Parotid gland irradiation is a major risk factor for xerostomia in patients undergoing RT for head and neck cancers. We hypothesize that texture analysis allows for quantization of changes in parotid gland structure. 28 predominantly male patients diagnosed with locally advanced OPC who received concurrent chemoradiation constituted our cohort. The corresponding AUCs and CIs for predictive models suggest that the functional approach yields a superior ROC compared to using either the baseline radiomics feature or using the delta ratio between the mid and initial time points. Textural kinetic trajectories from consequential intra-treatment CT scans can predict for subsequent early and late radiation-induced toxicities. 
Support: 
National Institutes of Health: 
NCI-R01-CA214825, &ldquo;SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment&rdquo; 
NCI-R01CA225190, &ldquo;QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data&rdquo;

Actual Martingale residual by predicted value from random forest plot of time-to-local failure for the three sets based on the radiomic signature.

Normal Tissue Radiomic Feature Kinetics in Oropharygeal Cancers Treated with Image-Guided Radiation Therapy: Assessment of Parotid Glands Textural Changes on Daily Non-contrast CT

Recent data demonstrate that human papillomavirus (HPV)-associated oropharyngeal cancers (OPC) have discrete biology and clinical behavior as compared to HPV-negative OPC. We hypothesize that some phenotypic image features can be correlated to tumor biology in a non-invasive fashion. Some imaging features, specifically shape and intensity features can make a distinction between HPV-associated and HPV-negative OPC. Quantitative information from pretreatment imaging of intact tumor can give a better idea on tumor biology. Further studies are needed to validate the correlation between these radiomic biomarkers and other intrinsic tumor characteristics. 
Support: 
National Institutes of Health: 
NCI-R01-CA214825, &ldquo;SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment&rdquo; 
NCI-R01CA225190, &ldquo;QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data&rdquo;

Flowchart of patient selection for inclusion.

Predicting the HPV P16 Status of Oropharyngeal Cancer Patients Using Radiomics and an Ensemble of Random Forests

Traditional models of slideware assume one presenter controls attention through slide navigation and pointing while a passive audience views the action. This paradigm limits group interactions, curtailing opportunities for attendees to use slides to participate in a collaborative discourse. However, as slideware permeates contexts beyond simple one-to-many presentations, there are growing efforts to shift the dynamics of collocated interactions. Technologies exist to shift the one-to-many information control paradigm to variations that extend functions to multiple attendees. But there is limited detailed research on how to design such multi-person attentional control and facilitate collocated interactions without disrupting existing work practices. We report on a detailed naturalistic case of using presentation in a design meeting, where participants used Office Social, an experimental slideware technology that enabled open access to shared interaction with slides across multiple devices. We explore how the design of Office Social supported informal collaboration. Our video-based analysis reveals how the orderly structures ofconversational turn-taking and bodily conduct were used in conjunction with theaffordances of the socio-technical ecosystem to organize collective activity. We suggest that supporting collocated interactions should take account of the existing conversational methods for achieving orderly collaboration rather than superimposing prescriptive technological methods of order. 
Keywords: Presentation interactivity; Interaction design; Audience participation; Collocated interaction; Collaboration; Open systems; Design meeting 
<a href="https://doi.org/10.1080/07370024.2017.1388170">doi.org/10.1080/07370024.2017.1388170</a>

Office Social is designed as a PowerPoint add-in (A) and a companion smartphone app (B). When a slideshow is launched, the slide on the laptop screen is mirrored on the presenter’s smartphone and the public display.

