74,490 research outputs found
Visualising the structure of document search results: A comparison of graph theoretic approaches
This is the post-print of the article - Copyright @ 2010 Sage PublicationsPrevious work has shown that distance-similarity visualisation or ‘spatialisation’ can provide a potentially useful context in which to browse the results of a query search, enabling the user to adopt a simple local foraging or ‘cluster growing’ strategy to navigate through the retrieved document set. However, faithfully mapping feature-space models to visual space can be problematic owing to their inherent high dimensionality and non-linearity. Conventional linear approaches to dimension reduction tend to fail at this kind of task, sacrificing local structural in order to preserve a globally optimal mapping. In this paper the clustering performance of a recently proposed algorithm called isometric feature mapping (Isomap), which deals with non-linearity by transforming dissimilarities into geodesic distances, is compared to that of non-metric multidimensional scaling (MDS). Various graph pruning methods, for geodesic distance estimation, are also compared. Results show that Isomap is significantly better at preserving local structural detail than MDS, suggesting it is better suited to cluster growing and other semantic navigation tasks. Moreover, it is shown that applying a minimum-cost graph pruning criterion can provide a parameter-free alternative to the traditional K-neighbour method, resulting in spatial clustering that is equivalent to or better than that achieved using an optimal-K criterion
Feeling what you hear: tactile feedback for navigation of audio graphs
Access to digitally stored numerical data is currently very limited for sight impaired people. Graphs and visualizations are often used to analyze relationships between numerical data, but the current methods of accessing them are highly visually mediated. Representing data using audio feedback is a common method of making data more accessible, but methods of navigating and accessing the data are often serial in nature and laborious. Tactile or haptic displays could be used to provide additional feedback to support a point-and-click type interaction for the visually impaired. A requirements capture conducted with sight impaired computer users produced a review of current accessibility technologies, and guidelines were extracted for using tactile feedback to aid navigation. The results of a qualitative evaluation with a prototype interface are also presented. Providing an absolute position input device and tactile feedback allowed the users to explore the graph using tactile and proprioceptive cues in a manner analogous to point-and-click techniques
A Wikipedia Literature Review
This paper was originally designed as a literature review for a doctoral
dissertation focusing on Wikipedia. This exposition gives the structure of
Wikipedia and the latest trends in Wikipedia research
Finding undetected protein associations in cell signaling by belief propagation
External information propagates in the cell mainly through signaling cascades
and transcriptional activation, allowing it to react to a wide spectrum of
environmental changes. High throughput experiments identify numerous molecular
components of such cascades that may, however, interact through unknown
partners. Some of them may be detected using data coming from the integration
of a protein-protein interaction network and mRNA expression profiles. This
inference problem can be mapped onto the problem of finding appropriate optimal
connected subgraphs of a network defined by these datasets. The optimization
procedure turns out to be computationally intractable in general. Here we
present a new distributed algorithm for this task, inspired from statistical
physics, and apply this scheme to alpha factor and drug perturbations data in
yeast. We identify the role of the COS8 protein, a member of a gene family of
previously unknown function, and validate the results by genetic experiments.
The algorithm we present is specially suited for very large datasets, can run
in parallel, and can be adapted to other problems in systems biology. On
renowned benchmarks it outperforms other algorithms in the field.Comment: 6 pages, 3 figures, 1 table, Supporting Informatio
Electroencephalographic field influence on calcium momentum waves
Macroscopic EEG fields can be an explicit top-down neocortical mechanism that
directly drives bottom-up processes that describe memory, attention, and other
neuronal processes. The top-down mechanism considered are macrocolumnar EEG
firings in neocortex, as described by a statistical mechanics of neocortical
interactions (SMNI), developed as a magnetic vector potential . The
bottom-up process considered are waves prominent in synaptic
and extracellular processes that are considered to greatly influence neuronal
firings. Here, the complimentary effects are considered, i.e., the influence of
on momentum, . The canonical
momentum of a charged particle in an electromagnetic field, (SI units), is calculated, where the charge of
is , is the magnitude of the charge of an
electron. Calculations demonstrate that macroscopic EEG can be
quite influential on the momentum of ions, in
both classical and quantum mechanics. Molecular scales of
wave dynamics are coupled with fields developed at macroscopic
regional scales measured by coherent neuronal firing activity measured by scalp
EEG. The project has three main aspects: fitting models to EEG
data as reported here, building tripartite models to develop
models, and studying long coherence times of waves in the
presence of due to coherent neuronal firings measured by scalp
EEG. The SMNI model supports a mechanism wherein the interaction at tripartite synapses, via a dynamic centering
mechanism (DCM) to control background synaptic activity, acts to maintain
short-term memory (STM) during states of selective attention.Comment: Final draft. http://ingber.com/smni14_eeg_ca.pdf may be updated more
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