Manually Acquiring Targets From Multiple Viewpoints Using Video Feedback.

OBJECTIVE: The effect of camera viewpoint was studied when performing visually obstructed psychomotor targeting tasks. BACKGROUND: Previous research in laparoscopy and robotic teleoperation found that complex perceptual-motor adaptations associated with misaligned viewpoints corresponded to degraded performance in manipulation. Because optimal camera positioning is often unavailable in restricted environments, alternative viewpoints that might mitigate performance effects are not obvious. METHODS: A virtual keyboard-controlled targeting task was remotely distributed to workers of Amazon Mechanical Turk. The experiment was performed by 192 subjects for a static viewpoint with independent parameters of target direction, Fitts' law index of difficulty, viewpoint azimuthal angle (AA), and viewpoint polar angle (PA). A dynamic viewpoint experiment was also performed by 112 subjects in which the viewpoint AA changed after every trial. RESULTS: AA and target direction had significant effects on performance for the static viewpoint experiment. Movement time and travel distance increased while AA increased until there was a discrete improvement in performance for 180°. Increasing AA from 225° to 315° linearly decreased movement time and distance. There were significant main effects of current AA and magnitude of transition for the dynamic viewpoint experiment. Orthogonal direction and no-change viewpoint transitions least affected performance. CONCLUSIONS: Viewpoint selection should aim to minimize associated rotations within the manipulation plane when performing targeting tasks whether implementing a static or dynamic viewing solution. Because PA rotations had negligible performance effects, PA adjustments may extend the space of viable viewpoints. APPLICATIONS: These results can inform viewpoint selection for visual feedback during psychomotor tasks.

LayerCake: a tool for the visual comparison of viral deep sequencing data.

MOTIVATION: The advent of next-generation sequencing (NGS) has created unprecedented opportunities to examine viral populations within individual hosts, among infected individuals and over time. Comparing sequence variability across viral genomes allows for the construction of complex population structures, the analysis of which can yield powerful biological insights. However, the simultaneous display of sequence variation, coverage depth and quality scores across thousands of bases presents a unique visualization challenge that has not been fully met by current NGS analysis tools. RESULTS: Here, we present LayerCake, a self-contained visualization tool that allows for the rapid analysis of variation in viral NGS data. LayerCake enables the user to simultaneously visualize variations in multiple viral populations across entire genomes within a highly customizable framework, drawing attention to pertinent and interesting patterns of variation. We have successfully deployed LayerCake to assist with a variety of different genomics datasets. AVAILABILITY AND IMPLEMENTATION: Program downloads and detailed instructions are available at http://graphics.cs.wisc.edu/WP/layercake under a modified MIT license. LayerCake is a cross-platform tool written in the Processing framework for Java. CONTACT: mcorrell@cs.wisc.edu.

Four types of ensemble coding in data visualizations.

Ensemble coding supports rapid extraction of visual statistics about distributed visual information. Researchers typically study this ability with the goal of drawing conclusions about how such coding extracts information from natural scenes. Here we argue that a second domain can serve as another strong inspiration for understanding ensemble coding: graphs, maps, and other visual presentations of data. Data visualizations allow observers to leverage their ability to perform visual ensemble statistics on distributions of spatial or featural visual information to estimate actual statistics on data. We survey the types of visual statistical tasks that occur within data visualizations across everyday examples, such as scatterplots, and more specialized images, such as weather maps or depictions of patterns in text. We divide these tasks into four categories: identification of sets of values, summarization across those values, segmentation of collections, and estimation of structure. We point to unanswered questions for each category and give examples of such cross-pollination in the current literature. Increased collaboration between the data visualization and perceptual psychology research communities can inspire new solutions to challenges in visualization while simultaneously exposing unsolved problems in perception research.

Two novel simian arteriviruses in captive and wild baboons (Papio spp.).

