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Purpose To determine whether patient similarity in terms of head and neck cancer spread through lymph nodes correlates significantly with radiation-associated toxicity. Materials and methods 582 head and neck cancer patients received radiotherapy for oropharyngeal cancer (OPC) and had non-metastatic affected lymph nodes in the head and neck. Affected lymph nodes were segmented from pretreatment contrast-enhanced tomography scans and categorized according to consensus guidelines. Similar patients were clustered into 4 groups according to a graph-based representation of disease spread through affected lymph nodes. Correlation between dysphagia-associated symptoms and patient groups was calculated. Results Out of 582 patients, 26% (152) experienced toxicity during a follow up evaluation 6 months after completion of radiotherapy treatment. Patient groups identified by our approach were significantly correlated with dysphagia, feeding tube, and aspiration toxicity (p< .0005). Discussion Our results suggest that structural geometry-aware characterization of affected lymph nodes can be used to better predict radiation-associated dysphagia at time of diagnosis, and better inform treatment guidelines. Conclusion Our work successfully stratified a patient cohort into similar groups using a structural geometry, graph-encoding of affected lymph nodes in oropharyngeal cancer patients, that were predictive of late radiation-associated dysphagia and toxicity.
Andrew Wentzel; Timothy Luciani; Lisanne V. van Dijk; Nicolette Taku; Baher Elgohari; Abdallah S.R. Mohamed; Guadalupe Canahuate; Clifton Fuller; David M. Vock; G. Elisabeta Marai. Precision association of lymphatic disease spread with radiation-associated toxicity in oropharyngeal squamous carcinomas. Radiotherapy and Oncology 2021, 161, 152 -158.
AMA StyleAndrew Wentzel, Timothy Luciani, Lisanne V. van Dijk, Nicolette Taku, Baher Elgohari, Abdallah S.R. Mohamed, Guadalupe Canahuate, Clifton Fuller, David M. Vock, G. Elisabeta Marai. Precision association of lymphatic disease spread with radiation-associated toxicity in oropharyngeal squamous carcinomas. Radiotherapy and Oncology. 2021; 161 ():152-158.
Chicago/Turabian StyleAndrew Wentzel; Timothy Luciani; Lisanne V. van Dijk; Nicolette Taku; Baher Elgohari; Abdallah S.R. Mohamed; Guadalupe Canahuate; Clifton Fuller; David M. Vock; G. Elisabeta Marai. 2021. "Precision association of lymphatic disease spread with radiation-associated toxicity in oropharyngeal squamous carcinomas." Radiotherapy and Oncology 161, no. : 152-158.
Purpose Using a cohort of 582 head and neck cancer patients with nodal disease, we employed clustering over a novel graph-based geometrical representation of lymph node spread in order to identify groups of similar patients. We show that these groups are significantly correlated with radiation-associated dysphagia (RAD), and predictive of late aspiration and feeding tube toxicity. Materials and methods All patients received radiotherapy for oropharyngeal cancer (OPC) and had non-metastatic affected lymph nodes in the head and neck. Affected lymph nodes were segmented from pretreatment contrast-enhanced tomography scans and categorized according to consensus guidelines. Similar patients were clustered into 4 groups according to a graph-based representation of affected lymph nodes. Correlation between dysphagia associated symptoms and patient groups was calculated. Results Out of 582 patients, 26% (152) experienced toxicity during a follow up evaluation 6 months after completion of radiotherapy treatment. Patient groups identified by our approach were significantly correlated with dysphagia, feeding tube, and aspiration toxicity (p <.0005). Conclusion: Our work successfully stratified a patient cohort into similar groups using a structural geometry, graph-encoding of affected lymph nodes in OPC patients, that were predictive of late radiation-associated dysphagia. Our results suggest that structural geometry-aware characterization of affected lymph nodes can be used to better predict radiation-associated dysphagia at time of diagnosis, and better inform treatment guidelines.
Andrew Wentzel; Timothy Luciani; Lisanne V. Van Dijk; Nicolette Taku; Baher Elgohari; Abdallah S. R. Mohamed; Guadalupe Canahuate; Clifton David Fuller; David M. Vock; G. Elisabeta Marai. Precision association of lymphatic disease spread with radiation-associated toxicity in oropharyngeal squamous carcinomas. 2020, 1 .
