46,345 research outputs found
Conjunctive Visual and Auditory Development via Real-Time Dialogue
Human developmental learning is capable of
dealing with the dynamic visual world, speech-based
dialogue, and their complex real-time association.
However, the architecture that realizes
this for robotic cognitive development has
not been reported in the past. This paper takes
up this challenge. The proposed architecture does
not require a strict coupling between visual and
auditory stimuli. Two major operations contribute
to the “abstraction” process: multiscale temporal
priming and high-dimensional numeric abstraction
through internal responses with reduced variance.
As a basic principle of developmental learning,
the programmer does not know the nature
of the world events at the time of programming
and, thus, hand-designed task-specific representation
is not possible. We successfully tested the
architecture on the SAIL robot under an unprecedented
challenging multimodal interaction mode:
use real-time speech dialogue as a teaching source
for simultaneous and incremental visual learning
and language acquisition, while the robot is viewing
a dynamic world that contains a rotating object
to which the dialogue is referring
Preprocessing Solar Images while Preserving their Latent Structure
Telescopes such as the Atmospheric Imaging Assembly aboard the Solar Dynamics
Observatory, a NASA satellite, collect massive streams of high resolution
images of the Sun through multiple wavelength filters. Reconstructing
pixel-by-pixel thermal properties based on these images can be framed as an
ill-posed inverse problem with Poisson noise, but this reconstruction is
computationally expensive and there is disagreement among researchers about
what regularization or prior assumptions are most appropriate. This article
presents an image segmentation framework for preprocessing such images in order
to reduce the data volume while preserving as much thermal information as
possible for later downstream analyses. The resulting segmented images reflect
thermal properties but do not depend on solving the ill-posed inverse problem.
This allows users to avoid the Poisson inverse problem altogether or to tackle
it on each of 10 segments rather than on each of 10 pixels,
reducing computing time by a factor of 10. We employ a parametric
class of dissimilarities that can be expressed as cosine dissimilarity
functions or Hellinger distances between nonlinearly transformed vectors of
multi-passband observations in each pixel. We develop a decision theoretic
framework for choosing the dissimilarity that minimizes the expected loss that
arises when estimating identifiable thermal properties based on segmented
images rather than on a pixel-by-pixel basis. We also examine the efficacy of
different dissimilarities for recovering clusters in the underlying thermal
properties. The expected losses are computed under scientifically motivated
prior distributions. Two simulation studies guide our choices of dissimilarity
function. We illustrate our method by segmenting images of a coronal hole
observed on 26 February 2015
The Marketing of Political Candidates: Current Tactics and Future Strategies
Alexander Haig, Jr., a 1988 presidential hopeful, remarked, concerning campaign politics: I have learned the secret of life--it\u27s in marketing (1986). The emergence of marketing in non-business organizations, so well documented in recent writings, is accented by its ascendancy in politics (Laczniak and Caywood 1987; Four More years 1985; Mauser 1983; Rothschild 1978). Any campaign manager interested in applying orthodox marketing principles to his or her craft will find the use of marketing techniques in the political sphere particularly instructive
Guided Proofreading of Automatic Segmentations for Connectomics
Automatic cell image segmentation methods in connectomics produce merge and
split errors, which require correction through proofreading. Previous research
has identified the visual search for these errors as the bottleneck in
interactive proofreading. To aid error correction, we develop two classifiers
that automatically recommend candidate merges and splits to the user. These
classifiers use a convolutional neural network (CNN) that has been trained with
errors in automatic segmentations against expert-labeled ground truth. Our
classifiers detect potentially-erroneous regions by considering a large context
region around a segmentation boundary. Corrections can then be performed by a
user with yes/no decisions, which reduces variation of information 7.5x faster
than previous proofreading methods. We also present a fully-automatic mode that
uses a probability threshold to make merge/split decisions. Extensive
experiments using the automatic approach and comparing performance of novice
and expert users demonstrate that our method performs favorably against
state-of-the-art proofreading methods on different connectomics datasets.Comment: Supplemental material available at
http://rhoana.org/guidedproofreading/supplemental.pd
- …