Beyond presentation: Shared slideware control as a resource for collocated collaboration

We introduce a web-based visual comparison approach for the systematic exploration of dynamic activation networks across biological datasets. Understanding the dynamics of such networks in he context of demographic factors like age is a fundamental problem in computational systems biology and neuroscience. We design visual encodings for the dynamic and community characteristics of these temporal networks. Our multi-scale approach blends nested mosaic matrices that capture temporal characteristics of the data, spatial views of the network data, Kiviat diagrams and mirror glyphs that detail the temporal behavior and community assignment of specific nodes. A top design specifically targeted at pairwise visual comparison further supports the comparative analysis of multiple dataset activations. We demonstrate the effectiveness of this approach through a case study on mouse brain network data. Domain expert feedback indicates this approach can help identify trends and anomalies in the data. &copy; 2017 Society for Imaging Science and Technology. 
[DOI: 10.2352/J.ImagingSci.Technol.2017.61.6.000000]

Integration of temporal community characteristics into the brain slice of an aged mouse, across 64 time steps.

RemBrain: Exploring Dynamic Biospatial Networks with MosaicMatrices and Mirror Glyphs

Background: Visualizing the complex probability landscape of stochastic gene regulatory networks can further
biologists&rsquo; understanding of phenotypic behavior associated with specific genes. 
Results: We present PRODIGEN (PRObability DIstribution of GEne Networks), a web-based visual analysis tool for the systematic exploration of probability distributions over simulation time and state space in such networks. PRODIGEN was designed in collaboration with bioinformaticians who research stochastic gene networks. The analysis tool combines in a novel way existing, expanded, and new visual encodings to capture the time-varying characteristics of probability distributions: spaghetti plots over one dimensional projection, heatmaps of distributions over 2D projections, enhanced with overlaid time curves to display temporal changes, and novel individual glyphs of state information corresponding to particular peaks. 
Conclusions: We demonstrate the effectiveness of the tool through two case studies on the computed probabilisti landscape of a gene regulatory network and of a toggle-switch network. Domain expert feedback indicates that our visual approach can help biologists: 1) visualize probabilities of stable states, 2) explore the temporal probability distributions, and 3) discover small peaks in the probability landscape that have potential relation to specific diseases. 
Keywords: Systems biology, Feature detection and tracking, Network analysis, Modelling/Simulation

The PRODIGEN interface consists of several visual components.

PRODIGEN: visualizing the probability landscape of stochastic gene regulatory networks in state and time space

A system and methods for visualizing information on a generally large scale. Certain embodiments include display elements that include a minimized border margin, 2D/3D dual functionality, and ultra-high resolution. The display elements may be positioned relative to an infrastructure element configured to permit generating a wide variety of display geometries. 
This patent resulted in realizing the CAVE2 Hybrid Reality Environment.

Illustration of user viewing an embodiment of a patterned-retarded barrier layer shifted adjacent to a layer of pixels from a side view.

System and Methods for Visualizing Information (US 9,800,862 B2 - CAVE2)

One of the significant challenges of care transitions in Intensive Care Units (ICUs) is the lack of effective support tools for outgoing
clinicians to find, filter, organize, and annotate information that can be effectively handed off to the incoming team. We present a large display interactive multivariate visual approach, aimed towards supporting clinicians during the transition of care. We first provide a characterization of the problem domain in terms of data and tasks, based on an observation session at the University of Illinois Hospital, and on interviews with several biomedical researchers and ICU clinicians. Informed by this experience, we design a scalable, interactive visual approach that supports both overview and detail views of ICU patient data, as well as anomaly detection, comparison, and annotation of the data. We demonstrate a large-display implementation of the visualization on an existing anonymized ICU dataset. Feedback from domain experts indicates this approach successfully meets the requirements of effective care transitions. 
Index Terms: ICU care transition, large display visualization, shared cognition, collaborative decision making

(Left) Two colocated testers establish a common information space using Echo on a tiled large display wall that runs SAGE2 middleware. (Right) A group of testers run Echo synchronously on a large display screen, on a laptop and on a tablet.

Echo: A Large Display Interactive Visualization of ICU Data for Effective Care Handoffs

Mini Challenge 2 of the VAST Challenge 2017 focused on a small industrial area south of the fictional Mistford preserve, specifically around four manufacturing factories. Our main goal was to develop a visual analytics tool to explore the spatio-temporal chemical readings and wind data. Specifically, we wanted to determine which factories were responsible for emitting which chemicals and to determine the performance of the nine sensors in the area. In order to help achieve this goal, we developed a web-based application that utilizes interactive visualizations and path line analysis for revealing sensor errors and chemical reading spikes, along with pinpointing the possible sources of chemical reading spikes.

