Designing Improved Sediment Transport Visualizations

Monitoring, or more commonly, modeling of sediment transport in the coastal environment is a critical task with relevance to coastline stability, beach erosion, tracking environmental contaminants, and safety of navigation. Increased intensity and regularity of storms such as Superstorm Sandy heighten the importance of our understanding of sediment transport processes. A weakness of current modeling capabilities is the ability to easily visualize the result in an intuitive manner. Many of the available visualization software packages display only a single variable at once, usually as a two-dimensional, plan-view cross-section. With such limited display capabilities, sophisticated 3D models are undermined in both the interpretation of results and dissemination of information to the public. Here we explore a subset of existing modeling capabilities (specifically, modeling scour around man-made structures) and visualization solutions, examine their shortcomings and present a design for a 4D visualization for sediment transport studies that is based on perceptually-focused data visualization research and recent and ongoing developments in multivariate displays. Vector and scalar fields are co-displayed, yet kept independently identifiable utilizing human perception\u27s separation of color, texture, and motion. Bathymetry, sediment grain-size distribution, and forcing hydrodynamics are a subset of the variables investigated for simultaneous representation. Direct interaction with field data is tested to support rapid validation of sediment transport model results. Our goal is a tight integration of both simulated data and real world observations to support analysis and simulation of the impact of major sediment transport events such as hurricanes. We unite modeled results and field observations within a geodatabase designed as an application schema of the Arc Marine Data Model. Our real-world focus is on the Redbird Artificial Reef Site, roughly 18 nautical miles offshor- Delaware Bay, Delaware, where repeated surveys have identified active scour and bedform migration in 27 m water depth amongst the more than 900 deliberately sunken subway cars and vessels. Coincidently collected high-resolution multibeam bathymetry, backscatter, and side-scan sonar data from surface and autonomous underwater vehicle (AUV) systems along with complementary sub-bottom, grab sample, bottom imagery, and wave and current (via ADCP) datasets provide the basis for analysis. This site is particularly attractive due to overlap with the Delaware Bay Operational Forecast System (DBOFS), a model that provides historical and forecast oceanographic data that can be tested in hindcast against significant changes observed at the site during Superstorm Sandy and in predicting future changes through small-scale modeling around the individual reef objects

The visual uncertainty paradigm for controlling screen-space information in visualization

The information visualization pipeline serves as a lossy communication channel for presentation of data on a screen-space of limited resolution. The lossy communication is not just a machine-only phenomenon due to information loss caused by translation of data, but also a reflection of the degree to which the human user can comprehend visual information. The common entity in both aspects is the uncertainty associated with the visual representation. However, in the current linear model of the visualization pipeline, visual representation is mostly considered as the ends rather than the means for facilitating the analysis process. While the perceptual side of visualization is also being studied, little attention is paid to the way the visualization appears on the display. Thus, we believe there is a need to study the appearance of the visualization on a limited-resolution screen in order to understand its own properties and how they influence the way they represent the data. I argue that the visual uncertainty paradigm for controlling screen-space information will enable us in achieving user-centric optimization of a visualization in different application scenarios. Conceptualization of visual uncertainty enables us to integrate the encoding and decoding aspects of visual representation into a holistic framework facilitating the definition of metrics that serve as a bridge between the last stages of the visualization pipeline and the user's perceptual system. The goal of this dissertation is three-fold: i) conceptualize a visual uncertainty taxonomy in the context of pixel-based, multi-dimensional visualization techniques that helps systematic definition of screen-space metrics, ii) apply the taxonomy for identifying sources of useful visual uncertainty that helps in protecting privacy of sensitive data and also for identifying the types of uncertainty that can be reduced through interaction techniques, and iii) application of the metrics for designing information-assisted models that help in visualization of high-dimensional, temporal data

The design-by-adaptation approach to universal access: learning from videogame technology

This paper proposes an alternative approach to the design of universally accessible interfaces to that provided by formal design frameworks applied ab initio to the development of new software. This approach, design-byadaptation, involves the transfer of interface technology and/or design principles from one application domain to another, in situations where the recipient domain is similar to the host domain in terms of modelled systems, tasks and users. Using the example of interaction in 3D virtual environments, the paper explores how principles underlying the design of videogame interfaces may be applied to a broad family of visualization and analysis software which handles geographical data (virtual geographic environments, or VGEs). One of the motivations behind the current study is that VGE technology lags some way behind videogame technology in the modelling of 3D environments, and has a less-developed track record in providing the variety of interaction methods needed to undertake varied tasks in 3D virtual worlds by users with varied levels of experience. The current analysis extracted a set of interaction principles from videogames which were used to devise a set of 3D task interfaces that have been implemented in a prototype VGE for formal evaluation

GRASS: Generative Recursive Autoencoders for Shape Structures

We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures. Our key insight is that 3D shapes are effectively characterized by their hierarchical organization of parts, which reflects fundamental intra-shape relationships such as adjacency and symmetry. We develop a recursive neural net (RvNN) based autoencoder to map a flat, unlabeled, arbitrary part layout to a compact code. The code effectively captures hierarchical structures of man-made 3D objects of varying structural complexities despite being fixed-dimensional: an associated decoder maps a code back to a full hierarchy. The learned bidirectional mapping is further tuned using an adversarial setup to yield a generative model of plausible structures, from which novel structures can be sampled. Finally, our structure synthesis framework is augmented by a second trained module that produces fine-grained part geometry, conditioned on global and local structural context, leading to a full generative pipeline for 3D shapes. We demonstrate that without supervision, our network learns meaningful structural hierarchies adhering to perceptual grouping principles, produces compact codes which enable applications such as shape classification and partial matching, and supports shape synthesis and interpolation with significant variations in topology and geometry.Comment: Corresponding author: Kai Xu ([email protected]

