The benefits of using a walking interface to navigate virtual environments

Navigation is the most common interactive task performed in three-dimensional virtual environments (VEs), but it is also a task that users often find difficult. We investigated how body-based information about the translational and rotational components of movement helped participants to perform a navigational search task (finding targets hidden inside boxes in a room-sized space). When participants physically walked around the VE while viewing it on a head-mounted display (HMD), they then performed 90% of trials perfectly, comparable to participants who had performed an equivalent task in the real world during a previous study. By contrast, participants performed less than 50% of trials perfectly if they used a tethered HMD (move by physically turning but pressing a button to translate) or a desktop display (no body-based information). This is the most complex navigational task in which a real-world level of performance has been achieved in a VE. Behavioral data indicates that both translational and rotational body-based information are required to accurately update one's position during navigation, and participants who walked tended to avoid obstacles, even though collision detection was not implemented and feedback not provided. A walking interface would bring immediate benefits to a number of VE applications

Validation of automotive electromagnetic models

The problems of modelling the electromagnetic characteristics of vehicles and the experimental validation of such models are considered. The validity of the measurement methods that are applied in model validation exercises is of particular concern. A philosophy for approaching the validation of automotive electromagnetic models of realistic complexity is presented. Mathematical modelling of the key elements of the measurement processes is proposed as the only reliable mechanism for addressing these issues. Areas considered include: basic elements of numerical models; geometrical fidelity requirements for model elements; calibration and use of experimental transducers; the inclusion of cables in electromagnetic models; essential content for vehicle models. A number of practical measurement processes are also investigated using numerical methods, leading to recommendations for improved practices in: calibration of transducers for current measurement at high frequencies; measurement of radiated emissions from vehicles; identification of range requirements for simple methods of determining antenna gain and related characteristics in EMC test facilities. The impact of such measures on the success of model validation studies for automotive applications is demonstrated. It is concluded that experimental results are no less in need of validation than the numerical results that are, more conventionally, judged against them

Visual Analytics of Event Data using Multiple Mining Methods

Most researchers use a single method of mining to analyze event data. This paper uses case studies from two very differentdomains (electronic health records and cybersecurity) to investigate how researchers can gain breakthrough insights by com-bining multiple event mining methods in a visual analytics workflow. The aim of the health case study was to identify patternsof missing values, which was daunting because the 615 million missing values occurred in 43,219 combinations of fields. How-ever, a workflow that involved exclusive set intersections (ESI), frequent itemset mining (FIM) and then two more ESI stepsallowed us to identify that 82% of the missing values were from just 244 combinations. The cybersecurity case study’s aim wasto understand users’ behavior from logs that contained 300 types of action, gathered from 15,000 sessions and 1,400 users.Sequential frequent pattern mining (SFPM) and ESI highlighted some patterns in common, and others that were not. For thelatter, SFPM stood out for its ability to action sequences that were buried within otherwise different sessions, and ESI detectedsubtle signals that were missed by SFPM. In summary, this paper demonstrates the importance of using multiple perspectives,complementary set mining methods and a diverse workflow when using visual analytics to analyze complex event data

Symmetric and asymmetric action integration during cooperative object manipulation in virtual environments

Cooperation between multiple users in a virtual environment (VE) can take place at one of three levels. These are defined as where users can perceive each other (Level 1), individually change the scene (Level 2), or simultaneously act on and manipulate the same object (Level 3). Despite representing the highest level of cooperation, multi-user object manipulation has rarely been studied. This paper describes a behavioral experiment in which the piano movers' problem (maneuvering a large object through a restricted space) was used to investigate object manipulation by pairs of participants in a VE. Participants' interactions with the object were integrated together either symmetrically or asymmetrically. The former only allowed the common component of participants' actions to take place, but the latter used the mean. Symmetric action integration was superior for sections of the task when both participants had to perform similar actions, but if participants had to move in different ways (e.g., one maneuvering themselves through a narrow opening while the other traveled down a wide corridor) then asymmetric integration was superior. With both forms of integration, the extent to which participants coordinated their actions was poor and this led to a substantial cooperation overhead (the reduction in performance caused by having to cooperate with another person)

Using Miniature Visualizations of Descriptive Statistics to Investigate the Quality of Electronic Health Records