EVL Project Team (from left to right): Andrew Burks, Joe Borowicz, Elisabeta Marai (faculty advisor), Joshua Castor and Manu Thomas

MC2 - Mining Factory Pollution Data through a Spatial-Nonspatial Flow Approach

Our web-based image analysis tool for the VAST 2017 Mini-Challenge 3 combines small multiple views of satellite images, linked semantic zooming and image intensity histograms, along with filter controls. The resulting tool allow users to interactively analyze spatio-temporal changes in the preserve area.

EVL Project Team (from left to right): Bart Kupiec, Andrew Burks, Elisabeta Marai (faculty advisor) and Vijayraj Mahida

MC3 - A Web-Based Interactive Image Explorer for Temporal Analysis of Satellite Images

This paper describes a web-based traffic data analysis tool developed for the VAST 2017 Mini Challenge 1. The tool consists of two linked heat maps which allow for the inspection of daily activity for vehicles, as well as a histogram which allows for the analysis of total time spent by vehicles in the park. Combined, these views allow for the analysis of both spatial and temporal patterns in the park preserve.

Traffic data analysis tool, with a large overview interactive heat map and a detailed heat map to the side; the maps capture spatio-temporal patterns A stacked histogram allows for temporal analysis; bins can be zoomed in for closer inspection.

MC1: A Bespoke Analysis Tool for Spatio-temporal Park Traffic Data

&ldquo;Hearts and Minds: The Interrogations Project&rdquo; is an interactive virtual reality narrative performance made for the EVL&rsquo;s CAVE2&trade; large-scale 320-degree panoramic virtual reality environment that visualizes stories of violence and the post-traumatic stress experienced by ordinary American soldiers who became torturers in the course of serving their country. During the American-led counterinsurgency and counter-terrorism campaigns in Iraq in the years after 11 September 2001, the torture and abuse of detainees was a commonplace tactic.

Hearts and Minds as presented in the CAVE2 in the Electronic Visualization Lab at the University of Illinois Chicago.

Hearts and Minds: The Interrogations Project

The computational study of the dynamic and stochastic natures of gene regulatory networks is a challenging topic in systems biology. Visualizing ensemble time-evolving probability landscapes of stochastic gene networks can further biologists&rsquo; understanding of phenotypic behavior associated with specific genes. We present a web-based visual analysis tool for the exploration of peak distributions over state space and simulation time in such stochastic networks, and the comparison of peak distributions between multiple simulations. Our approach combines multiple linked views to capture ensemble time-evolving probability landscapes. A peak trajectory cube provides users an overview of peak spatiotemporal distributions between six simulations. A peak projection map shows the exact peak locations of multiple simulations at the user selected time. At a more detailed level, users can inspect a particular state in the peak projection map to view for each simulation both the probability values over time, and the local probability landscape shapes. This information is displayed in a small multiple using two glyphs: profile glyphs and arrow glyphs. The arrow glyph indicates that a state is a peak when all the glyph eight arrows point towards the glyph center. In the figure, a disagreement between the arrow glyphs and the peak projection map demonstrates that probability distributions over genes in this system are not independent of each other. Our visual analysis tool allows bioinformatics researchers to explore and compare the time evolving changes of probability landscapes from multiple simulations efficiently, without running many small scripts and computing all characteristics separately. 
Keywords: Systems biology, Feature detection and tracking, Modelling/Simulation, Ensemble visualization, Spatiotemporal visualization