UNLABELLED: Since the 1960s, simian hemorrhagic fever virus (SHFV; Nidovirales, Arteriviridae) has caused highly fatal outbreaks of viral hemorrhagic fever in captive Asian macaque colonies. However, the source(s) of these outbreaks and the natural reservoir(s) of this virus remain obscure. Here we report the identification of two novel, highly divergent simian arteriviruses related to SHFV, Mikumi yellow baboon virus 1 (MYBV-1) and Southwest baboon virus 1 (SWBV-1), in wild and captive baboons, respectively, and demonstrate the recent transmission of SWBV-1 among captive baboons. These findings extend our knowledge of the genetic and geographic diversity of the simian arteriviruses, identify baboons as a natural host of these viruses, and provide further evidence that baboons may have played a role in previous outbreaks of simian hemorrhagic fever in macaques, as has long been suspected. This knowledge should aid in the prevention of disease outbreaks in captive macaques and supports the growing body of evidence that suggests that simian arterivirus infections are common in Old World monkeys of many different species throughout Africa. IMPORTANCE: Historically, the emergence of primate viruses both in humans and in other primate species has caused devastating outbreaks of disease. One strategy for preventing the emergence of novel primate pathogens is to identify microbes with the potential for cross-species transmission in their natural state within reservoir species from which they might emerge. Here, we detail the discovery and characterization of two related simian members of the Arteriviridae family that have a history of disease emergence and host switching. Our results expand the phylogenetic and geographic range of the simian arteriviruses and define baboons as a natural host for these viruses. Our findings also identify a potential threat to captive macaque colonies by showing that simian arteriviruses are actively circulating in captive baboons.

Conditional CD8+ T cell escape during acute simian immunodeficiency virus infection.

CD8+ T cell responses rapidly select viral variants during acute human immunodeficiency virus (HIV)/simian immunodeficiency virus (SIV) infection. We used pyrosequencing to examine variation within three SIV-derived epitopes (Gag386₋394GW9, Nef103₋111RM9, and Rev59₋68SP10) targeted by immunodominant CD8+ T cell responses in acutely infected Mauritian cynomolgus macaques. In animals recognizing all three epitopes, variation within Rev59₋68SP10 was associated with delayed accumulation of variants in Gag386₋394GW9 but had no effect on variation within Nef103₋111RM9. This demonstrates that the entire T cell repertoire, rather than a single T cell population, influences the timing of immune escape, thereby providing the first example of conditional CD8+ T cell escape in HIV/SIV infection.

A Problem Space for Designing Visualizations.

Visualization researchers and visualization professionals seek appropriate abstractions of visualization requirements that permit considering visualization solutions independently from specific problems. Abstractions can help us design, analyze, organize, and evaluate the things we create. The literature has many task structures (taxonomies, typologies, etc.), design spaces, and related "frameworks" that provide abstractions of the problems a visualization is meant to address. In this Visualization Viewpoints article, we introduce a different one, a problem space that complements existing frameworks by focusing on the needs that a visualization is meant to solve. We believe it provides a valuable conceptual tool for designing and discussing visualizations.

Local functional descriptors for surface comparison based binding prediction.

BACKGROUND: Molecular recognition in proteins occurs due to appropriate arrangements of physical, chemical, and geometric properties of an atomic surface. Similar surface regions should create similar binding interfaces. Effective methods for comparing surface regions can be used in identifying similar regions, and to predict interactions without regard to the underlying structural scaffold that creates the surface. RESULTS: We present a new descriptor for protein functional surfaces and algorithms for using these descriptors to compare protein surface regions to identify ligand binding interfaces. Our approach uses descriptors of local regions of the surface, and assembles collections of matches to compare larger regions. Our approach uses a variety of physical, chemical, and geometric properties, adaptively weighting these properties as appropriate for different regions of the interface. Our approach builds a classifier based on a training corpus of examples of binding sites of the target ligand. The constructed classifiers can be applied to a query protein providing a probability for each position on the protein that the position is part of a binding interface. We demonstrate the effectiveness of the approach on a number of benchmarks, demonstrating performance that is comparable to the state-of-the-art, with an approach with more generality than these prior methods. CONCLUSIONS: Local functional descriptors offer a new method for protein surface comparison that is sufficiently flexible to serve in a variety of applications.

GRAPE: GRaphical Abstracted Protein Explorer.

