79,820 research outputs found
Rethinking Map Legends with Visualization
This design paper presents new guidance for creating map legends in a dynamic environment. Our contribution is a set of guidelines for legend design in a visualization context and a series of illustrative themes through which they may be expressed. These are demonstrated in an applications context through interactive software prototypes. The guidelines are derived from cartographic literature and in liaison with EDINA who provide digital mapping services for UK tertiary education. They enhance approaches to legend design that have evolved for static media with visualization by considering: selection, layout, symbols, position, dynamism and design and process. Broad visualization legend themes include: The Ground Truth Legend, The Legend as Statistical Graphic and The Map is the Legend. Together, these concepts enable us to augment legends with dynamic properties that address specific needs, rethink their nature and role and contribute to a wider re-evaluation of maps as artifacts of usage rather than statements of fact. EDINA has acquired funding to enhance their clients with visualization legends that use these concepts as a consequence of this work. The guidance applies to the design of a wide range of legends and keys used in cartography and information visualization
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
We present the Bayesian Case Model (BCM), a general framework for Bayesian
case-based reasoning (CBR) and prototype classification and clustering. BCM
brings the intuitive power of CBR to a Bayesian generative framework. The BCM
learns prototypes, the "quintessential" observations that best represent
clusters in a dataset, by performing joint inference on cluster labels,
prototypes and important features. Simultaneously, BCM pursues sparsity by
learning subspaces, the sets of features that play important roles in the
characterization of the prototypes. The prototype and subspace representation
provides quantitative benefits in interpretability while preserving
classification accuracy. Human subject experiments verify statistically
significant improvements to participants' understanding when using explanations
produced by BCM, compared to those given by prior art.Comment: Published in Neural Information Processing Systems (NIPS) 2014,
Neural Information Processing Systems (NIPS) 201
Designing labeled graph classifiers by exploiting the R\'enyi entropy of the dissimilarity representation
Representing patterns as labeled graphs is becoming increasingly common in
the broad field of computational intelligence. Accordingly, a wide repertoire
of pattern recognition tools, such as classifiers and knowledge discovery
procedures, are nowadays available and tested for various datasets of labeled
graphs. However, the design of effective learning procedures operating in the
space of labeled graphs is still a challenging problem, especially from the
computational complexity viewpoint. In this paper, we present a major
improvement of a general-purpose classifier for graphs, which is conceived on
an interplay between dissimilarity representation, clustering,
information-theoretic techniques, and evolutionary optimization algorithms. The
improvement focuses on a specific key subroutine devised to compress the input
data. We prove different theorems which are fundamental to the setting of the
parameters controlling such a compression operation. We demonstrate the
effectiveness of the resulting classifier by benchmarking the developed
variants on well-known datasets of labeled graphs, considering as distinct
performance indicators the classification accuracy, computing time, and
parsimony in terms of structural complexity of the synthesized classification
models. The results show state-of-the-art standards in terms of test set
accuracy and a considerable speed-up for what concerns the computing time.Comment: Revised versio
Recommended from our members
Rapid Prototyping of PEM Fuel Cell Bi-Polar Plates using 3D Printing and Thermal Spray Deposition
This article presents the results of exploratory research on novel methods for the
fabrication of functional, metallic, gas flow, bi polar plates (BPP) for use in proton
exchange membrane fuel cells (PEMFC). Low cost, high speed, additive manufacturing
methods that combine 3D printing (3DP) and thermal spray (TS) technologies are
described. Functional flow plates were manufactured by creating 3DP patterns and then
depositing, and releasing, dense metals with TS methods. The new method yields dense
metal plates, with interesting options for material choices and complex designs.Mechanical Engineerin
Mixed-signal CNN array chips for image processing
Due to their local connectivity and wide functional capabilities, cellular nonlinear networks (CNN) are excellent candidates for the implementation of image processing algorithms using VLSI analog parallel arrays. However, the design of general purpose, programmable CNN chips with dimensions required for practical applications raises many challenging problems to analog designers. This is basically due to the fact that large silicon area means large development cost, large spatial deviations of design parameters and low production yield. CNN designers must face different issues to keep reasonable enough accuracy level and production yield together with reasonably low development cost in their design of large CNN chips. This paper outlines some of these major issues and their solutions
Advanced characterization and simulation of SONNE: a fast neutron spectrometer for Solar Probe Plus
SONNE, the SOlar NeutroN Experiment proposed for Solar Probe Plus, is designed to measure solar neutrons from 1-20 MeV and solar gammas from 0.5-10 MeV. SONNE is a double scatter instrument that employs imaging to maximize its signal-to-noise ratio by rejecting neutral particles from non-solar directions. Under the assumption of quiescent or episodic small-flare activity, one can constrain the energy content and power dissipation by fast ions in the low corona. Although the spectrum of protons and ions produced by nanoflaring activity is unknown, we estimate the signal in neutrons and γ−rays that would be present within thirty solar radii, constrained by earlier measurements at 1 AU. Laboratory results and simulations will be presented illustrating the instrument sensitivity and resolving power
- …