AMA StyleAndrew Wentzel, Timothy Luciani, Lisanne V. Van Dijk, Nicolette Taku, Baher Elgohari, Abdallah S. R. Mohamed, Guadalupe Canahuate, Clifton David Fuller, David M. Vock, G. Elisabeta Marai. Precision association of lymphatic disease spread with radiation-associated toxicity in oropharyngeal squamous carcinomas. . 2020; ():1.
Chicago/Turabian StyleAndrew Wentzel; Timothy Luciani; Lisanne V. Van Dijk; Nicolette Taku; Baher Elgohari; Abdallah S. R. Mohamed; Guadalupe Canahuate; Clifton David Fuller; David M. Vock; G. Elisabeta Marai. 2020. "Precision association of lymphatic disease spread with radiation-associated toxicity in oropharyngeal squamous carcinomas." , no. : 1.
The visualization theory foundations draw on several domains, from signal processing to software design and perception. This chapter describes the landscape of visualization foundations along three aspects: a Humans aspect, a Systems aspect, and a Formal aspect, along with the domains the visualization foundations are rooted in. This chapter further provides definitions for the visualization, theory foundation, theory, model, and concept terms, and a discussion of theory granularity, from grand theories to middle-range theories and to practice theories. The chapter further discusses several challenges related to the theory fabric of the visualization that result from the diversity of our roots. The chapter ends with a discussion of possible evaluation criteria for theory components, with respect to the range of theories and models, from mathematical frameworks to guidelines and best practice advice presented in this book.
G. Elisabeta Marai; Torsten Möller. The Fabric of Visualization. Foundations of Data Visualization 2020, 5 -14.
AMA StyleG. Elisabeta Marai, Torsten Möller. The Fabric of Visualization. Foundations of Data Visualization. 2020; ():5-14.
Chicago/Turabian StyleG. Elisabeta Marai; Torsten Möller. 2020. "The Fabric of Visualization." Foundations of Data Visualization , no. : 5-14.
Purpose Using a 200 Head and Neck cancer (HNC) patient cohort, we employ patient similarity based on tumor location, volume, and proximity to organs at risk to predict radiation-associated dysphagia (RAD) in a new patient receiving intensity modulated radiation therapy (IMRT). Material and methods All patients were treated using curative-intent IMRT. Anatomical features were extracted from contrast-enhanced tomography scans acquired pre-treatment. Patient similarity was computed using a topological similarity measure, which allowed for the prediction of normal tissues' mean doses. We performed feature selection and clustering, and used the resulting groups of patients to forecast RAD. We used Logistic Regression (LG) cross-validation to assess the potential toxicity risk of these groupings. Results Out of 200 patients, 34 patients were recorded as having RAD. Patient clusters were significantly correlated with RAD (p< .0001). The area under the receiver-operator curve (AUC) using pre-established, baseline features gave a predictive accuracy of 0.79, while the addition of our cluster labels improved accuracy to 0.84. Conclusion Our results show that spatial information available pre-treatment can be used to robustly identify groups of RAD high-risk patients. We identify feature sets that considerably improve toxicity risk prediction beyond what is possible using baseline features. Our results also suggest that similarity-based predicted mean doses to organs can be used as valid predictors of risk to organs.
Andrew Wentzel; Peter Hanula; Lisanne V. van Dijk; Baher Elgohari; Abdallah S.R. Mohamed; Carlos E. Cardenas; Clifton D. Fuller; David M. Vock; Guadalupe Canahuate; G.E. Marai. Precision toxicity correlates of tumor spatial proximity to organs at risk in cancer patients receiving intensity-modulated radiotherapy. Radiotherapy and Oncology 2020, 148, 245 -251.
AMA StyleAndrew Wentzel, Peter Hanula, Lisanne V. van Dijk, Baher Elgohari, Abdallah S.R. Mohamed, Carlos E. Cardenas, Clifton D. Fuller, David M. Vock, Guadalupe Canahuate, G.E. Marai. Precision toxicity correlates of tumor spatial proximity to organs at risk in cancer patients receiving intensity-modulated radiotherapy. Radiotherapy and Oncology. 2020; 148 ():245-251.
