Thursday, October 17, 2019
Location: Gould Simpson (GS) 906
Speakers: Kate Isaacs, Alex Bigelow, Katy Williams
Title: Three Short Visualization Talks: Design Methodology, Network Data Wrangling, and ASCII-only Graph Drawing
Speaker: Katy Williams
Title: Visualizing a Moving Target: A Design Study on Task Parallel Programs in the Presence of Evolving Data and Concerns
Abstract: Common pitfalls in visualization projects include lack of data availability and the domain users' needs and focus changing too rapidly for the design process to complete. While it is often prudent to avoid such projects, we argue it can be beneficial to engage them in some cases as the visualization process can help refine data collection, solving a "chicken and egg" problem of having the data and tools to analyze it. We found this to be the case in the domain of task parallel computing where such data and tooling is an open area of research. Despite these hurdles, we conducted a design study. Through a tightly-coupled iterative design process, we built Atria, a multi-view execution graph visualization to support performance analysis. Atria simplifies the initial representation of the execution graph by aggregating nodes as related to their line of code. We deployed Atria on multiple platforms, some requiring design alteration. We describe how we adapted the design study methodology to the "moving target" of both the data and the domain experts' concerns and how this movement kept both the visualization and programming project healthy.
Speaker: Alex Bigelow
Title: Origraph: Interactive Network Wrangling
Abstract: Networks are a natural way of thinking about many datasets. The data on which a network is based, however, is rarely collected in a form that suits the analysis process, making it necessary to create and reshape networks. Data wrangling is widely acknowledged to be a critical part of the data analysis pipeline, yet interactive network wrangling has received little attention in the visualization research community. In this talk, I discuss a set of operations that are important for wrangling network datasets and introduce a visual data wrangling tool, Origraph, that enables analysts to apply these operations to their datasets. Key operations include creating a network from source data such as tables, reshaping a network by introducing new node or edge classes, filtering nodes or edges, and deriving new node or edge attributes. Origraph enables analysts to execute these operations with little to no programming, and to immediately visualize the results. In addition, we introduce interfaces designed to aid analysts in specifying arguments for sensible network wrangling operations. We demonstrate the usefulness of Origraph through a use case: first in exploring the influence of money on the political support for the war in Yemen.
Speaker: Kate Isaacs
Title: Preserving Command Line Workflow for a Package Management System using ASCII DAG Visualization
Abstract: Package managers provide ease of access to applications by removing the time-consuming and sometimes completely prohibitive barrier of successfully building, installing, and maintaining the software for a system. Package management system developers, package maintainers, and users may consult the dependency graph of a package when a simple listing is insufficient for their analyses. However, users working in a remote command line environment must disrupt their workflow to visualize dependency graphs in graphical programs, possibly needing to move files between devices or incur forwarding lag. To preserve the command line workflow, we develop an interactive ASCII visualization for its dependency graphs. We evaluate the use of our visualization through a command line-centered study, comparing it to two existing approaches. We observe that despite the limitations of the ASCII representation, our visualization is preferred by participants when approached from a command line interface workflow.
Tuesday, August 27, 2019
Location: Gould Simpson (GS) 906
Speaker: Micheal Cherktov, Ph.d
Title: Gauges, Loops, and Polynomials for Partition Functions of
Graphical models (GM) represent multivariate and generally not normalized probability istributions. Computing the normalization factor, called the partition function (PF), is the main inference challenge relevant to multiple statistical and optimization applications. The problem is of an exponential complexity with respect to the number of variables. In this manuscript, aimed at approximating the PF, we consider Multi-Graph Models (MGMs) where binary variables and multivariable factors are associated with edges and nodes, respectively, of an undirected multi-graph. We suggest a new methodology for analysis and computations that combines the Gauge Function (GF) technique with the technique from the field of real stable polynomials. We show that the GF, representing a single-out term in a finite sum expression for the PF which achieves xtremum at the so-called Belief-Propagation (BP) gauge, has a natural polynomial representation in terms of gauges/variables associated with edges of the multi-graph. Moreover, GF can be used to recover the PF through a sequence of transformations allowing appealing algebraic and graphical interpretations. algebraically, one step in the sequence consists in application of a differential operator over gauges associated with an edge. Graphically, the sequence is interpreted as a repetitive elimination/contraction of edges resulting in MGMs on decreasing in size (number of edges) graphs with the same PF as in the original MGM.
Even though complexity of computing factors in the sequence of derived MGMs and respective GFs grow exponentially with the number of eliminated edges, polynomials associated with the new factors remain
bi-stable if the original factors have this property. Moreover, we show that BP estimations in the sequence do not decrease, each low-bounding the PF.
Michael Chertkov is the Director of the Applied Mathematics at the University of Arizona. He received a PhD in physics from the Weizmann Institute of Science in 1996 and spent three years at Princeton University as a R. H. Dicke Fellow in the Department of Physics. He joined Los Alamos National Lab in 1999, initially as a J. R. Oppenheimer Fellow in the Theoretical Division, where he led projects in the physics of algorithms, energy grid systems, physics and engineering informed data science, and machine learning for turbulence. He is a fellow of the American Physical Society (APS) and a senior member of IEEE.
Host: Stephen Kobourov