The region surrounding a protein, known as the surface of interaction or molecular surface, can provide valuable insight into its function. Unfortunately, due to the complexity of both their geometry and their surface fields, study of these surfaces can be slow and difficult and important features may be hard to identify. Here, we describe our GRaphical Abstracted Protein Explorer, or GRAPE, a web server that allows users to explore abstracted representations of proteins. These abstracted surfaces effectively reduce the level of detail of the surface of a macromolecule, using a specialized algorithm that removes small bumps and pockets, while preserving large-scale structural features. Scalar fields, such as electrostatic potential and hydropathy, are smoothed to further reduce visual complexity. This entirely new way of looking at proteins complements more traditional views of the molecular surface. GRAPE includes a thin 3D viewer that allows users to quickly flip back and forth between both views. Abstracted views provide a fast way to assess both a molecule's shape and its different surface field distributions. GRAPE is freely available at http://grape.uwbacter.org.

embComp: Visual Interactive Comparison of Vector Embeddings.

This article introduces embComp, a novel approach for comparing two embeddings that capture the similarity between objects, such as word and document embeddings. We survey scenarios where comparing these embedding spaces is useful. From those scenarios, we derive common tasks, introduce visual analysis methods that support these tasks, and combine them into a comprehensive system. One of embComp's central features are overview visualizations that are based on metrics for measuring differences in the local structure around objects. Summarizing these local metrics over the embeddings provides global overviews of similarities and differences. Detail views allow comparison of the local structure around selected objects and relating this local information to the global views. Integrating and connecting all of these components, embComp supports a range of analysis workflows that help understand similarities and differences between embedding spaces. We assess our approach by applying it in several use cases, including understanding corpora differences via word vector embeddings, and understanding algorithmic differences in generating embeddings.

Single-query Path Planning Using Sample-efficient Probability Informed Trees.

In this work, we present a novel sampling-based path planning method, called SPRINT. The method finds solutions for high dimensional path planning problems quickly and robustly. Its efficiency comes from minimizing the number of collision check samples. This reduction in sampling relies on heuristics that predict the likelihood that samples will be useful in the search process. Specifically, heuristics (1) prioritize more promising search regions; (2) cull samples from local minima regions; and (3) steer the search away from previously observed collision states. Empirical evaluations show that our method finds shorter or comparable-length solution paths in significantly less time than commonly used methods. We demonstrate that these performance gains can be largely attributed to our approach to achieve sample efficiency.

CellO: comprehensive and hierarchical cell type classification of human cells with the Cell Ontology.

Cell type annotation is a fundamental task in the analysis of single-cell RNA-sequencing data. In this work, we present CellO, a machine learning-based tool for annotating human RNA-seq data with the Cell Ontology. CellO enables accurate and standardized cell type classification of cell clusters by considering the rich hierarchical structure of known cell types. Furthermore, CellO comes pre-trained on a comprehensive data set of human, healthy, untreated primary samples in the Sequence Read Archive. CellO's comprehensive training set enables it to run out of the box on diverse cell types and achieves competitive or even superior performance when compared to existing state-of-the-art methods. Lastly, CellO's linear models are easily interpreted, thereby enabling exploration of cell-type-specific expression signatures across the ontology. To this end, we also present the CellO Viewer: a web application for exploring CellO's models across the ontology.

Assessing limited visibility feedback for overhead manufacturing assembly tasks.

Worker posture, task time and performance are often affected when one-handed manual dexterous tasks are performed in small overhead spaces under an obscured view. A common method used for supplementing visual feedback in these cases is a hand-held telescopic mirror, but that involves working with both arms extended overhead, and is often accompanied by awkward neck and shoulder postures. A video camera was considered as an alternative to using a mirror for visual feedback and reducing overhead reach. A mirror, a borescope and an omnidirectional camera were evaluated while laboratory participants performed three one-handed simulated manufacturing tasks in a small overhead enclosure. Videos were recorded for quantifying the time that postures were assumed while performing the tasks. The average time that both arms were above mid-shoulder height for the omnidirectional camera was more than 2.5 times less than for the mirror and borescope. The average proportion of neck strain time was 0.01% (or less) for both the omnidirectional camera and the borescope, compared to 83.68% for the mirror. No significant differences were observed in task completion times between the three modalities. Hence, an omnidirectional camera can provide visibility while reducing straining postures for manufacturing operations involving overhead work.

Task-Level Authoring for Remote Robot Teleoperation.

