La noche de los asesinos: Text, Staging and Audience

La noche de los asesinos: Text, Staging and Audienc

Flexible occlusion rendering for improved views of three-dimensional medical images

The goal of this work is to enable more rapid and accurate diagnosis of pathology from three-dimensional (3D) medical images by augmenting standard volume rendering techniques to display otherwise-occluded features within the volume. When displaying such data sets with volume rendering, appropriate selection of the transfer function is critical for determining which features of the data will be displayed. In many cases, however, no transfer function is able to produce the most useful views for diagnosis of pathology. Flexible Occlusion Rendering (FOR) is an addition to standard ray cast volume rendering that modulates accumulated color and opacity along each ray upon detecting features indicating the separation between objects of the same intensity range. For contrast-enhanced MRI and CT data, these separation features are intensity peaks. To detect these peaks, a dual-threshold method is used to reduce sensitivity to noise. To further reduce noise and enable control over the spatial scale of the features detected, a smoothed version of the original data set is used for feature detection, while rendering the original data at high resolution. Separating the occlusion feature detection from the volume rendering transfer function enables robust occlusion determination and seamless transition from occluded views to non-occluded views of surfaces during virtual fly-throughs. FOR has been applied to virtual arthroscopy of joints from MRI data. For example, survey views of entire shoulder socket surfaces have been rendered to enable rapid evaluation by automatically removing the occluding material of the humeral head. Such views are not possible with standard volume rendering. FOR has also been successfully applied to virtual ureteroscopy of the renal collecting system from CT data, and knee fracture visualization from CT data

Visual Analysis of High-Dimensional Event Sequence Data via Dynamic Hierarchical Aggregation

Temporal event data are collected across a broad range of domains, and a variety of visual analytics techniques have been developed to empower analysts working with this form of data. These techniques generally display aggregate statistics computed over sets of event sequences that share common patterns. Such techniques are often hindered, however, by the high-dimensionality of many real-world event sequence datasets because the large number of distinct event types within such data prevents effective aggregation. A common coping strategy for this challenge is to group event types together as a pre-process, prior to visualization, so that each group can be represented within an analysis as a single event type. However, computing these event groupings as a pre-process also places significant constraints on the analysis. This paper presents a dynamic hierarchical aggregation technique that leverages a predefined hierarchy of dimensions to computationally quantify the informativeness of alternative levels of grouping within the hierarchy at runtime. This allows users to dynamically explore the hierarchy to select the most appropriate level of grouping to use at any individual step within an analysis. Key contributions include an algorithm for interactively determining the most informative set of event groupings from within a large-scale hierarchy of event types, and a scatter-plus-focus visualization that supports interactive hierarchical exploration. While these contributions are generalizable to other types of problems, we apply them to high-dimensional event sequence analysis using large-scale event type hierarchies from the medical domain. We describe their use within a medical cohort analysis tool called Cadence, demonstrate an example in which the proposed technique supports better views of event sequence data, and report findings from domain expert interviews.Comment: To Appear in IEEE Transactions on Visualization and Computer Graphics (TVCG), Volume 26 Issue 1, 2020. Also part of proceedings for IEEE VAST 201

Selection Bias Tracking and Detailed Subset Comparison for High-Dimensional Data

The collection of large, complex datasets has become common across a wide variety of domains. Visual analytics tools increasingly play a key role in exploring and answering complex questions about these large datasets. However, many visualizations are not designed to concurrently visualize the large number of dimensions present in complex datasets (e.g. tens of thousands of distinct codes in an electronic health record system). This fact, combined with the ability of many visual analytics systems to enable rapid, ad-hoc specification of groups, or cohorts, of individuals based on a small subset of visualized dimensions, leads to the possibility of introducing selection bias--when the user creates a cohort based on a specified set of dimensions, differences across many other unseen dimensions may also be introduced. These unintended side effects may result in the cohort no longer being representative of the larger population intended to be studied, which can negatively affect the validity of subsequent analyses. We present techniques for selection bias tracking and visualization that can be incorporated into high-dimensional exploratory visual analytics systems, with a focus on medical data with existing data hierarchies. These techniques include: (1) tree-based cohort provenance and visualization, with a user-specified baseline cohort that all other cohorts are compared against, and visual encoding of the drift for each cohort, which indicates where selection bias may have occurred, and (2) a set of visualizations, including a novel icicle-plot based visualization, to compare in detail the per-dimension differences between the baseline and a user-specified focus cohort. These techniques are integrated into a medical temporal event sequence visual analytics tool. We present example use cases and report findings from domain expert user interviews.Comment: IEEE Transactions on Visualization and Computer Graphics (TVCG), Volume 26 Issue 1, 2020. Also part of proceedings for IEEE VAST 201

Woodwind Recital

This is the program for the woodwind recital featuring Dean Morris, Debra Fanks, Jane Chu, Becky Davis. Additionally, the flute ensemble, including Becky Davis, April Davis, Pam Estes, Nancilou Poole, Jane Chu, and Debra Franks, performed. This recital took place on April 27, 1976, in the Mabee Fine Arts Center Recital Hall

An automated method mapping parametric features between computer aided design software

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonEnterprise efficiency is limited by data exchange. A product designer might specify the geometry of a product with a Computer Aided Design program, an engineer might re-use that geometry data to calculate physical properties of the product using a Finite Element Analysis program. These different domains place different requirements on the product representation. Representations of product data required for different tasks is dependent on the vendor software associated with those tasks, sharing data between different vendor programs is limited by incompatibility of the vendor formats used. In the case of Computer Aided Design where the virtual form of an object is modelled, no standard data format captures complete model data. Common data standards transfer model surface geometry without capturing the topological elements from which these geometries are constructed. There are prescriptive data representations to allow these features to be specified in a neutral format, but little incentive for vendors to adopt these schemes. Recent efforts instead focus on identifying similar feature elements between different vendor CAD programs, however this approach relies on onerous manual identification requiring frequent revision. This research develops methods to automate the task of mapping relationships between different data format representations. Two independent matching techniques identify similar CAD feature functions between heterogeneous programs. Text similarity and object geometry matching techniques are combined to match the data formats associated with CAD programs. An efficient search for matching function parameters is performed using a genetic algorithm that incorporates semantic data matching and geometry data matching. A greedy semantic matching algorithm is developed that compares with the Doc2vec short text matching technique over the API dataset tested. A SVD geometric surface registration technique is developed that requires fewer calculations than an equivalent Iterative Closest Point method