5,745 research outputs found
Discussion of: A statistical analysis of multiple temperature proxies: Are reconstructions of surface temperatures over the last 1000 years reliable?
Discussion of "A statistical analysis of multiple temperature proxies: Are
reconstructions of surface temperatures over the last 1000 years reliable?" by
B.B. McShane and A.J. Wyner [arXiv:1104.4002]Comment: Published in at http://dx.doi.org/10.1214/10-AOAS409 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Barnes Hospital Bulletin
https://digitalcommons.wustl.edu/bjc_barnes_bulletin/1040/thumbnail.jp
A graphical, scalable and intuitive method for the placement and the connection of biological cells
We introduce a graphical method originating from the computer graphics domain
that is used for the arbitrary and intuitive placement of cells over a
two-dimensional manifold. Using a bitmap image as input, where the color
indicates the identity of the different structures and the alpha channel
indicates the local cell density, this method guarantees a discrete
distribution of cell position respecting the local density function. This
method scales to any number of cells, allows to specify several different
structures at once with arbitrary shapes and provides a scalable and versatile
alternative to the more classical assumption of a uniform non-spatial
distribution. Furthermore, several connection schemes can be derived from the
paired distances between cells using either an automatic mapping or a
user-defined local reference frame, providing new computational properties for
the underlying model. The method is illustrated on a discrete homogeneous
neural field, on the distribution of cones and rods in the retina and on a
coronal view of the basal ganglia.Comment: Corresponding code at https://github.com/rougier/spatial-computatio
Uncertainty in climate science and climate policy
This essay, written by a statistician and a climate scientist, describes our
view of the gap that exists between current practice in mainstream climate
science, and the practical needs of policymakers charged with exploring
possible interventions in the context of climate change. By `mainstream' we
mean the type of climate science that dominates in universities and research
centres, which we will term `academic' climate science, in contrast to `policy'
climate science; aspects of this distinction will become clearer in what
follows.
In a nutshell, we do not think that academic climate science equips climate
scientists to be as helpful as they might be, when involved in climate policy
assessment. Partly, we attribute this to an over-investment in high resolution
climate simulators, and partly to a culture that is uncomfortable with the
inherently subjective nature of climate uncertainty.Comment: submitted as contribution to Conceptual Foundations of
ClimateModeling, Winsberg, E. and Lloyd, E., eds., The University of Chicago
Pres
On the use of simple dynamical systems for climate predictions: A Bayesian prediction of the next glacial inception
Over the last few decades, climate scientists have devoted much effort to the
development of large numerical models of the atmosphere and the ocean. While
there is no question that such models provide important and useful information
on complicated aspects of atmosphere and ocean dynamics, skillful prediction
also requires a phenomenological approach, particularly for very slow
processes, such as glacial-interglacial cycles. Phenomenological models are
often represented as low-order dynamical systems. These are tractable, and a
rich source of insights about climate dynamics, but they also ignore large
bodies of information on the climate system, and their parameters are generally
not operationally defined. Consequently, if they are to be used to predict
actual climate system behaviour, then we must take very careful account of the
uncertainty introduced by their limitations. In this paper we consider the
problem of the timing of the next glacial inception, about which there is
on-going debate. Our model is the three-dimensional stochastic system of
Saltzman and Maasch (1991), and our inference takes place within a Bayesian
framework that allows both for the limitations of the model as a description of
the propagation of the climate state vector, and for parametric uncertainty.
Our inference takes the form of a data assimilation with unknown static
parameters, which we perform with a variant on a Sequential Monte Carlo
technique (`particle filter'). Provisional results indicate peak glacial
conditions in 60,000 years.Comment: superseeds the arXiv:0809.0632 (which was published in European
Reviews). The Bayesian section has been significantly expanded. The present
version has gone scientific peer review and has been published in European
Physics Special Topics. (typo in DOI and in Table 1 (psi -> theta) corrected
on 25th August 2009
A Computational Model of Spatial Memory Anticipation during Visual Search
Some visual search tasks require to memorize the location of stimuli that
have been previously scanned. Considerations about the eye movements raise the
question of how we are able to maintain a coherent memory, despite the frequent
drastically changes in the perception. In this article, we present a
computational model that is able to anticipate the consequences of the eye
movements on the visual perception in order to update a spatial memor
A computational approach to the covert and overt deployment of spatial attention
Popular computational models of visual attention tend to neglect the
influence of saccadic eye movements whereas it has been shown that the primates
perform on average three of them per seconds and that the neural substrate for
the deployment of attention and the execution of an eye movement might
considerably overlap. Here we propose a computational model in which the
deployment of attention with or without a subsequent eye movement emerges from
local, distributed and numerical computations
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