Visualizing ensemble time-evolving probability landscapes of stochastic networks

To determine acceptability, usefulness, and ease of use for four nursing clinical decision support interface prototypes. In a simulated hospital environment, 60 registered nurses (48 female; mean age=33.7 10.8; mean years of experience=8.1 9.7) participated in a randomized study with four study groups. Measures included acceptability, usefulness, and ease of use scales. Mean scores were high for acceptability, usefulness, and the ease of use for all four groups. Inexperienced participants (<1 year) reported higher perceived ease of use (p=.05) and perceived usefulness (p=.01) than those with ≥1 year experience. Participants completed the protocol and reported that all four interfaces, including the control (HANDS), were acceptable, easy to use, and useful. Further study is warranted before clinical implementation within the electronic health record. 
Keywords: clinical decision support; practice-based evidence; electronic health record; end-of-life care; interface usability; simulation

Four screen shots (A–D) show the HANDS control group interface that was used to orient the participant to the major features of the care planning system.

Acceptability of clinical decision support prototypes for a nursing electronic health record to facilitate palliative care outcomes.

Background - Clinical decision support (CDS) tools - with easily understood and actionable information, at the point of care - are needed to help registered nurses (RNs) make evidence-based decisions. Not clear are the optimal formats of CDS tools. Thorough, preclinical testing is desirable to avoid costly errors associated with premature implementation in electronic health records. 
Objective - To determine the feasibility of the protocol for designed to compare multiple CDS formats, and evaluate effects of numeracy and graph literacy on RN adoption of best practices and care-planning time in a simulated environment. 
Methods - In this pilot study, 60 RNs were randomly assigned to one of four CDS conditions (control; text; text+graph; text+table) and asked to adjust the plan of care for two patient scenarios over three shifts. A total of 14 best practices were identified for the two patients and sent as suggestions with evidence to the three CDS groups. Best practice adoption rates, care-planning time, and their relationship to the RN&rsquo;'s numeracy and graph literacy scores were assessed. 
Results - CDS groups had a higher adoption rate of best practices (p < .001) across all shifts and decreased care-planning time in shifts two (p = .01) and three (p = .02) compared to the control group. Higher numeracy and graph literacy were associated with shorter care-planning times under text+table (p = .05) and text+graph conditions (p = .01). No significant differences were found between the three CDS groups on adoption rate and care-planning time. 
Discussion - This pilot study demonstrates the feasibility of our protocol. Findings show preliminary evidence that CDS improves the efficiency and effectiveness of care-planning decisions, and that the optimal format may depend on individual RN characteristics. We recommend a study with sufficient power to compare different CDS formats, and assess the impact of potential covariates on adoption rates and care-planning time. 
Keywords: care plans; decision support; standardized nursing terminologies; usability

Nursing care plan examples within the three different S-HANDS CDS prototypes.

Toward meaningful care plan clinical decision support: Feasibility and effects of a simulated pilot study

We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological models that are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-component biological molecules. Our technique visualizes the dynamics of these rules as they evolve over time. Using the data produced by KaSim, an open source stochastic simulator of rule-based models written in the Kappa language, DINs provide a node-link diagram that represents the influence that each rule has on the other rules. That is, rather than representing individual biological components or types, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactive DIN-Viz software tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, and to identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation of a circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle. 
Index Terms - Dynamic networks, biological data visualization, rule-based modeling, protein-protein interaction networks.

A screenshot of the DIN-Viz application for analyzing the dynamics of a rule-based model of a protein-protein interaction network (i.e., a Dynamic Influence Network).

Dynamic Influence Networks for Rule-based Models

We introduce a deep learning approach for the identification of shock locations in large-scale tensor field datasets. Such datasets are typically generated by turbulent combustion simulations. In this proof of concept approach, we use deep learning to learn mappings from strain tensors to Schlieren images which serve as labels. The use of neural networks allows for the Schlieren values to be approximated more efficiently than calculating the values from the density gradient. In addition, we show that this approach can be used to predict the Schlieren values for both two-dimensional and three-dimensional tensor fields, potentially allowing for anomaly detection in tensor flows. Results on two shock example datasets show that this approach can assist in the extraction of features from reacting flow tensor fields. 
Support: 
NSF CAREER IIS-1541277

Networks for 3D Sod dataset (left) and the 2D Blast dataset (right).

