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In recent years, aiming to enhance and extend user experiences beyond the real world, Extended Reality (XR) has emerged to become a new paradigm that enables a plethora of applications [1], e.g., online gaming, online conferencing, social media, etc. XR refers to the human-machine interactions that combine real and virtual environments with the support of computing/communications technologies and wearable devices. The XR content is generated by providers or other users, including audio, video and other metadata. In general, the generated XR content is transmitted to XR devices and rendered into XR scenes (i.e., to generate an image from a 2D or 3D model by means of a computer program), where users can experience a hybrid experience of the real and virtual worlds.

 

We propose using surface and aerial shared autonomous electric vehicles (SAEVs) to improve the resilience of infrastructure and communities, or SAEV-R. In disruptive events, SAEVs can be temporarily deployed to evacuate and rescue at-risk populations, provide essential supplies and services to vulnerable households, and transport repair crews and equipment. We present a modeling framework for feasibility analysis and strategic planning associated with deploying SAEVs for disaster relief. The framework guides our examination of three scenarios: a hurricane-induced power outage, a pandemic-affected vulnerable population, and earthquake-damaged infrastructure. The results demonstrate the flexibility of the proposed framework and showcase the potential and versatility of SAEV-R systems to improve resilience. 

The prevalent point cloud compression (PCC) standards of today are utilized to encode various types of point cloud data, allowing for reasonable bandwidth and storage usage. With increasing demand for high-fidelity three-dimensional (3D) models for a large variety of applications, including immersive visual communication, Augmented reality (AR) and Virtual Reality (VR), navigation, autonomous driving, and smart city, point clouds are seeing increasing usage and development to meet the increasing demands. However, with the advancements in 3D modelling and sensing, the amount of data required to accurately depict such representations and models is likewise ballooning to increasingly large proportions, leading to the development and standardization of the point cloud compression standards. In this article, we provide an overview of some topical and popular MPEG point cloud compression (PCC) standards. We discuss the development and applications of the Geometry-based PCC (G-PCC) and Video-based PCC (V-PCC) standards as they escalate in importance in an era of virtual reality and machine learning. Finally, we conclude our article describing the future research directions and applications of the PCC standards of today.

 

Knowledge-dependent tasks typically use two sources of knowledge: parametric, learned at training time, and contextual, given as a passage at inference time. To understand how models use these sources together, we formalize the problem of knowledge conflicts, where the contextual information contradicts the learned information. Analyzing the behaviour of popular models, we measure their over-reliance on memorized information (the cause of hallucinations), and uncover important factors that exacerbate this behaviour. Lastly, we propose a simple method to mitigate over-reliance on parametric knowledge, which minimizes hallucination, and improves out-of-distribution generalization by 4% - 7%. Our findings demonstrate the importance for practitioners to evaluate model tendency to hallucinate rather than read, and show that our mitigation strategy encourages generalization to evolving information (i.e. time-dependent queries). To encourage these practices, we have released our framework for generating knowledge conflicts.