35,047 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
Convolutional Neural Networks Via Node-Varying Graph Filters
Convolutional neural networks (CNNs) are being applied to an increasing
number of problems and fields due to their superior performance in
classification and regression tasks. Since two of the key operations that CNNs
implement are convolution and pooling, this type of networks is implicitly
designed to act on data described by regular structures such as images.
Motivated by the recent interest in processing signals defined in irregular
domains, we advocate a CNN architecture that operates on signals supported on
graphs. The proposed design replaces the classical convolution not with a
node-invariant graph filter (GF), which is the natural generalization of
convolution to graph domains, but with a node-varying GF. This filter extracts
different local features without increasing the output dimension of each layer
and, as a result, bypasses the need for a pooling stage while involving only
local operations. A second contribution is to replace the node-varying GF with
a hybrid node-varying GF, which is a new type of GF introduced in this paper.
While the alternative architecture can still be run locally without requiring a
pooling stage, the number of trainable parameters is smaller and can be
rendered independent of the data dimension. Tests are run on a synthetic source
localization problem and on the 20NEWS dataset.Comment: Submitted to DSW 2018 (IEEE Data Science Workshop
A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation
Cellular electron cryo-tomography enables the 3D visualization of cellular
organization in the near-native state and at submolecular resolution. However,
the contents of cellular tomograms are often complex, making it difficult to
automatically isolate different in situ cellular components. In this paper, we
propose a convolutional autoencoder-based unsupervised approach to provide a
coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate
that the autoencoder can be used for efficient and coarse characterization of
features of macromolecular complexes and surfaces, such as membranes. In
addition, the autoencoder can be used to detect non-cellular features related
to sample preparation and data collection, such as carbon edges from the grid
and tomogram boundaries. The autoencoder is also able to detect patterns that
may indicate spatial interactions between cellular components. Furthermore, we
demonstrate that our autoencoder can be used for weakly supervised semantic
segmentation of cellular components, requiring a very small amount of manual
annotation.Comment: Accepted by Journal of Structural Biolog
Yeast Protein Interactome Topology Provides Framework for Coordinated-Functionality
The architecture of the network of protein-protein physical interactions in
Saccharomyces cerevisiae is exposed through the combination of two
complementary theoretical network measures, betweenness centrality and
`Q-modularity'. The yeast interactome is characterized by well-defined
topological modules connected via a small number of inter-module protein
interactions. Should such topological inter-module connections turn out to
constitute a form of functional coordination between the modules, we speculate
that this coordination is occurring typically in a pair-wise fashion, rather
than by way of high-degree hub proteins responsible for coordinating multiple
modules. The unique non-hub-centric hierarchical organization of the
interactome is not reproduced by gene duplication-and-divergence stochastic
growth models that disregard global selective pressures.Comment: Final, revised version. 13 pages. Please see Nucleic Acids open
access article for higher resolution figure
A Hierarchical Bayesian Framework for Constructing Sparsity-inducing Priors
Variable selection techniques have become increasingly popular amongst
statisticians due to an increased number of regression and classification
applications involving high-dimensional data where we expect some predictors to
be unimportant. In this context, Bayesian variable selection techniques
involving Markov chain Monte Carlo exploration of the posterior distribution
over models can be prohibitively computationally expensive and so there has
been attention paid to quasi-Bayesian approaches such as maximum a posteriori
(MAP) estimation using priors that induce sparsity in such estimates. We focus
on this latter approach, expanding on the hierarchies proposed to date to
provide a Bayesian interpretation and generalization of state-of-the-art
penalized optimization approaches and providing simultaneously a natural way to
include prior information about parameters within this framework. We give
examples of how to use this hierarchy to compute MAP estimates for linear and
logistic regression as well as sparse precision-matrix estimates in Gaussian
graphical models. In addition, an adaptive group lasso method is derived using
the framework.Comment: Submitted for publication; corrected typo
Word contexts enhance the neural representation of individual letters in early visual cortex
Visual context facilitates perception, but how this is neurally implemented remains unclear. One example of contextual facilitation is found in reading, where letters are more easily identified when embedded in a word. Bottom-up models explain this word advantage as a post-perceptual decision bias, while top-down models propose that word contexts enhance perception itself. Here, we arbitrate between these accounts by presenting words and nonwords and probing the representational fidelity of individual letters using functional magnetic resonance imaging. In line with top-down models, we find that word contexts enhance letter representations in early visual cortex. Moreover, we observe increased coupling between letter information in visual cortex and brain activity in key areas of the reading network, suggesting these areas may be the source of the enhancement. Our results provide evidence for top-down representational enhancement in word recognition, demonstrating that word contexts can modulate perceptual processing already at the earliest visual regions
- …