A Deep Learning Approach to Identifying Shock Locations in Turbulent Combustion Tensor Fields

Cancers arising from the oropharynx have become increasingly more studied in the past few years, as they are now epidemic domestically. These tumors are treated with definitive (chemo)radiotherapy, and have local recurrence as a primary mode of clinical failure. Recent data suggest that &lsquo;radiomics&rsquo;, or extraction of image texture analysis to generate mineable quantitative data from medical images, can reflect phenotypes for various cancers. Several groups have shown that developed radiomic signatures, in head and neck cancers, can be correlated with survival outcomes. This data descriptor defines a repository for head and neck radiomic challenges, executed via a Kaggle in Class platform, in partnership with the MICCAI society 2016 annual meeting. These public challenges were designed to leverage radiomics and/or machine learning workflows to discriminate HPV phenotype in one challenge (HPV status challenge) and to identify patients who will develop a local recurrence in the primary tumor volume in the second one (Local recurrence prediction challenge) in a segmented, clinically curated anonymized oropharyngeal cancer (OPC) data set. 
Support: 
National Science Foundation: 
NSF-DMS-1557559, &ldquo;QuBBD Collaborative Research SMART Spatial Nonspatial Multidimensional Adaptive Radiotherapy Treatment&rdquo;

Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges

Scheduling conferences is a common task in both research and industry, which requires relatively small groups to collaborate and negotiate in order to solve an often-large logistical problem with many nuances. For large conferences, the process can take days and it is traditionally a manual procedure performed using physical tools such as whiteboards and sticky notes. We present the design and implementation of StickySchedule, a multi-user application for use on interactive large-scale shared displays to better enable groups to organize large conference-scheduling data. To evaluate our tool, we present observations from novice users, and authentic use cases with expert feedback from organizers who are heavily involved in large conference scheduling. The main contributions of our work are documenting the collaborative and competitive aspects of conference scheduling, creating a tool that incorporates successful features and addresses identified issues with prior works, and verifying the usefulness of our tool by observing and discussing a variety of use cases, in both collocated and remote-distributed settings. 
Author Keywords: Large-scale displays; collaboration; multi-user interaction; conference scheduling. 
ACM Classification Keywords: H.5.3. Information interfaces and presentation (HCI): Group and Organization Interfaces; Computer-supported cooperative work.

Simulated organization committee scheduling a conference using StickySchedule. Interaction is supported from both personal devices (e.g. a mouse from one’s laptop) and touch directly on the large-scale shared display

StickySchedule: An Interactive Multi-user Application for Conference Scheduling on Large-scale Shared Displays

Although grief is often associated with death, receiving a diagnosis of a terminal disease for oneself or a loved one also unleashes an onslaught of emotions for the individual and caregivers. Sorrow, grief, fear, anger, and anxiety may fill the time between diagnosis and loss. Rando defines anticipatory grief as &ldquo;the phenomenon encompassing the processes of mourning, coping, interaction, planning, and psychosocial reorganization that are stimulated and begun in part in response to the awareness of the impending loss of a loved one (death) and in the recognition of associated losses in the past, present, and future.&rdquo; Though its topical presence in the literature has increased over the past half-century, the continued discrepant findings highlight the importance of further research into the outcomes of those experiencing anticipatory grief. The purpose of our study was to examine the outcomes of hospitalized end-of-life patients with anticipatory grief. 
Globally, 8 million people die of cancer every year. Coupled with the 7.7 million new annual diagnoses of dementia worldwide as well as the millions who lose family members to other terminal diseases, the apparent impact of anticipatory grief on the lives of the patient and caregivers is colossal. Research has been contradictory in its findings regarding of the long-term effects of anticipatory grief. Some argue that anticipatory grief may lead to a prolonged grief reaction, whereas some argue that it aids recovery from the grief process after death, and others that it is not related to post-death bereavement.5 One cited contributory reason to the contradictory findings is the lack of an operational definition. An underutilized solution is the NANDA International's (NANDA-I, formerly North American Nursing Diagnosis Association International) operational definition of Anticipatory Grieving, which defines the concept as an action state of engagement in grief work before the death or loss occurs (Table 1), instead of as a non-gerundial noun, anticipatory grief, which is used in much of the literature. In this article, we use Anticipatory Grieving (AG) to represent the NANDA-I diagnosis, and anticipatory grief to represent the concept, which is similar to but not necessarily conforming to the defining characteristics of AG (Table 1). Researchers have demonstrated that nurses, well-trained in the use of clinical decision support, accurately use standardized nursing terms and measures for nursing diagnoses, interventions, and outcomes in the electronic health record (EHR), which makes it possible to evaluate the impact of care provided to patients with AG. 
Keywords: electronic health record; nursing; anticipatory grieving; palliative care; grief

