Single Viewpoints Research Articles

Intersecting realms: a cross-disciplinary examination of VR quality of experience research

The advent of virtual reality (VR) technology has necessitated a reevaluation of quality of experience (QoE) models. While numerous recent efforts have been dedicated to creating comprehensive QoE frameworks it seems that the majority of the factors studied as potential influencers of QoE are often limited to single disciplinary viewpoints or specific user-related aspects. Furthermore, the majority of literature reviews in this domain seem to have predominantly focused on academic sources, overlooking industry insights. To address these points, the current research took an interdisciplinary literature review approach to examine QoE literature covering both academic and industry sources from diverse fields (i.e., psychology, ergonomics, user experience, communication science, and engineering). Based on this rich dataset, we created a QoE model that illustrated 252 factors grouped into four branches - user, system, context, and content. The main finding of this review emphasized the substantial gap in the current research landscape, where complex interactions among user, system, context, and content factors in VR are overlooked. The current research not only identified this crucial disparity in existing QoE studies but also provided a substantial online repository of over 200 QoE-related factors. The repository serves as an indispensable tool for future researchers aiming to construct a more holistic understanding of QoE.

Multiple embodiment relations: sense-making in dissociative experiences

ABSTRACT In response to a call for new conceptualizations of the self that avoid mind/body dualisms, recent research has introduced more flexible and fluid theorizations of personhood. This paper takes a phenomenological approach to examine the perceived shifts in body ownership and distribution of agency which underpin such theorizations. We focus on two dissociative experiences, namely, spirit possession and virtual reality, explicating how consumers can experience changes of perspective by disconnecting from the mainstream of their conscious awareness, allowing for a controlled “letting go,” resulting in therapeutic gains. We highlight how consumers create a larger assemblage of the self beyond single viewpoints for a new kind of experiencing. We argue that existing work has treated embodiment in a fairly symmetrical manner, insufficiently considering the inherently somatic characteristics of consumer experiences as an articulation of a multiplicity and distribution of embodiment and agentic relations, both human and non-human.

Quantifying errors in 3D CME parameters derived from synthetic data using white-light reconstruction techniques

Current efforts in space weather forecasting of CMEs have been focused on predicting their arrival time and magnetic structure. To make these predictions, methods have been developed to derive the true CME speed, size, position, and mass, among others. Difficulties in determining the input parameters for CME forecasting models arise from the lack of direct measurements of the coronal magnetic fields and uncertainties in estimating the CME 3D geometric and kinematic parameters after eruption. White-light coronagraph images are usually employed by a variety of CME reconstruction techniques that assume more or less complex geometries. This is the first study from our International Space Science Institute (ISSI) team “Understanding Our Capabilities in Observing and Modeling Coronal Mass Ejections”, in which we explore how subjectivity affects the 3D CME parameters that are obtained from the Graduated Cylindrical Shell (GCS) reconstruction technique, which is widely used in CME research. To be able to quantify such uncertainties, the “true” values that are being fitted should be known, which are impossible to derive from observational data. We have designed two different synthetic scenarios where the “true” geometric parameters are known in order to quantify such uncertainties for the first time. We explore this by using two sets of synthetic data: 1) Using the ray-tracing option from the GCS model software itself, and 2) Using 3D magnetohydrodynamic (MHD) simulation data from the Magnetohydrodynamic Algorithm outside a Sphere code. Our experiment includes different viewing configurations using single and multiple viewpoints. CME reconstructions using a single viewpoint had the largest errors and error ranges overall for both synthetic GCS and simulated MHD white-light data. As the number of viewpoints increased from one to two, the errors decreased by approximately 4° in latitude, 22° in longitude, 14° in tilt, and 10° in half-angle. Our results quantitatively show the critical need for at least two viewpoints to be able to reduce the uncertainty in deriving CME parameters. We did not find a significant decrease in errors when going from two to three viewpoints for our specific hypothetical three spacecraft scenario using synthetic GCS white-light data. As we expected, considering all configurations and numbers of viewpoints, the mean absolute errors in the measured CME parameters are generally significantly higher in the case of the simulated MHD white-light data compared to those from the synthetic white-light images generated by the GCS model. We found the following CME parameter error bars as a starting point for quantifying the minimum error in CME parameters from white-light reconstructions: Δθ (latitude)=6°-3°+2°, Δϕ (longitude)=11°-6°+18°, Δγ (tilt)=25°-7°+8°, Δα(half-angle)=10°-6°+12°, Δh (height)=0.6-0.4+1.2R⊙, and Δκ (ratio)=0.1-0.02+0.03.

Leveraging heat accumulation of district heating network to improve performances of integrated energy system under source-load uncertainties

The multiple uncertainties in renewable energy and loads and the thermoelectric coupling characteristic of the integrated energy system (IES) restrict the accommodation of renewable energy. The IES contains massive pipelines in its district heating network, which signifies the heat storage potential. This paper incorporates the dynamic performance of the district heating network into the multi-scenario optimization model to improve IES's operational performance. Herein, the graph theory and Kirchhoff law are employed to construct the dynamic model of district heating network from the single pipeline and network viewpoints, which characterizes the thermal accumulation performance. The stochastic scenarios are generated by combining Latin hypercube sampling for the initial scenarios and scenario curtailment 0–1 algorithm based on Wasserstein probability distance for the curtailment scenarios to capture the uncertainties. Then, a stochastic multi-scenario optimization method is proposed, which is implemented into a case study to analyze the influences of critical parameters and the performance improvement resulted from the network thermal accumulation. The results show that the scenario curtailment 0–1 algorithm can obtain stable and repeatable scenarios. Considering the heat accumulation characteristics of the district heating network can improve the economic performance by 2.41% and wind energy accommodation by 5.51%.