Chicago/Turabian StyleAndrew Wentzel; Peter Hanula; Lisanne V. van Dijk; Baher Elgohari; Abdallah S.R. Mohamed; Carlos E. Cardenas; Clifton D. Fuller; David M. Vock; Guadalupe Canahuate; G.E. Marai. 2020. "Precision toxicity correlates of tumor spatial proximity to organs at risk in cancer patients receiving intensity-modulated radiotherapy." Radiotherapy and Oncology 148, no. : 245-251.
Precision medicine seeks to tailor therapy to the individual patient, based on statistical correlates from patients who are similar to the one under consideration. These correlates can and should go beyond genetics, and in general, beyond tabular or array data that can be easily represented computationally and compared. For example, in many types of cancer, cancer treatment and toxicity depend in large measure on the spatial disease spread—e.g., metastasizes to regional lymph nodes in head and neck cancer. However, there is currently a lack of methodology for integrating spatial information when considering patient similarity. We present a novel modeling methodology for the comparison of cancer patients within a cohort, based on the spatial spread of the lymph nodes affected in each patient. The method uses a topological map, bigrams, and hierarchical clustering to group patients based on their similarity. We compare this approach against a nonspatial (categorical) similarity approach where patients are binned solely by their affected nodes. We present similarity results on a 582 head and neck cancer patient cohort, along with two visual abstractions for analysis of the results, and we present clinician feedback. Our novel methodology partitions a patient cohort into clinically meaningful groups more susceptible to treatment side-effects. Such spatially-aware similarity approaches can help maximize the effectiveness of each patient’s treatment.
T. Luciani; A. Wentzel; B. Elgohari; H. Elhalawani; A. Mohamed; G. Canahuate; D.M. Vock; C.D. Fuller; G.E. Marai. A spatial neighborhood methodology for computing and analyzing lymph node carcinoma similarity in precision medicine. Journal of Biomedical Informatics 2020, 112, 100067 .
AMA StyleT. Luciani, A. Wentzel, B. Elgohari, H. Elhalawani, A. Mohamed, G. Canahuate, D.M. Vock, C.D. Fuller, G.E. Marai. A spatial neighborhood methodology for computing and analyzing lymph node carcinoma similarity in precision medicine. Journal of Biomedical Informatics. 2020; 112 ():100067.
Chicago/Turabian StyleT. Luciani; A. Wentzel; B. Elgohari; H. Elhalawani; A. Mohamed; G. Canahuate; D.M. Vock; C.D. Fuller; G.E. Marai. 2020. "A spatial neighborhood methodology for computing and analyzing lymph node carcinoma similarity in precision medicine." Journal of Biomedical Informatics 112, no. : 100067.
Biological network figures are ubiquitous in the biology and medical literature. On the one hand, a good network figure can quickly provide information about the nature and degree of interactions between items and enable inferences about the reason for those interactions. On the other hand, good network figures are difficult to create. In this paper, we outline 10 simple rules for creating biological network figures for communication, from choosing layouts, to applying color or other channels to show attributes, to the use of layering and separation. These rules are accompanied by illustrative examples. We also provide a concise set of references and additional resources for each rule.
G. Elisabeta Marai; Bruno Pinaud; Katja Bühler; Alexander Lex; John H. Morris. Ten simple rules to create biological network figures for communication. PLOS Computational Biology 2019, 15, e1007244 .
AMA StyleG. Elisabeta Marai, Bruno Pinaud, Katja Bühler, Alexander Lex, John H. Morris. Ten simple rules to create biological network figures for communication. PLOS Computational Biology. 2019; 15 (9):e1007244.
Chicago/Turabian StyleG. Elisabeta Marai; Bruno Pinaud; Katja Bühler; Alexander Lex; John H. Morris. 2019. "Ten simple rules to create biological network figures for communication." PLOS Computational Biology 15, no. 9: e1007244.