Percentage of NOC met for Anticipatory Grieving related NOCs © 2014 HANDS Team

Outcomes for End-of-Life Patients with Anticipatory Grieving: Insights from Practice with Standardized Nursing Terminologies within an Interoperable Internet-based Electronic Health Record

Efficient RDF, graph-based queries are becoming more pertinent based on the increased interest in data analytics and its intersection with large, unstructured but connected data. Many commercial systems have adopted distributed RDF graph systems in order to handle increasing dataset sizes and complex queries. This paper introduces a distribute graph approach to pre-processing linked data. Instead of traversing the memory graph, our system indexes pre-processed join elements that are organized in a graph structure. We analyze the Dbpedia data-set (derived from the Wikipedia cor-pus) and compare our access method to the graph traversal access approach which we also devise. Results show from our experiments that the distributed, pre-processed graph approach to accessing linked data is faster than the traversal approach over a specific range of linked queries. 
CCS CONCEPTS: Computing methodologies, Distributed algorithms 
KEYWORDS: RDF, High Performance Computing; Distributed Algorithms

(a) Graph-store algorithm Graph construction and neighbor times over 1M, .5M triple dataset.

A Distributed Graph Approach for Pre-processing Linked RDF Data Using Supercomputers

Clear Speech consists of crisp, loud, and slowly spoken sentence(s) for increased intelligibility where the utterances are distinguishable and audible without much strain on the human ear. For individuals with auditory comprehension disabilities, clear speech would be easier to comprehend than casual or normal speech. In this effort, we try to automate the conversion of casual speech to clear speech using machine translation. 
In the current work we develop a Recurrent Neural Network (RNN) that uses parallel corpora with one set of corpus as input and predicts output by the parameters learned from another corpus for this translation. Basing our study from literature, we make use of Deep Bidirectional Long Short term memory (LSTM) based RNN model because of its proven effectiveness in speech translation domain. To generalize, we convert voice A to voice B by passing voice A through multiple forward-backward LSTM exploiting its capability to learn voice B from both past and future time steps in voice A trained to regress voice B via backpropagation through time. 
We performed our preliminary experiments on a male to female speech conversion task, following which we were able to extend the developed pipeline for casual to clear speech conversion.

Male(top), Female(middle), Output(bottom) of the synthesized speech of the ARCTI Ccorpus