Descriptive statistics are typically presented as text, but that quickly becomes overwhelming when datasets contain many variables or analysts need to compare multiple datasets. Visualization offers a solution, but is rarely used apart from to show cardinalities (e.g., the % missing values) or distributions of a small set of variables. This paper describes dataset- and variable-centric designs for visualizing three categories of descriptive statistic (cardinalities, distributions and patterns), which scale to more than 100 variables, and use multiple channels to encode important semantic differences (e.g., zero vs. 1+ missing values). We evaluated our approach using large (multi-million record) primary and secondary care datasets. The miniature visualizations provided our users with a variety of important insights, including differences in character patterns that indicate data validation issues, missing values for a variable that should always be complete, and inconsistent encryption of patient identifiers. Finally, we highlight the need for research into methods of identifying anomalies in the distributions of dates in health data

A set-based visual analytics approach to analyze retail data

This paper explores how a set-based visual analytics approach could be useful for analyzing customers' shopping behavior, and makes three main contributions. First, it describes the scale and characteristics of a real-world retail dataset from a major supermarket. Second, it presents a scalable visual analytics workflow to quickly identify patterns in shopping behavior. To assess the workflow, we conducted a case study that used data from four convenience stores and provides several insights about customers' shopping behavior. Third, from our experience with analyzing real-world retail data and comments made by our industry partner, we outline four research challenges for visual analytics to tackle large set intersection problems

Cell-selective Knockout and 3D Confocal Image Analysis Reveals Separate Roles for Astrocyte- and Endothelial-derived CCL2 in Neuroinflammation

Background Expression of chemokine CCL2 in the normal central nervous system (CNS) is nearly undetectable, but is significantly upregulated and drives neuroinflammation during experimental autoimmune encephalomyelitis (EAE), an animal model of multiple sclerosis which is considered a contributing factor in the human disease. As astrocytes and brain microvascular endothelial cells (BMEC) forming the blood–brain barrier (BBB) are sources of CCL2 in EAE and other neuroinflammatory conditions, it is unclear if one or both CCL2 pools are critical to disease and by what mechanism(s). Methods Mice with selective CCL2 gene knockout (KO) in astrocytes (Astro KO) or endothelial cells (Endo KO) were used to evaluate the respective contributions of these sources to neuroinflammation, i.e., clinical disease progression, BBB damage, and parenchymal leukocyte invasion in a myelin oligodendrocyte glycoprotein peptide (MOG35-55)-induced EAE model. High-resolution 3-dimensional (3D) immunofluorescence confocal microscopy and colloidal gold immuno-electron microscopy were employed to confirm sites of CCL2 expression, and 3D immunofluorescence confocal microscopy utilized to assess inflammatory responses along the CNS microvasculature. Results Cell-selective loss of CCL2 immunoreactivity was demonstrated in the respective KO mice. Compared to wild-type (WT) mice, Astro KO mice showed reduced EAE severity but similar onset, while Endo KO mice displayed near normal severity but significantly delayed onset. Neither of the KO mice showed deficits in T cell proliferation, or IL-17 and IFN-γ production, following MOG35-55 exposure in vitro, or altered MOG-major histocompatibility complex class II tetramer binding. 3D confocal imaging further revealed distinct actions of the two CCL2 pools in the CNS. Astro KOs lacked the CNS leukocyte penetration and disrupted immunostaining of CLN-5 at the BBB seen during early EAE in WT mice, while Endo KOs uniquely displayed leukocytes stalled in the microvascular lumen. Conclusions These results point to astrocyte and endothelial pools of CCL2 each regulating different stages of neuroinflammation in EAE, and carry implications for drug delivery in neuroinflammatory disease

Improvements proposed to noisy-OR derivatives for multi-causal analysis: A case study of simultaneous electromagnetic disturbances

In multi-causal analysis, the independence of causal influence (ICI) assumed by the noisy-OR (NOR) model can be used to predict the probability of the effect when several causes are present simultaneously, and to identify (when it fails) inter-causal dependence (ICD) between them. The latter is possible only if the probability of observing the multi-causal effect is available for comparison with a corresponding NOR estimate. Using electromagnetic interference in an integrated circuit as a case study, the data corresponding to the probabilities of observing failures (effect) due to the injection of individual (single cause) and simultaneous electromagnetic disturbances having different frequencies (multiple causes) were collected. This data is initially used to evaluate the NOR model and its existing derivatives, which have been proposed to reduce the error in predictions for higher-order multi-causal interactions that make use of the available information on lower-order interactions. Then, to address the identified limitations of the NOR and its existing derivatives, a new deterministic model called Super-NOR is proposed, which is based on correction factors estimated from the available ICD information