Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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Flow The Psychology Of Optimal Experience

The Visual Display of Quantitative Information

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The Logic and Limits of Trust

An ongoing debate in the Visualization community concerns the role that visualization types play in data understanding. In human cognition, understanding and memorability are intertwined. As a first step towards being able to ask questions about impact and effectiveness, here we ask: 'What makes a visualization memorable?' We ran the largest scale visualization study to date using 2,070 single-panel visualizations, categorized with visualization type (e.g., bar chart, line graph, etc.), collected from news media sites, government reports, scientific journals, and infographic sources. Each visualization was annotated with additional attributes, including ratings for data-ink ratios and visual densities. Using Amazon's Mechanical Turk, we collected memorability scores for hundreds of these visualizations, and discovered that observers are consistent in which visualizations they find memorable and forgettable. We find intuitive results (e.g., attributes like color and the inclusion of a human recognizable object enhance memorability) and less intuitive results (e.g., common graphs are less memorable than unique visualization types). Altogether our findings suggest that quantifying memorability is a general metric of the utility of information, an essential step towards determining how to design effective visualizations.

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What Makes a Visualization Memorable

Defining trust is an important endeavor given its applicability to assessing public mood to much of the innovation in the newly formed autonomous industry, such as artificial intelligence (AI),medical bots, drones, autonomous vehicles, and smart factories [19].Through developing a reliable index or means to measure trust,this may have wide impact from fostering acceptance and adoption of smart systems to informing policy makers about the public atmosphere and willingness to adopt innovate change, and has been identified as an important indicator in a recent UK policy brief [8].In this paper, we reflect on the importance and potential impact of developing Visualization Psychology in the context of solving definitions and policy decision making problems for complex constructs such as "trust".

The Development of Visualization Psychology Analysis Tools to Account for Trust

In this paper, we introduce a framework for implementing deep copy on top of MPI. The process is initiated by passing just the root object of the dynamic data structure. Our framework takes care of all pointer traversal, communication, copying and reconstruction on receiving nodes. The benefit of our approach is that MPI users can deep copy complex dynamic data structures without the need to write bespoke communication or serialize/deserialize methods for each object. These methods can present a challenging implementation problem that can quickly become unwieldy to maintain when working with complex structured data. This paper demonstrates our generic implementation, which encapsulates both approaches. We analyze the approach with a variety of structures (trees, graphs (including complete graphs) and rings) and demonstrate that it performs comparably to hand written implementations, using a vastly simplified programming interface. We make the source code available completely as a convenient header file. Subjects Computer Networks and Communications, Distributed and Parallel Computing, Programming Languages

Implementing generalized deep-copy in MPI

This paper presents a novel color scheme designed to address the challenge of visualizing data series with large value ranges, where scale transformation provides limited support. We focus on meteo-rological data, where the presence of large value ranges is common. We apply our approach to meteorological scatterplots, as one of the most common plots used in this domain area. Our approach leverages the numerical representation of mantissa and exponent of the values to guide the design of novel “nested” color schemes, able to emphasize differences between magnitudes. Our user study evaluates the new designs, the state of the art color scales and rep-resentative color schemes used in the analysis of meteorological data: ColorCrafter, Viridis, and Rainbow. We assess accuracy, time and confidence in the context of discrimination (comparison) and interpretation (reading) tasks. Our proposed color scheme signifi-cantly outperforms the others in interpretation tasks, while showing comparable performances in discrimination tasks.

Color Coding of Large Value Ranges Applied to Meteorological Data

As we grow more reliant on AI systems for an increasing variety of applications in our lives, the need to understand and interpret such systems also becomes more pronounced, be it for improvement, trust, or legal liability. AI Planning is one type of task that provides explanation challenges, particularly due to the increasing complexity in generated plans and convoluted causal chains that connect actions and determine overall plan structure. While there are many recent techniques to support plan explanation, visual aids for navigating this data are quite limited. Furthermore, there is often a barrier between techniques focused on abstract planning concepts and domain-related explanations. In this paper, we present a visual analytics tool to support plan summarization and interaction, focusing in robotics domains using an actor-based structure. We show how users can quickly grasp vital information about actions involved in a plan and how they relate to each other. Finally, we present a framework used to design our tool, highlighting how general PDDL elements can be converted into visual representations and further connecting concept to domain.

Actor-Focused Interactive Visualization for AI Planning

One common goal nearly all global illumination algorithms aim for is solving the light transport problem. The most visual noticeable one in the general case is the diffuse-diffuse light transport between surfaces. Most global illumination algorithms are very good at solving it. In many global illumination solutions the Lambertian diffuse surface reflection model is assumed. However, over the years several other diffuse models have been introduced – mostly outside of the computer graphics community – that according to their authors mimic real surface behaviour more accurately. In this paper we evaluate some of the better known models and place them into the context of solving global illumination problems.

Rita Borgo

Papers

The Development of Visualization Psychology Analysis Tools to Account for Trust

Implementing generalized deep-copy in MPI

Color Coding of Large Value Ranges Applied to Meteorological Data

Actor-Focused Interactive Visualization for AI Planning

Evaluation of Different Diffuse Surface Reflection Models for Global Illumination.