Deep Bidirectional LSTM based RNN for Casual Speech to Clear Speech conversion

In the domain of large-scale visualization instruments, Hybrid Reality Environments (HREs) are a recent innovation that combines the best-in-class capabilities of immersive environments, with the best-in-class capabilities of ultra-high-resolution display walls. HREs create a seamless 2D/3D environment that supports both information-rich analysis as well as virtual-reality simulation exploration at a resolution matching human visual acuity. Collaborative research groups in HREs tend to work on a variety of tasks during a research session (sometimes in parallel), and these tasks require 2D data views, 3D views, linking between them, and the ability to bring in (or hide) data quickly as needed. Addressing these needs requires a matching software infrastructure that fully leverages the technological affordances offered by these new instruments. In this dissertation, I detail the requirements of such infrastructure and outline the model of an operating system for Hybrid Reality Environments. I present an implementation of core components of this model, called Omegalib. I show how this implementation has been successfully used to create HRE visualizations for research, education and outreach. One key feature of Omegalib is its ability to support multiple applications running simultaneously on an HRE. This is a significant improvement compared to classic immersive systems, which normally support a single application at a time. HREs (and large displays in general) are ideal systems to support co-located collaboration, and the ability to run and control multi-application workspaces is key to effective work in this setting. However, enabling multiple immersive applications on a large display presents several challenges. I discuss our solution to these challenges and present
an extension of Omegalib that supports fully dynamic, multi-view, multi-user immersion. I evaluate this new system, which I call Multiview Immersion (MVI) in a formal user study: two-user groups are asked to compare 3D sonar scans using MVI, single-view immersion and multiple views without immersion (simulating a standard display wall). My objective is understanding the effect of MVI on analysis effectiveness, view usage and collaboration patterns compared to alternatives. MVI appears to reduce analysis error rates for this sample task and makes it more likely for both users to remain engaged with the analysis task. 
Intellectual Merit 
The work outlined in this dissertation extends the state of the art in large display software infrastructures, solves several limitations in current systems (lack of immersion support in multiwindow environments, single-application-only immersive environments) and provides an advanced platform for application development on Hybrid Reality Environments. Moreover, this work outlines a technique for describing co-located collaboration patterns with large displays, based on user gaze direction and communication activity. 
Broader Impact 
An operating system for Hybrid Reality Environments will accelerate research using thes novel instruments in two ways. It will simplify and speed up the creation of domain-specific applications that make full use of HREs. And it will support co-located user groups tackling modern research, analysis or planning tasks requiring multiple heterogeneous data displays involving immersive and non-immersive views. The results of the user study presented in this work identify and describe features of co-located collaborative work within Hybrid Reality Environments that can be extended to large displays in general, and can guide the design of applications for these systems.

The Cyber-Commons display wall at EVL, displaying multiple views from a water chemistry dataset.

Multiview Immersion in Hybrid Reality Environments

We propose a &ldquo;Scalable Amplified Group Environment&rdquo; to enable large-scale human and data interaction and collaboration on High-Resolution Display Walls. To enable effective critical thinking and brainstorming sessions within the room and between distributed sites, we need new technologies that can be described as &ldquo;Collaboration as a Service&rdquo; (CaaS). 
CaaS features include: 
- On demand collaboration with dynamic resource allocation to support collaborative visualization sessions first within the room and then between several sites, 
- Enabling online large scale data analysis and visual analytics, 
- Support for many visualization types: local rendering (web-based) and hardware remote rendering transmitted as high-quality video streams (GPU-rendering and video compression in the cloud).

Collaboration as a Service?

Conditional survival (CS), the survival beyond a pre-defined time interval, can identify periods of higher mortality risk for patients with locally advanced laryngeal cancer who face treatment-related toxicity and comorbidities related to alcohol and smoking in the survivorship setting. Using Weibull regression modeling, we analyzed retrospectively abstracted data from 638 records of patients who received radiation to identify prognostic factors for overall survival (OS) and recurrence free survival (RFS) for the first 3 years of survival and for OS conditional upon 3 years of survival. The CS was iteratively calculated, stratifying on variables that were statistically significant on multivariate regression. Predictive nomograms were generated. The median total follow up time was 175 months. The 3- and 6- year actuarial overall survival (OS) was 68% (95% confidence interval [CI] 65-72%) and 49% (CI 45-53%). The 3-year conditional overall survival (COS) at 3 years was 72% (CI 65-74%). Black patients had worse COS over time. Nodal disease was significantly associated with recurrence, but after 3 years, the 3-year conditional RFS converged for all nodal groups. In conclusion, the CS analysis in this patient cohort identified subgroups and time intervals that may represent opportunities for intervention. 
Support: 
National Institutes of Health: 
NCI-R01-CA214825, &ldquo;SMART-ACT: Spatial Methodologic Approaches for Risk Assessment and Therapeutic Adaptation in Cancer Treatment&rdquo;