In virtual reality (VR) applications such as games, virtual training, and interactive neurorehabilitation, one can employ either the first-person user perspective or the third-person perspective to perceive the virtual environment; however, applications rarely offer both perspectives for the same task. We used a targeted-reaching task in a large-scale virtual reality environment (N=30 healthy volunteers) to evaluate the effects of user perspective on the head and upper extremity movements, and on user performance. We further evaluated how different cognitive challenges would modulate these effects. Finally, we obtained the user-reported engagement level under the different perspectives. We found that first-person perspective resulted in larger head movements (3.52±1.3m) than the third-person perspective (2.41±0.7m). First-person perspective also resulted in more upper-extremity movement (30.08±7.28m compared to 26.66±4.86m) and longer completion times (61.3±16.4s compared to 53±10.4s) for more challenging tasks such as the “flipped mode”, in which moving one arm causes the opposite virtual arm to move. We observed no significant effect of user perspective alone on the success rate. Subjects reported experiencing roughly the same level of engagement in both first-person and third-person perspectives (F(1.58)=0.9,P=.445). User perspective and its interaction with higher-cognitive load tasks influences the extent of movement and user performance in a virtual theater environment, and may influence the choice of the interface type (first or third person) in immersive training depending on the user conditions and exercise requirements.
Juan Trelles Trabucco; Andrea Rottigni; Marco Cavallo; Daniel Bailey; James Patton; G. Elisabeta Marai. User perspective and higher cognitive task-loads influence movement and performance in immersive training environments. BMC Biomedical Engineering 2019, 1, 1 -12.
AMA StyleJuan Trelles Trabucco, Andrea Rottigni, Marco Cavallo, Daniel Bailey, James Patton, G. Elisabeta Marai. User perspective and higher cognitive task-loads influence movement and performance in immersive training environments. BMC Biomedical Engineering. 2019; 1 (1):1-12.
Chicago/Turabian StyleJuan Trelles Trabucco; Andrea Rottigni; Marco Cavallo; Daniel Bailey; James Patton; G. Elisabeta Marai. 2019. "User perspective and higher cognitive task-loads influence movement and performance in immersive training environments." BMC Biomedical Engineering 1, no. 1: 1-12.
Through the use of open data portals, cities, districts and countries are increasingly making available energy consumption data. These data have the potential to inform both policymakers and local communities. At the same time, however, these datasets are large and complicated to analyze. We present the activity-centered-design, from requirements to evaluation, of a web-based visual analysis tool to explore energy consumption in Chicago. The resulting application integrates energy consumption data and census data, making it possible for both amateurs and experts to analyze disaggregated datasets at multiple levels of spatial aggregation and to compare temporal and spatial differences. An evaluation through case studies and qualitative feedback demonstrates that this visual analysis application successfully meets the goals of integrating large, disaggregated urban energy consumption datasets and of supporting analysis by both lay users and experts.
Juan Trelles Trabucco; Dongwoo Lee; Sybil Derrible; G. Elisabeta Marai. Visual Analysis of a Smart City’s Energy Consumption. Multimodal Technologies and Interaction 2019, 3, 30 .
AMA StyleJuan Trelles Trabucco, Dongwoo Lee, Sybil Derrible, G. Elisabeta Marai. Visual Analysis of a Smart City’s Energy Consumption. Multimodal Technologies and Interaction. 2019; 3 (2):30.
Chicago/Turabian StyleJuan Trelles Trabucco; Dongwoo Lee; Sybil Derrible; G. Elisabeta Marai. 2019. "Visual Analysis of a Smart City’s Energy Consumption." Multimodal Technologies and Interaction 3, no. 2: 30.
Hybrid virtual reality environments allow analysts to choose how much of the screen real estate they want to use for Virtual Reality (VR) immersion, and how much they want to use for displaying different types of 2D data. We present the use-based design and evaluation of an immersive visual analytics application for cosmological data that uses such a 2D/3D hybrid environment. The applications is a first-in-kind immersive instantiation of the Activity-Centered-Design theoretical paradigm, as well as a first documented immersive instantiation of a details-first paradigm based on scientific workflow theory. Based on a rigorous analysis of the user activities and on a details-first paradigm, the application was designed to allow multiple domain experts to interactively analyze visual representations of spatial (3D) and nonspatial (2D) cosmology data pertaining to dark matter formation. These hybrid data are represented at multiple spatiotemporal scales as time-aligned merger trees, pixel-based heatmaps, GPU-accelerated point clouds and geometric primitives, which can further be animated according to simulation data and played back for analysis. We have demonstrated this multi-scale application to several groups of lay users and domain experts, as well as to two senior domain experts from the Adler Planetarium, who have significant experience in immersive environments. Their collective feedback shows that this hybrid, immersive application can assist researchers in the interactive visual analysis of large-scale cosmological simulation data while overcoming navigation limitations of desktop visualizations.
