) and total blood GSH of groups were determined before and after exposure. Results: Mean of total antioxidant ability of plasma after exposure to noise and CO were 707.5&plusmn; 31.7 and 811.3&plusmn; 51.8 &micro;mol/lit. Blood GSH of these groups were 1.39&plusmn; 0.09 and 1.79&plusmn;0.08 mg/dl RBCs respectively. These values were less than the values in control group significantly ( p value <0.05). Conclusion: Exposure to noise and noise plus CO can reduce blood antioxidant ability and blood GSH, but the role of noise in the reduction is more than CO

DCODE: A Distributed Column-Oriented Database Engine for Big Data Analytics

Part 10: Big Data and Text MiningInternational audienceWe propose a novel Distributed Column-Oriented Database Engine (DCODE) for efficient analytic query processing that combines advantages of both column storage and parallel processing. In DCODE, we enhance an existing open-source columnar database engine by adding the capability for handling queries over a cluster. Specifically, we studied parallel query execution and optimization techniques such as horizontal partitioning, exchange operator allocation, query operator scheduling, operator push-down, and materialization strategies, etc. The experiments over the TPC-H dataset verified the effectiveness of our system