Learning Human Motion Models for Long-term Predictions

We propose a new architecture for the learning of predictive spatio-temporal motion models from data alone. Our approach, dubbed the Dropout Autoencoder LSTM, is capable of synthesizing natural looking motion sequences over long time horizons without catastrophic drift or motion degradation. The model consists of two components, a 3-layer recurrent neural network to model temporal aspects and a novel auto-encoder that is trained to implicitly recover the spatial structure of the human skeleton via randomly removing information about joints during training time. This Dropout Autoencoder (D-AE) is then used to filter each predicted pose of the LSTM, reducing accumulation of error and hence drift over time. Furthermore, we propose new evaluation protocols to assess the quality of synthetic motion sequences even for which no ground truth data exists. The proposed protocols can be used to assess generated sequences of arbitrary length. Finally, we evaluate our proposed method on two of the largest motion-capture datasets available to date and show that our model outperforms the state-of-the-art on a variety of actions, including cyclic and acyclic motion, and that it can produce natural looking sequences over longer time horizons than previous methods

Smell's puzzling discrepancy: Gifted discrimination, yet pitiful identification

Mind &Language, Volume 35, Issue 1, Page 90-114, February 2020

Listening-Mode-Centered Sonification Design for Data Exploration

Grond F. Listening-Mode-Centered Sonification Design for Data Exploration. Bielefeld: Bielefeld University; 2013.From the Introduction to this thesis: Through the ever growing amount of data and the desire to make them accessible to the user through the sense of listening, sonification, the representation of data by using sound has been subject of active research in the computer sciences and the field of HCI for the last 20 years. During this time, the field of sonification has diversified into different application areas: today, sound in auditory display informs the user about states and actions on the desktop and in mobile devices; sonification has been applied in monitoring applications, where sound can range from being informative to alarming; sonification has been used to give sensory feedback in order to close the action and perception loop; last but not least, sonifications have also been developed for exploratory data analysis, where sound is used to represent data with unknown structures for hypothesis building. Coming from the computer sciences and HCI, the conceptualization of sonification has been mostly driven by application areas. On the other hand, the sonic arts who have always contributed to the community of auditory display have a genuine focus on sound. Despite this close interdisciplinary relation of communities of sound practitioners, a rich and sound- (or listening)-centered concept about sonification is still missing as a point of departure for a more application and task overarching approach towards design guidelines. Complementary to the useful organization along fields of applications, a conceptual framework that is proper to sound needs to abstract from applications and also to some degree from tasks, as both are not directly related to sound. I hence propose in this thesis to conceptualize sonifications along two poles where sound serves either a normative or a descriptive purpose. In the beginning of auditory display research, a continuum between a symbolic and an analogic pole has been proposed by Kramer (1994a, page 21). In this continuum, symbolic stands for sounds that coincide with existing schemas and are more denotative, analogic stands for sounds that are informative through their connotative aspects. (compare Worrall (2009, page 315)). The notions of symbolic and analogic illustrate the struggle to find apt descriptions of how the intention of the listener subjects audible phenomena to a process of meaning making and interpretation. Complementing the analogic-symbolic continuum with descriptive and normative purposes of displays is proposed in the light of the recently increased research interest in listening modes and intentions. Similar to the terms symbolic and analogic, listening modes have been discussed in auditory display since the beginning usually in dichotomic terms which were either identified with the words listening and hearing or understood as musical listening and everyday listening as proposed by Gaver (1993a). More than 25 years earlier, four direct listening modes have been introduced by Schaeffer (1966) together with a 5th synthetic mode of reduced listening which leads to the well-known sound object. Interestingly, Schaeffer’s listening modes remained largely unnoticed by the auditory display community. Particularly the notion of reduced listening goes beyond the connotative and denotative poles of the continuum proposed by Kramer and justifies the new terms descriptive and normative. Recently, a new taxonomy of listening modes has been proposed by Tuuri and Eerola (2012) that is motivated through an embodied cognition approach. The main contribution of their taxonomy is that it convincingly diversifies the connotative and denotative aspects of listening modes. In the recently published sonification handbook, multimodal and interactive aspects in combination with sonification have been discussed as promising options to expand and advance the field by Hunt and Hermann (2011), who point out that there is a big need for a better theoretical foundation in order to systematically integrate these aspects. The main contribution of this thesis is to address this need by providing alternative and complementary design guidelines with respect to existing approaches, all of which have been conceived before the recently increased research interest in listening modes. None of the existing contributions to design frameworks integrates multimodality, and listening modes with a focus on exploratory data analysis, where sonification is conceived to support the understanding of complex data potentially helping to identify new structures therein. In order to structure this field the following questions are addressed in this thesis: • How do natural listening modes and reduced listening relate to the proposed normative and descriptive display purposes? • What is the relationship of multimodality and interaction with listening modes and display purposes? • How can the potential of embodied cognition based listening modes be put to use for exploratory data sonification? • How can listening modes and display purposes be connected to questions of aesthetics in the display? • How do data complexity and Parameter-mapping sonification relate to exploratory data analysis and listening modes