Remote teleoperation of robots can broaden the reach of domain specialists across a wide range of industries such as home maintenance, health care, light manufacturing, and construction. However, current direct control methods are impractical, and existing tools for programming robot remotely have focused on users with significant robotic experience. Extending robot remote programming to end users, i.e., users who are experts in a domain but novices in robotics, requires tools that balance the rich features necessary for complex teleoperation tasks with ease of use. The primary challenge to usability is that novice users are unable to specify complete and robust task plans to allow a robot to perform duties autonomously, particularly in highly variable environments. Our solution is to allow operators to specify shorter sequences of high-level commands, which we call task-level authoring, to create periods of variable robot autonomy. This approach allows inexperienced users to create robot behaviors in uncertain environments by interleaving exploration, specification of behaviors, and execution as separate steps. End users are able to break down the specification of tasks and adapt to the current needs of the interaction and environments, combining the reactivity of direct control to asynchronous operation. In this paper, we describe a prototype system contextualized in light manufacturing and its empirical validation in a user study where 18 participants with some programming experience were able to perform a variety of complex telemanipulation tasks with little training. Our results show that our approach allowed users to create flexible periods of autonomy and solve rich manipulation tasks. Furthermore, participants significantly preferred our system over comparative more direct interfaces, demonstrating the potential of our approach for enabling end users to effectively perform remote robot programming.

Corrective Shared Autonomy for Addressing Task Variability.

Many tasks, particularly those involving interaction with the environment, are characterized by high variability, making robotic autonomy difficult. One flexible solution is to introduce the input of a human with superior experience and cognitive abilities as part of a shared autonomy policy. However, current methods for shared autonomy are not designed to address the wide range of necessary corrections (e.g., positions, forces, execution rate, etc.) that the user may need to provide to address task variability. In this paper, we present corrective shared autonomy, where users provide corrections to key robot state variables on top of an otherwise autonomous task model. We provide an instantiation of this shared autonomy paradigm and demonstrate its viability and benefits such as low user effort and physical demand via a system-level user study on three tasks involving variability situated in aircraft manufacturing.

CAVA: A Visual Analytics System for Exploratory Columnar Data Augmentation Using Knowledge Graphs.

Most visual analytics systems assume that all foraging for data happens before the analytics process; once analysis begins, the set of data attributes considered is fixed. Such separation of data construction from analysis precludes iteration that can enable foraging informed by the needs that arise in-situ during the analysis. The separation of the foraging loop from the data analysis tasks can limit the pace and scope of analysis. In this paper, we present CAVA, a system that integrates data curation and data augmentation with the traditional data exploration and analysis tasks, enabling information foraging in-situ during analysis. Identifying attributes to add to the dataset is difficult because it requires human knowledge to determine which available attributes will be helpful for the ensuing analytical tasks. CAVA crawls knowledge graphs to provide users with a a broad set of attributes drawn from external data to choose from. Users can then specify complex operations on knowledge graphs to construct additional attributes. CAVA shows how visual analytics can help users forage for attributes by letting users visually explore the set of available data, and by serving as an interface for query construction. It also provides visualizations of the knowledge graph itself to help users understand complex joins such as multi-hop aggregations. We assess the ability of our system to enable users to perform complex data combinations without programming in a user study over two datasets. We then demonstrate the generalizability of CAVA through two additional usage scenarios. The results of the evaluation confirm that CAVA is effective in helping the user perform data foraging that leads to improved analysis outcomes, and offer evidence in support of integrating data augmentation as a part of the visual analytics pipeline.

Short-term exposure to testosterone propionate leads to rapid bill color and dominance changes in zebra finches.

Testosterone (T) can influence both male-male competition and mate choice displays. In zebra finches, female mate choice is based in part on bill color, and bill color has been shown to be enhanced by long-term testosterone supplementation. However, it is not clear whether bill color plays a role in male-male interactions and how bill color responds to shorter-term changes in T. We tested whether a single injection of testosterone propionate (TP) would influence male-male dominance interactions and lead to rapid (over a three-day period) changes in bill color. In addition, we tested whether bill color predicted aggression and dominance. We allowed birds in triads to establish hierarchies and then injected either dominant or subordinate individuals with TP, in addition to establishing sham control triads. We found that red chroma, but not hue, predicted aggressiveness of males. Exposure to TP led both dominant and subordinate birds to increase dominance scores over three days, longer than the <24h period in which injected TP stays active. In addition, exposure to TP increased red chroma and hue in three days showing the dynamic nature of allocation of pigments to the bill. Our results suggest that zebra finches can modulate T and bill color levels over short time periods and these changes may occur through positive feedback between T-levels and dominance.