Peter Hanula; Kamil Piekutowski; Julieta Aguilera; G. E. Marai. DarkSky Halos: Use-Based Exploration of Dark Matter Formation Data in a Hybrid Immersive Virtual Environment. Frontiers in Robotics and AI 2019, 6, 1 .
AMA StylePeter Hanula, Kamil Piekutowski, Julieta Aguilera, G. E. Marai. DarkSky Halos: Use-Based Exploration of Dark Matter Formation Data in a Hybrid Immersive Virtual Environment. Frontiers in Robotics and AI. 2019; 6 (11):1.
Chicago/Turabian StylePeter Hanula; Kamil Piekutowski; Julieta Aguilera; G. E. Marai. 2019. "DarkSky Halos: Use-Based Exploration of Dark Matter Formation Data in a Hybrid Immersive Virtual Environment." Frontiers in Robotics and AI 6, no. 11: 1.
This article provides a 25 year-long perspective on Immersive Analytics through the lens of first-in-kind technological advancements introduced at the Electronic Visualization Laboratory, University of Illinois at Chicago, along with the challenges and lessons learned from multiple immersive analytics projects.
G. Elisabeta Marai; Jason Leigh; Andrew Johnson. Immersive Analytics Lessons From the Electronic Visualization Laboratory: A 25-Year Perspective. IEEE Computer Graphics and Applications 2019, 39, 54 -66.
AMA StyleG. Elisabeta Marai, Jason Leigh, Andrew Johnson. Immersive Analytics Lessons From the Electronic Visualization Laboratory: A 25-Year Perspective. IEEE Computer Graphics and Applications. 2019; 39 (3):54-66.
Chicago/Turabian StyleG. Elisabeta Marai; Jason Leigh; Andrew Johnson. 2019. "Immersive Analytics Lessons From the Electronic Visualization Laboratory: A 25-Year Perspective." IEEE Computer Graphics and Applications 39, no. 3: 54-66.
Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the “HPV” challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the “local recurrence” challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings.
Hesham ElHalawani; Timothy A. Lin; Stefania Volpe; Abdallah S. R. Mohamed; Aubrey L. White; James Zafereo; Andrew J. Wong; Joel Berends; Shady Abohashem; Bowman Williams; Jeremy M. Aymard; Aasheesh Kanwar; Subha Perni; Crosby D. Rock; Luke Cooksey; Shauna Campbell; Pei Yang; Khahn Nguyen; Rachel B. Ger; Carlos E. Cardenas; Xenia J. Fave; Carlo Sansone; Gabriele Piantadosi; Stefano Marrone; Rongjie Liu; Chao Huang; Kaixian Yu; Tengfei Li; Yang Yu; Youyi Zhang; Hongtu Zhu; Jeffrey S. Morris; Veerabhadran Baladandayuthapani; John W. Shumway; Alakonanda Ghosh; Andrei Pöhlmann; Hady Ahmady Phoulady; Vibhas Goyal; Guadalupe Canahuate; G. Elisabeta Marai; David Vock; Stephen Y. Lai; Dennis S. Mackin; Laurence E. Court; John Freymann; Keyvan Farahani; Jayashree Kaplathy-Cramer; Clifton D. Fuller. Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges. Frontiers in Oncology 2018, 8, 1 .
AMA StyleHesham ElHalawani, Timothy A. Lin, Stefania Volpe, Abdallah S. R. Mohamed, Aubrey L. White, James Zafereo, Andrew J. Wong, Joel Berends, Shady Abohashem, Bowman Williams, Jeremy M. Aymard, Aasheesh Kanwar, Subha Perni, Crosby D. Rock, Luke Cooksey, Shauna Campbell, Pei Yang, Khahn Nguyen, Rachel B. Ger, Carlos E. Cardenas, Xenia J. Fave, Carlo Sansone, Gabriele Piantadosi, Stefano Marrone, Rongjie Liu, Chao Huang, Kaixian Yu, Tengfei Li, Yang Yu, Youyi Zhang, Hongtu Zhu, Jeffrey S. Morris, Veerabhadran Baladandayuthapani, John W. Shumway, Alakonanda Ghosh, Andrei Pöhlmann, Hady Ahmady Phoulady, Vibhas Goyal, Guadalupe Canahuate, G. Elisabeta Marai, David Vock, Stephen Y. Lai, Dennis S. Mackin, Laurence E. Court, John Freymann, Keyvan Farahani, Jayashree Kaplathy-Cramer, Clifton D. Fuller. Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges. Frontiers in Oncology. 2018; 8 ():1.
