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Teleporting, or jumping, is a common method of moving through virtual environments. It provides a simple user interface, but deprives users of self-motion cues that are important to acquiring spatial knowledge. This paper examines one parameter of the teleportation interface, the teleportation or jump distance, and how that may affect spatial knowledge acquisition. We report the results of an experiment that examined the effects of two different, but fixed teleportation distances on how users could acquire knowledge of landmarks and routes. The results suggest that the teleport distance does not matter, hence teleportation as an interface is robust. However, use of teleportation resulted in significantly increased simulator sickness, a surprising result. 

In computer-aided drug discovery, quantitative structure activity relation models are trained to predict biological activity from chemical structure. Despite the recent success of applying graph neural network to this task, important chemical information such as molecular chirality is ignored. To fill this crucial gap, we propose Molecular-Kernel Graph NeuralNetwork (MolKGNN) for molecular representation learning, which features SE(3)-/conformation invariance, chirality-awareness, and interpretability. For our MolKGNN, we first design a molecular graph convolution to capture the chemical pattern by comparing the atom's similarity with the learnable molecular kernels. Furthermore, we propagate the similarity score to capture the higher-order chemical pattern. To assess the method, we conduct a comprehensive evaluation with nine well-curated datasets spanning numerous important drug targets that feature realistic high class imbalance and it demonstrates the superiority of MolKGNN over other graph neural networks in computer-aided drug discovery. Meanwhile, the learned kernels identify patterns that agree with domain knowledge, confirming the pragmatic interpretability of this approach. Our code and supplementary material are publicly available at https://github.com/meilerlab/MolKGNN. 

Decades of research have shown that absolute egocentric distance is underestimated in virtual environments (VEs) when compared with the real world. This finding has implications on the use of VEs for applications that require an accurate sense of absolute scale. Fortunately, this underperception of scale can be attenuated by several factors, making perception more similar to (but still not the same as) that of the real world. Here, we examine these factors as two categories: (i) experience inherent to the observer, and (ii) characteristics inherent to the display technology. We analyse how these factors influence the sources of information for absolute distance perception with the goal of understanding how the scale of virtual spaces is calibrated. We identify six types of cues that change with these approaches, contributing both to a theoretical understanding of depth perception in VEs and a call for future research that can benefit from changing technologies. This article is part of the theme issue ‘New approaches to 3D vision’. 

Consumer level virtual experiences almost always occur when physical space is limited, either by the constraints of an indoor space or of a tracked area. This observation coupled with the need for movement through large virtual spaces has resulted in a proliferation of research into locomotion interfaces that decouples movement through the virtual environment from movement in the real world. While many locomotion interfaces support movement of some kind in the real world, some do not. This paper examines the effect of the amount of physical space used in the real world on one popular locomotion interface, resetting, when compared to a locomotion interface that requires minimal physical space, walking in place. The metric used to compare the two locomotion interfaces was navigation performance, specifically, the acquisition of survey knowledge. We find that, while there are trade-offs between the two methods, walking in place is preferable in small spaces. 

Although Augmented Reality (AR) can be easily implemented with most smartphones and tablets today, the investigation of distance perception with these types of devices has been limited. In this paper, we question whether the distance of a virtual human, e.g., avatar, seen through a smartphone or tablet display is perceived accurately. We also investigate, due to the Covid-19 pandemic and increased sensitivity to distances to others, whether a coughing avatar that either does or does not have a mask on affects distance estimates compared to a static avatar. We performed an experiment in which all participants estimated the distances to avatars that were either static or coughing, with and without masks on. Avatars were placed at a range of distances that would be typical for interaction, i.e., action space. Data on judgments of distance to the varying avatars was collected in a distributed manner by deploying an app for smartphones. Results showed that participants were fairly accurate in estimating the distance to all avatars, regardless of coughing condition or mask condition. Such findings suggest that mobile AR applications can be used to obtain accurate estimations of distances to virtual others "in the wild," which is promising for using AR for simulations and training applications that require precise distance estimates. 

Today, augmented reality (AR) is most easily experienced through a mobile device such as a modern smartphone. For AR to be useful for applications such as training, it is important to understand how people perceive interactions with virtual objects presented to them via mobile AR. In this paper, we investigated two judgments of action capabilities (affordances) with virtual objects presented through smartphones: passing through an aperture and stepping over a gap. Our goals were to 1) determine if people can reliably scale these judgments to their body dimensions or capabilities and 2) explore whether cues presented in the context of the action could change their judgments. Assessments of perceived action capabilities were made in a pre/post-test design in which observers judged their affordances towards virtual objects prior to seeing an AR cue denoting their body dimension/capability, while viewing the cue, and after seeing the cue. Different patterns of results were found for the two affordances. For passing through, estimates became closer to shoulder width in the post-cue compared to the pre-cue block. For gap stepping, estimates were closer to actual stepping capability while viewing the cue, but did not persist when the cue was no longer present. Overall, our findings show that mobile smartphones can be used to assess perceived action capabilities with virtual targets and that AR cues can influence the perception of action capabilities in these devices. Our work provides a foundation for future studies investigating perception with the use of mobile AR with smartphones.