A Survey on Deep Learning Based Methods and Datasets for Monocular 3D Object Detection

Owing to recent advancements in deep learning methods and relevant databases, it is becoming increasingly easier to recognize 3D objects using only RGB images from single viewpoints. This study investigates the major breakthroughs and current progress in deep learning-based monocular 3D object detection. For relatively low-cost data acquisition systems without depth sensors or cameras at multiple viewpoints, we first consider existing databases with 2D RGB photos and their relevant attributes. Based on this simple sensor modality for practical applications, deep learning-based monocular 3D object detection methods that overcome significant research challenges are categorized and summarized. We present the key concepts and detailed descriptions of representative single-stage and multiple-stage detection solutions. In addition, we discuss the effectiveness of the detection models on their baseline benchmarks. Finally, we explore several directions for future research on monocular 3D object detection.

Mixed camera viewpoints improve learning medical hand procedure from video in nurse training?

Recent research has shown that camera viewpoints can influence how hand procedures are learnt from videos. However, studies have generally investigated videos showing only single viewpoints, for example, face-to-face or over-the-shoulder. Single views may not be appropriate for learning complex medical procedures involving spatial viewpoint changes. The goal of the present study was to investigate the effect of mixed camera viewpoints on learning a complex medical hand procedure from a video. Using a pretest-posttest paradigm, 43 students at a French nursing school had to learn a complex hand procedure from a video showing an expert nurse teacher inserting an indwelling catheter in a closed system using a simulation mannequin. Three video conditions were compared, delivering the same information in the same amount of time, from different viewpoints: (i) face-to-face only (FtF), (ii) over-the-shoulder only (OtS), and (iii) alternating face-to-face and over-the-shoulder views for each step of the procedure (MixW). The students were randomly assigned (N = 10–11) to one of four groups: the three experimental viewing conditions (FtF, OtS, MixW) and a control condition without video. Pre- and post-tests consisted of performing the hand procedure in a fully equipped full-scale simulation room. Results showed that learners in the mixed viewpoint (MixW) group performed better than the other groups (FtF, OtS, and control condition). Learners in the FtF and OtS groups outperformed those in the control group.

Using general-purpose compression algorithms for music analysis

General-purpose compression algorithms encode files as dictionaries of substrings with the positions of these strings’ occurrences. We hypothesized that such algorithms could be used for pattern discovery in music. We compared LZ77, LZ78, Burrows–Wheeler and COSIATEC on classifying folk song melodies. A novel method was used, combining multiple viewpoints, the k-nearest-neighbour algorithm and a novel distance metric, corpus compression distance. Using single viewpoints, COSIATEC outperformed the general-purpose compressors, with a classification success rate of 85% on this task. However, by combining 8 of the 10 best-performing viewpoints, including seven that used LZ77, the classification success rate rose to over 94%. In a second experiment, we compared LZ77 with COSIATEC on the task of discovering subject and countersubject entries in fugues by J.S. Bach. When voice information was absent in the input data, COSIATEC outperformed LZ77 with a mean score of 0.123, compared with 0.053 for LZ77. However, when the music was processed a voice at a time, the score for LZ77 more than doubled to 0.124. We also discovered a significant correlation between compression factor and score for all the algorithms, supporting the hypothesis that the best analyses are those represented by the shortest descriptions.

Consistent Partial Model Checking

We propose assertion-consistency (AC) semi-lattices as suitable orders for the analysis of partial models. Such orders express semantic entailment, multiple-viewpoint and multiple-valued analysis, maintain internal consistency of reasoning, and subsume nite De Morgan lattices. We classify those orders that are nite and distributive and apply them to design an ecien t algorithm for multiple-viewpoint checking, where checks are delegated to single-viewpoint models | ecien tly driven by the order structure. Instrumentations of this algorithm enable the detection and location of inconsistencies across viewpoint boundaries. To validate the approach, we investigate multiple-valued models and their compositional property semantics over a nite distributive AC lattice. We prove that this semantics is computed by our algorithm above whenever the primes of the AC lattice determine ‘projected’ single viewpoints and the order between primes is preserved as renemen ts of single-viewpoint models. As a case study, we discuss a multiple-valued notion of state-machines with rst-order logic plus transitive closure.

V4 lesions in macaques affect both single- and multiple-viewpoint shape discriminations

The role of cortical area V4 in complex shape discriminations was studied by testing the effects of V4 lesions in macaques on the ability to visually discriminate between images of three-dimensional (3D) objects from different viewpoints. Stimuli were presented in pairs in the lower left or lower right visual field quadrants about 4 deg from the fovea, and the monkeys judged on each trial whether the two views were of the same or of different objects. Object similarity was varied to determine a threshold shape difference. V4 lesions caused profound, retinotopic, and apparently permanent disruptions of discrimination, regardless of whether the images represented single or multiple viewpoints. In V4 lesioned portions of the visual field, monkeys could discriminate objects only when they differed much more grossly in shape than was true in control locations. These effects of the lesion were virtually identical for discriminations that had been learned before lesions were placed and for those learned afterwards. As in previous studies, V4 lesions elevated contrast thresholds by approximately a factor of two, but control observations showed that this was not the basis of the disruption of shape discrimination. Manipulation of cues to shape showed that in control locations, monkeys maintained excellent shape discrimination despite a variety of stimulus alterations, whereas in V4 lesioned areas their performance was easily disrupted. This finding suggests that V4 may support visual shape discriminations by facilitating the use of multiple visual cues. However, the fact that single-viewpoint and multiple-viewpoint discriminations were similarly affected indicates that the disruption was not specific to 3D shape discrimination, but may apply to a variety of subtle discriminations.