Chicago/Turabian StyleHesham ElHalawani; Timothy A. Lin; Stefania Volpe; Abdallah S. R. Mohamed; Aubrey L. White; James Zafereo; Andrew J. Wong; Joel Berends; Shady Abohashem; Bowman Williams; Jeremy M. Aymard; Aasheesh Kanwar; Subha Perni; Crosby D. Rock; Luke Cooksey; Shauna Campbell; Pei Yang; Khahn Nguyen; Rachel B. Ger; Carlos E. Cardenas; Xenia J. Fave; Carlo Sansone; Gabriele Piantadosi; Stefano Marrone; Rongjie Liu; Chao Huang; Kaixian Yu; Tengfei Li; Yang Yu; Youyi Zhang; Hongtu Zhu; Jeffrey S. Morris; Veerabhadran Baladandayuthapani; John W. Shumway; Alakonanda Ghosh; Andrei Pöhlmann; Hady Ahmady Phoulady; Vibhas Goyal; Guadalupe Canahuate; G. Elisabeta Marai; David Vock; Stephen Y. Lai; Dennis S. Mackin; Laurence E. Court; John Freymann; Keyvan Farahani; Jayashree Kaplathy-Cramer; Clifton D. Fuller. 2018. "Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges." Frontiers in Oncology 8, no. : 1.
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.
Mathew Monfort; Timothy Luciani; Jonathan Komperda; Brian Ziebart; Farzad Mashayek; G. Elisabeta Marai. A Deep Learning Approach to Identifying Shock Locations in Turbulent Combustion Tensor Fields. Eye Tracking and Visualization 2017, 375 -392.
AMA StyleMathew Monfort, Timothy Luciani, Jonathan Komperda, Brian Ziebart, Farzad Mashayek, G. Elisabeta Marai. A Deep Learning Approach to Identifying Shock Locations in Turbulent Combustion Tensor Fields. Eye Tracking and Visualization. 2017; ():375-392.
Chicago/Turabian StyleMathew Monfort; Timothy Luciani; Jonathan Komperda; Brian Ziebart; Farzad Mashayek; G. Elisabeta Marai. 2017. "A Deep Learning Approach to Identifying Shock Locations in Turbulent Combustion Tensor Fields." Eye Tracking and Visualization , no. : 375-392.
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.
Vishal Doshi; Sneha Tuteja; Krishna Bharadwaj; Davide Tantillo; Thomas Marrinan; James Patton; G. Elisabeta Marai. StickySchedule. Proceedings of the 6th ACM International Symposium on Pervasive Displays 2017, 2017, 2 .
AMA StyleVishal Doshi, Sneha Tuteja, Krishna Bharadwaj, Davide Tantillo, Thomas Marrinan, James Patton, G. Elisabeta Marai. StickySchedule. Proceedings of the 6th ACM International Symposium on Pervasive Displays. 2017; 2017 ():2.
Chicago/Turabian StyleVishal Doshi; Sneha Tuteja; Krishna Bharadwaj; Davide Tantillo; Thomas Marrinan; James Patton; G. Elisabeta Marai. 2017. "StickySchedule." Proceedings of the 6th ACM International Symposium on Pervasive Displays 2017, no. : 2.
Production of electricity and propulsion systems involve turbulent combustion. Computational modeling of turbulent combustion can improve the efficiency of these processes. However, large tensor datasets are the result of such simulations; these datasets are difficult to visualize and analyze. In this work we present an unsupervised statistical approach for the segmentation, visualization and potentially the tracking of regions of interest in large tensor data. The approach employs a machine learning clustering algorithm to locate and identify areas of interest based on specified parameters such as strain tensor value. Evaluation on two combustion datasets shows this approach can assist in the visual analysis of the combustion tensor field.
