81,299 research outputs found
Dynamic similarity promotes interpersonal coordination in joint-action
Human movement has been studied for decades and dynamic laws of motion that
are common to all humans have been derived. Yet, every individual moves
differently from everyone else (faster/slower, harder/smoother etc). We propose
here an index of such variability, namely an individual motor signature (IMS)
able to capture the subtle differences in the way each of us moves. We show
that the IMS of a person is time-invariant and that it significantly differs
from those of other individuals. This allows us to quantify the dynamic
similarity, a measure of rapport between dynamics of different individuals'
movements, and demonstrate that it facilitates coordination during interaction.
We use our measure to confirm a key prediction of the theory of similarity that
coordination between two individuals performing a joint-action task is higher
if their motions share similar dynamic features. Furthermore, we use a virtual
avatar driven by an interactive cognitive architecture based on feedback
control theory to explore the effects of different kinematic features of the
avatar motion on the coordination with human players
An integrative analysis of cancer gene expression studies using Bayesian latent factor modeling
We present an applied study in cancer genomics for integrating data and
inferences from laboratory experiments on cancer cell lines with observational
data obtained from human breast cancer studies. The biological focus is on
improving understanding of transcriptional responses of tumors to changes in
the pH level of the cellular microenvironment. The statistical focus is on
connecting experimentally defined biomarkers of such responses to clinical
outcome in observational studies of breast cancer patients. Our analysis
exemplifies a general strategy for accomplishing this kind of integration
across contexts. The statistical methodologies employed here draw heavily on
Bayesian sparse factor models for identifying, modularizing and correlating
with clinical outcome these signatures of aggregate changes in gene expression.
By projecting patterns of biological response linked to specific experimental
interventions into observational studies where such responses may be evidenced
via variation in gene expression across samples, we are able to define
biomarkers of clinically relevant physiological states and outcomes that are
rooted in the biology of the original experiment. Through this approach we
identify microenvironment-related prognostic factors capable of predicting long
term survival in two independent breast cancer datasets. These results suggest
possible directions for future laboratory studies, as well as indicate the
potential for therapeutic advances though targeted disruption of specific
pathway components.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS261 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Detailed interpretation and analysis of selected corn blight watch data sets
A detailed interpretation and analysis of selected corn blight data set was undertaken in order to better define the present capabilities and limitations of agricultural remote multispectral sensing and automatic processing techniques and to establish the areas of investigation needing futher attention in the development of operational survey systems. While the emphasis of this effort was directed toward the detection of various corn blight levels, problems related to the more general task of crop identification were also investigated. Since the analog recognition computer (SPARC) was fully committed to the more routine aspects of processing and since the detailed interpretation and analysis required more in the way of quantitative information, the CDC 1604 digital computer was employed
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