Conditional Survival Analysis of Patients With Locally Advanced Laryngeal Cancer: Construction of a Dynamic Risk Model and Clinical Nomogram

Mixed presence collaboration involves remote collaboration between multiple collocated groups. This paper presents the design and results of a user study that focused on mixed presence collaboration using large-scale tiled display walls. The research was conducted in order to compare data synchronization schemes for multi-user visualization applications. Our study compared three techniques for sharing data between display spaces with varying constraints and affordances. The results provide empirical evidence that using data sharing techniques with continuous synchronization between the sites lead to improved collaboration for a search and analysis task between remotely located groups. We have also identified aspects of synchronized sessions that result in increased remote collaborator awareness and parallel task coordination. It is believed that this research will lead to better utilization of large-scale tiled display walls for distributed group work. 
Author Keywords: Mixed presence collaboration; data-conferencing; multi-user interaction; large-scale displays. 
ACM Classification Keywords: H.5.3. [Information Interfaces and Presentation]: Group and Organization Interfaces: Computer-supported cooperative work, synchronous interaction, evaluation/methodology.

Teams of two engaged in mixed presence collaboration, working on finding a location to open a new coffee shop.

Mixed Presence Collaboration using Scalable Visualizations in Heterogeneous Display Spaces

SAGE2 is a user-centered platform for small groups or large distributed teams to access large-scale datasets from various sources and juxtapose, share and investigate content on tiled display walls. SAGE2 builds on SAGE (Scalable Adaptive Graphics Environment), developed with NSF prior funding. With 69 sites worldwide since 2015, SAGE2 is having a transformative effect on data exploration, visualization and collaboration, making cyberinfrastructure more accessible to end users both in the laboratory and in the classroom. 
<a href="http://dx.doi.org/10.6084/m9.figshare.4644796">2017 Poster: http://dx.doi.org/10.6084/m9.figshare.4644796</a>

SAGE2 Scalable Amplified Graphics Environment (poster 2017)

This article presents the main features and components of iCardia - an innovative mHealth platform designed to support remote monitoring and health coaching of cardiac rehabilitation (CR) patients, through Fitbit wearable sensor devices, smartphones, and personalized SMS text messages. The design and development of iCardia were based on an iterative, user-centered design process and an open-service architecture to ensure rapid scalability and adherence to evidence-based guidelines for easier transition into clinical practice. iCardia has the potential to enable a paradigm shift towards a collaborative CR environment that utilizes mHealth technologies to engage patients to more effectively self-manage their cardiovascular disease.

iCardia platform components and architecture

Development of an innovative mHealth platform for remote physical activity monitoring and health coaching of cardiac rehabilitation patients

The uncanny valley hypothesis predicts that an entity appearing almost human risks eliciting cold, eerie feelings in viewers. Categorization-based stranger avoidance theory identifies the cause of this feeling as categorizing the entity into a novel category. This explanation is doubtful because stranger is not a novel category in adults; infants do not avoid strangers while the category stranger remains novel; infants old enough to fear strangers prefer photographs of strangers to those more closely resembling a familiar person; and the uncanny valley&rsquo;s characteristic eeriness is seldom felt when meeting strangers. We repeated our original experiment with a more realistic 3D computer model and found no support for categorization-based stranger avoidance theory. By contrast, realism inconsistency theory explains cold, eerie feelings elicited by transitions between instances of two different, mutually exclusive categories, given that at least one category is anthropomorphic: Cold, eerie feelings are caused by prediction error
from perceiving some features as features of the first category and other features as features of the second category. In principle, realism inconsistency theory can explain not only negative evaluations of transitions between real and computer modeled humans but also between different vertebrate species. 
Keywords: Anthropomorphism; Computer animation; Face perception; Novelty; Stranger avoidance 
<a href="http://dx.doi.org/10.1016/j.cognition.2016.09.001">dx.doi.org/10.1016/j.cognition.2016.09.001</a>

Stimuli ratings of subjective realism, eeriness, warmth, and familiarity are plotted against their fraction of real as are percentage categorized as real and response time.