Adrian Maries; Timothy Luciani; P. H. Pisciuneri; Mehdi B. Nik; S. Levent Yilmaz; Peyman Givi; G. Elisabeta Marai. A Clustering Method for Identifying Regions of Interest in Turbulent Combustion Tensor Fields. Eye Tracking and Visualization 2015, 323 -338.
AMA StyleAdrian Maries, Timothy Luciani, P. H. Pisciuneri, Mehdi B. Nik, S. Levent Yilmaz, Peyman Givi, G. Elisabeta Marai. A Clustering Method for Identifying Regions of Interest in Turbulent Combustion Tensor Fields. Eye Tracking and Visualization. 2015; ():323-338.
Chicago/Turabian StyleAdrian Maries; Timothy Luciani; P. H. Pisciuneri; Mehdi B. Nik; S. Levent Yilmaz; Peyman Givi; G. Elisabeta Marai. 2015. "A Clustering Method for Identifying Regions of Interest in Turbulent Combustion Tensor Fields." Eye Tracking and Visualization , no. : 323-338.
Mechanistic models that describe the dynamical behaviors of biochemical systems are common in computational systems biology, especially in the realm of cellular signaling. The development of families of such models, either by a single research group or by different groups working within the same area, presents significant challenges that range from identifying structural similarities and differences between models to understanding how these differences affect system dynamics. We present the development and features of an interactive model exploration system, MOSBIE, which provides utilities for identifying similarities and differences between models within a family. Models are clustered using a custom similarity metric, and a visual interface is provided that allows a researcher to interactively compare the structures of pairs of models as well as view simulation results. We illustrate the usefulness of MOSBIE via two case studies in the cell signaling domain. We also present feedback provided by domain experts and discuss the benefits, as well as the limitations, of the approach.
John E WenskovitchJr.; Leonard A Harris; Jose-Juan Tapia; James R Faeder; G Elisabeta Marai. MOSBIE: a tool for comparison and analysis of rule-based biochemical models. BMC Bioinformatics 2014, 15, 316 .
AMA StyleJohn E WenskovitchJr., Leonard A Harris, Jose-Juan Tapia, James R Faeder, G Elisabeta Marai. MOSBIE: a tool for comparison and analysis of rule-based biochemical models. BMC Bioinformatics. 2014; 15 (1):316.
Chicago/Turabian StyleJohn E WenskovitchJr.; Leonard A Harris; Jose-Juan Tapia; James R Faeder; G Elisabeta Marai. 2014. "MOSBIE: a tool for comparison and analysis of rule-based biochemical models." BMC Bioinformatics 15, no. 1: 316.
We introduce a web-based computing infrastructure to assist the visual integration, mining and interactive navigation of large-scale astronomy observations. Following an analysis of the application domain, we design a client-server architecture to fetch distributed image data and to partition local data into a spatial index structure that allows prefix-matching of spatial objects. In conjunction with hardware-accelerated pixel-based overlays and an online cross-registration pipeline, this approach allows the fetching, displaying, panning and zooming of gigabit panoramas of the sky in real time. To further facilitate the integration and mining of spatial and non-spatial data, we introduce interactive trend images-compact visual representations for identifying outlier objects and for studying trends within large collections of spatial objects of a given class. In a demonstration, images from three sky surveys (SDSS, FIRST and simulated LSST results) are cross-registered and integrated as overlays, allowing cross-spectrum analysis of astronomy observations. Trend images are interactively generated from catalog data and used to visually mine astronomy observations of similar type. The front-end of the infrastructure uses the web technologies WebGL and HTML5 to enable cross-platform, web-based functionality. Our approach attains interactive rendering framerates; its power and flexibility enables it to serve the needs of the astronomy community. Evaluation on three case studies, as well as feedback from domain experts emphasize the benefits of this visual approach to the observational astronomy field; and its potential benefits to large scale geospatial visualization in general.
Timothy Basil Luciani; Brian Cherinka; Daniel Oliphant; Sean Myers; W. Michael Wood-Vasey; Alexandros Labrinidis; G. Elisabeta Marai. Large-Scale Overlays and Trends: Visually Mining, Panning and Zoomingthe Observable Universe. IEEE Transactions on Visualization and Computer Graphics 2014, 20, 1048 -1061.