Multi-scale surface descriptors.

Local shape descriptors compactly characterize regions of a surface, and have been applied to tasks in visualization, shape matching, and analysis. Classically, curvature has be used as a shape descriptor; however, this differential property characterizes only an infinitesimal neighborhood. In this paper, we provide shape descriptors for surface meshes designed to be multi-scale, that is, capable of characterizing regions of varying size. These descriptors capture statistically the shape of a neighborhood around a central point by fitting a quadratic surface. They therefore mimic differential curvature, are efficient to compute, and encode anisotropy. We show how simple variants of mesh operations can be used to compute the descriptors without resorting to expensive parameterizations, and additionally provide a statistical approximation for reduced computational cost. We show how these descriptors apply to a number of uses in visualization, analysis, and matching of surfaces, particularly to tasks in protein surface analysis.

Shared control-based bimanual robot manipulation.

Human-centered environments provide affordances for and require the use of two-handed, or bimanual, manipulations. Robots designed to function in, and physically interact with, these environments have not been able to meet these requirements because standard bimanual control approaches have not accommodated the diverse, dynamic, and intricate coordinations between two arms to complete bimanual tasks. In this work, we enabled robots to more effectively perform bimanual tasks by introducing a bimanual shared-control method. The control method moves the robot's arms to mimic the operator's arm movements but provides on-the-fly assistance to help the user complete tasks more easily. Our method used a bimanual action vocabulary, constructed by analyzing how people perform two-hand manipulations, as the core abstraction level for reasoning about how to assist in bimanual shared autonomy. The method inferred which individual action from the bimanual action vocabulary was occurring using a sequence-to-sequence recurrent neural network architecture and turned on a corresponding assistance mode, signals introduced into the shared-control loop designed to make the performance of a particular bimanual action easier or more efficient. We demonstrate the effectiveness of our method through two user studies that show that novice users could control a robot to complete a range of complex manipulation tasks more successfully using our method compared to alternative approaches. We discuss the implications of our findings for real-world robot control scenarios.

Text scaffolds for effective surface labeling.

In this paper we introduce a technique for applying textual labels to 3D surfaces. An effective labeling must balance the conflicting goals of conveying the shape of the surface while being legible from a range of viewing directions. Shape can be conveyed by placing the text as a texture directly on the surface, providing shape cues, meaningful landmarks and minimally obstructing the rest of the model. But rendering such surface text is problematic both in regions of high curvature, where text would be warped, and in highly occluded regions, where it would be hidden. Our approach achieves both labeling goals by applying surface labels to a 'text scaffold', a surface explicitly constructed to hold the labels. Text scaffolds conform to the underlying surface whenever possible, but can also float above problem regions, allowing them to be smooth while still conveying the overall shape. This paper provides methods for constructing scaffolds from a variety of input sources, including meshes, constructive solid geometry, and scalar fields. These sources are first mapped into a distance transform, which is then filtered and used to construct a new mesh on which labels are either manually or automatically placed. In the latter case, annotated regions of the input surface are associated with proximal regions on the new mesh, and labels placed using cartographic principles.

Considerations for Visualizing Comparison.

Supporting comparison is a common and diverse challenge in visualization. Such support is difficult to design because solutions must address both the specifics of their scenario as well as the general issues of comparison. This paper aids designers by providing a strategy for considering those general issues. It presents four considerations that abstract comparison. These considerations identify issues and categorize solutions in a domain independent manner. The first considers how the common elements of comparison-a target set of items that are related and an action the user wants to perform on that relationship-are present in an analysis problem. The second considers why these elements lead to challenges because of their scale, in number of items, complexity of items, or complexity of relationship. The third considers what strategies address the identified scaling challenges, grouping solutions into three broad categories. The fourth considers which visual designs map to these strategies to provide solutions for a comparison analysis problem. In sequence, these considerations provide a process for developers to consider support for comparison in the design of visualization tools. Case studies show how these considerations can help in the design and evaluation of visualization solutions for comparison problems.