4,128 research outputs found
Broken triangles: From value merging to a tractable class of general-arity constraint satisfaction problems
International audienceA binary CSP instance satisfying the broken-triangle property (BTP) can be solved in polynomial time. Unfortunately, in practice, few instances satisfy the BTP. We show that a local version of the BTP allows the merging of domain values in arbitrary instances of binary CSP, thus providing a novel polynomial-time reduction operation. Extensive experimental trials on benchmark instances demonstrate a significant decrease in instance size for certain classes of problems. We show that BTP-merging can be generalised to instances with constraints of arbitrary arity and we investigate the theoretical relationship with resolution in SAT. A directional version of general-arity BTP-merging then allows us to extend the BTP tractable class previously defined only for binary CSP. We investigate the complexity of several related problems including the recognition problem for the general-arity BTP class when the variable order is unknown, finding an optimal order in which to apply BTP merges and detecting BTP-merges in the presence of global constraints such as AllDifferent
Deep Directional Statistics: Pose Estimation with Uncertainty Quantification
Modern deep learning systems successfully solve many perception tasks such as
object pose estimation when the input image is of high quality. However, in
challenging imaging conditions such as on low-resolution images or when the
image is corrupted by imaging artifacts, current systems degrade considerably
in accuracy. While a loss in performance is unavoidable, we would like our
models to quantify their uncertainty in order to achieve robustness against
images of varying quality. Probabilistic deep learning models combine the
expressive power of deep learning with uncertainty quantification. In this
paper, we propose a novel probabilistic deep learning model for the task of
angular regression. Our model uses von Mises distributions to predict a
distribution over object pose angle. Whereas a single von Mises distribution is
making strong assumptions about the shape of the distribution, we extend the
basic model to predict a mixture of von Mises distributions. We show how to
learn a mixture model using a finite and infinite number of mixture components.
Our model allows for likelihood-based training and efficient inference at test
time. We demonstrate on a number of challenging pose estimation datasets that
our model produces calibrated probability predictions and competitive or
superior point estimates compared to the current state-of-the-art
On Broken Triangles (CP 2014)
International audienceA binary CSP instance satisfying the broken-triangle property (BTP) can be solved in polynomial time. Unfortunately, in practice, few instances satisfy the BTP. We show that a local version of the BTP allows the merging of domain values in binary CSPs, thus providing a novel polynomial-time reduction operation. Experimental trials on benchmark instances demonstrate a significant decrease in instance size for certain classes of problems. We show that BTP-merging can be generalised to instances with constraints of arbitrary arity. A directional version of the general-arity BTP then allows us to extend the BTP tractable class previously defined only for binary CSP
Autour des Triangles Cassés
National audienceUne instance CSP binaire qui satisfait la propriété des triangles cassés (BTP) peut etre résolue en temps polynomial. Malheureusement, en pratique, peu d'ins-tances satisfont cette propriété. Nous montrons qu'une version locale de BTP permet de fusionner des valeurs dans les domaines d'instances binaires quelconques. Des expérimentations démontrent la diminution significative de la taille de l'instance pour certaines classes de pro-bì emes. Ensuite, nous proposons une généralisation de cette fusion a des contraintes d'arité quelconque. En-fin, une version orientée nous permet d'´ etendre la classe polynomiale BTP. Ce papier est un résumé de l'article M. C. Cooper, A. El Mouelhi, C. Terrioux et B. Zanuttini. On Broken Triangles In Proceedings of CP,LNCS 8656, 9–24, 2014
A Dynamically Diluted Alignment Model Reveals the Impact of Cell Turnover on the Plasticity of Tissue Polarity Patterns
The polarisation of cells and tissues is fundamental for tissue morphogenesis
during biological development and regeneration. A deeper understanding of
biological polarity pattern formation can be gained from the consideration of
pattern reorganisation in response to an opposing instructive cue, which we
here consider by example of experimentally inducible body axis inversions in
planarian flatworms. Our dynamically diluted alignment model represents three
processes: entrainment of cell polarity by a global signal, local cell-cell
coupling aligning polarity among neighbours and cell turnover inserting
initially unpolarised cells. We show that a persistent global orienting signal
determines the final mean polarity orientation in this stochastic model.
Combining numerical and analytical approaches, we find that neighbour coupling
retards polarity pattern reorganisation, whereas cell turnover accelerates it.
We derive a formula for an effective neighbour coupling strength integrating
both effects and find that the time of polarity reorganisation depends linearly
on this effective parameter and no abrupt transitions are observed. This allows
to determine neighbour coupling strengths from experimental observations. Our
model is related to a dynamic -Potts model with annealed site-dilution and
makes testable predictions regarding the polarisation of dynamic systems, such
as the planarian epithelium.Comment: Preprint as prior to first submission to Journal of the Royal Society
Interface. 25 pages, 6 figures, plus supplement (18 pages, contains 1 table
and 7 figures). A supplementary movie is available from
https://dx.doi.org/10.6084/m9.figshare.c388781
A Neural Model of How the Brain Computes Heading from Optic Flow in Realistic Scenes
Animals avoid obstacles and approach goals in novel cluttered environments using visual information, notably optic flow, to compute heading, or direction of travel, with respect to objects in the environment. We present a neural model of how heading is computed that describes interactions among neurons in several visual areas of the primate magnocellular pathway, from retina through V1, MT+, and MSTd. The model produces outputs which are qualitatively and quantitatively similar to human heading estimation data in response to complex natural scenes. The model estimates heading to within 1.5° in random dot or photo-realistically rendered scenes and within 3° in video streams from driving in real-world environments. Simulated rotations of less than 1 degree per second do not affect model performance, but faster simulated rotation rates deteriorate performance, as in humans. The model is part of a larger navigational system that identifies and tracks objects while navigating in cluttered environments.National Science Foundation (SBE-0354378, BCS-0235398); Office of Naval Research (N00014-01-1-0624); National-Geospatial Intelligence Agency (NMA201-01-1-2016
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