AMA StyleTimothy Basil Luciani, Brian Cherinka, Daniel Oliphant, Sean Myers, W. Michael Wood-Vasey, Alexandros Labrinidis, G. Elisabeta Marai. Large-Scale Overlays and Trends: Visually Mining, Panning and Zoomingthe Observable Universe. IEEE Transactions on Visualization and Computer Graphics. 2014; 20 (7):1048-1061.
Chicago/Turabian StyleTimothy Basil Luciani; Brian Cherinka; Daniel Oliphant; Sean Myers; W. Michael Wood-Vasey; Alexandros Labrinidis; G. Elisabeta Marai. 2014. "Large-Scale Overlays and Trends: Visually Mining, Panning and Zoomingthe Observable Universe." IEEE Transactions on Visualization and Computer Graphics 20, no. 7: 1048-1061.
We introduce a web-based, client-server computing infrastructure to assist the interactive navigation of large-scale astronomy observations. Large image datasets are partitioned into a spatial index structure that allows prefix-matching of spatial objects. In conjunction with pixel-based overlays, this approach allows fetching, displaying, panning and zooming of gigabit panoramas of the sky in real time. Images from three sky surveys (SDSS, FIRST and simulated LSST results) are cross-registered and integrated as overlays, allowing cross-spectrum analysis of astronomy observations. The front-end of the infrastructure uses the web technologies We-bGL and HTML5 to enable cross-platform, web-based functionality. Our approach attains interactive rendering framerates; its power and flexibility enables us to serve the needs of the astronomy community. Evaluation on a galaxy case study, as well as feedback from domain experts emphasize the benefits of this visual approach to the observational astronomy field.
Timothy Luciani; Boyu Sun; Brian Cherinka; W. Michael Wood-Vasey; G. Elisabeta Marai; Sean Myers; Alexandros Labrinidis. Panning and zooming the observable universe with prefix-matching indices and pixel-based overlays. IEEE Symposium on Large Data Analysis and Visualization (LDAV) 2012, 1 -8.
AMA StyleTimothy Luciani, Boyu Sun, Brian Cherinka, W. Michael Wood-Vasey, G. Elisabeta Marai, Sean Myers, Alexandros Labrinidis. Panning and zooming the observable universe with prefix-matching indices and pixel-based overlays. IEEE Symposium on Large Data Analysis and Visualization (LDAV). 2012; ():1-8.
Chicago/Turabian StyleTimothy Luciani; Boyu Sun; Brian Cherinka; W. Michael Wood-Vasey; G. Elisabeta Marai; Sean Myers; Alexandros Labrinidis. 2012. "Panning and zooming the observable universe with prefix-matching indices and pixel-based overlays." IEEE Symposium on Large Data Analysis and Visualization (LDAV) , no. : 1-8.
These preliminary results show that pixel-based overlays have the potential to generate scalable, graphical representations of astronomy data. This approach may allow us to overcome bandwidth and screen-space current limitations in astronomy database visualization by following a WebGL - PHP client-server architecture. The advantages of this approach are its versatility and visual scalability (to the pixel level), enabling the visual analysis of large datasets. The resulting versatility allows for flexible control over the visualization and the client-side scripts. Accessing graphics hardware through WebGL further provides the users with a rich, graphics-accelerated web experience. Preliminary feedback from astronomy researchers emphasizes the benefits of visual analysis to this field.
Timothy Luciani; Rebecca Hachey; Daniel Q. Oliphant; Brian A. Cherinka; G. Elisabeta Marai. Pixel-based overlays for navigating a galaxy of observations. 2011 IEEE Symposium on Large Data Analysis and Visualization 2011, 137 -138.
AMA StyleTimothy Luciani, Rebecca Hachey, Daniel Q. Oliphant, Brian A. Cherinka, G. Elisabeta Marai. Pixel-based overlays for navigating a galaxy of observations. 2011 IEEE Symposium on Large Data Analysis and Visualization. 2011; ():137-138.
Chicago/Turabian StyleTimothy Luciani; Rebecca Hachey; Daniel Q. Oliphant; Brian A. Cherinka; G. Elisabeta Marai. 2011. "Pixel-based overlays for navigating a galaxy of observations." 2011 IEEE Symposium on Large Data Analysis and Visualization , no. : 137-138.