55,518 research outputs found
Research issues in data modeling for scientific visualization
This article summarizes some topics of modeling as they impinge on the future development of scientific data visualization. The benefits from visualization techniques in analyzing data are well established, but to build on these pioneering efforts, one must recognize modeling as a distinct structural component in the larger context of visualization and problem-solving systems. Volume modeling is the entry way to this arena of future development, and model-based rendering describes how scientists will view the results. Important side developments such as multiresolution modeling and model-based segmentation will contribute structural capability to these systems. All of these components ultimately depend on the mathematical foundations of scattered data modeling and on model validation and standards to incorporate this modeling methodology into effective tools for scientific inquiry.Postprint (published version
Curriculum Guidelines for Undergraduate Programs in Data Science
The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program
met for the purpose of composing guidelines for undergraduate programs in Data
Science. The group consisted of 25 undergraduate faculty from a variety of
institutions in the U.S., primarily from the disciplines of mathematics,
statistics and computer science. These guidelines are meant to provide some
structure for institutions planning for or revising a major in Data Science
Hypertiling -- a high performance Python library for the generation and visualization of hyperbolic lattices
Hypertiling is a high-performance Python library for the generation and
visualization of regular hyperbolic lattices embedded in the Poincar\'e disk
model. Using highly optimized, efficient algorithms, hyperbolic tilings with
millions of vertices can be created in a matter of minutes on a single
workstation computer. Facilities including computation of adjacent vertices,
dynamic lattice manipulation, refinements, as well as powerful plotting and
animation capabilities are provided to support advanced uses of hyperbolic
graphs. In this manuscript, we present a comprehensive exploration of the
package, encompassing its mathematical foundations, usage examples,
applications, and a detailed description of its implementation.Comment: 52 pages, 20 figure
Veni Vidi Vici, A Three-Phase Scenario For Parameter Space Analysis in Image Analysis and Visualization
Automatic analysis of the enormous sets of images is a critical task in life
sciences. This faces many challenges such as: algorithms are highly
parameterized, significant human input is intertwined, and lacking a standard
meta-visualization approach. This paper proposes an alternative iterative
approach for optimizing input parameters, saving time by minimizing the user
involvement, and allowing for understanding the workflow of algorithms and
discovering new ones. The main focus is on developing an interactive
visualization technique that enables users to analyze the relationships between
sampled input parameters and corresponding output. This technique is
implemented as a prototype called Veni Vidi Vici, or "I came, I saw, I
conquered." This strategy is inspired by the mathematical formulas of numbering
computable functions and is developed atop ImageJ, a scientific image
processing program. A case study is presented to investigate the proposed
framework. Finally, the paper explores some potential future issues in the
application of the proposed approach in parameter space analysis in
visualization
Predicativity and parametric polymorphism of Brouwerian implication
A common objection to the definition of intuitionistic implication in the
Proof Interpretation is that it is impredicative. I discuss the history of that
objection, argue that in Brouwer's writings predicativity of implication is
ensured through parametric polymorphism of functions on species, and compare
this construal with the alternative approaches to predicative implication of
Goodman, Dummett, Prawitz, and Martin-L\"of.Comment: Added further references (Pistone, Poincar\'e, Tabatabai, Van Atten
Understanding Visualization: A formal approach using category theory and semiotics
This article combines the vocabulary of semiotics and category theory to provide a formal analysis of visualization. It shows how familiar processes of visualization fit the semiotic frameworks of both Saussure and Peirce, and extends these structures using the tools of category theory to provide a general framework for understanding visualization in practice, including: relationships between systems, data collected from those systems, renderings of those data in the form of representations, the reading of those representations to create visualizations, and the use of those visualizations to create knowledge and understanding of the system under inspection. The resulting framework is validated by demonstrating how familiar information visualization concepts (such as literalness, sensitivity, redundancy, ambiguity, generalizability, and chart junk) arise naturally from it and can be defined formally and precisely. This article generalizes previous work on the formal characterization of visualization by, inter alia, Ziemkiewicz and Kosara and allows us to formally distinguish properties of the visualization process that previous work does not
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