23,114 research outputs found
A Methodology for Robust Multiproxy Paleoclimate Reconstructions and Modeling of Temperature Conditional Quantiles
Great strides have been made in the field of reconstructing past temperatures
based on models relating temperature to temperature-sensitive paleoclimate
proxies. One of the goals of such reconstructions is to assess if current
climate is anomalous in a millennial context. These regression based approaches
model the conditional mean of the temperature distribution as a function of
paleoclimate proxies (or vice versa). Some of the recent focus in the area has
considered methods which help reduce the uncertainty inherent in such
statistical paleoclimate reconstructions, with the ultimate goal of improving
the confidence that can be attached to such endeavors. A second important
scientific focus in the subject area is the area of forward models for proxies,
the goal of which is to understand the way paleoclimate proxies are driven by
temperature and other environmental variables. In this paper we introduce novel
statistical methodology for (1) quantile regression with autoregressive
residual structure, (2) estimation of corresponding model parameters, (3)
development of a rigorous framework for specifying uncertainty estimates of
quantities of interest, yielding (4) statistical byproducts that address the
two scientific foci discussed above. Our statistical methodology demonstrably
produces a more robust reconstruction than is possible by using
conditional-mean-fitting methods. Our reconstruction shares some of the common
features of past reconstructions, but also gains useful insights. More
importantly, we are able to demonstrate a significantly smaller uncertainty
than that from previous regression methods. In addition, the quantile
regression component allows us to model, in a more complete and flexible way
than least squares, the conditional distribution of temperature given proxies.
This relationship can be used to inform forward models relating how proxies are
driven by temperature
DeMoN: Depth and Motion Network for Learning Monocular Stereo
In this paper we formulate structure from motion as a learning problem. We
train a convolutional network end-to-end to compute depth and camera motion
from successive, unconstrained image pairs. The architecture is composed of
multiple stacked encoder-decoder networks, the core part being an iterative
network that is able to improve its own predictions. The network estimates not
only depth and motion, but additionally surface normals, optical flow between
the images and confidence of the matching. A crucial component of the approach
is a training loss based on spatial relative differences. Compared to
traditional two-frame structure from motion methods, results are more accurate
and more robust. In contrast to the popular depth-from-single-image networks,
DeMoN learns the concept of matching and, thus, better generalizes to
structures not seen during training.Comment: Camera ready version for CVPR 2017. Supplementary material included.
Project page:
http://lmb.informatik.uni-freiburg.de/people/ummenhof/depthmotionnet
DM-PhyClus: A Bayesian phylogenetic algorithm for infectious disease transmission cluster inference
Background. Conventional phylogenetic clustering approaches rely on arbitrary
cutpoints applied a posteriori to phylogenetic estimates. Although in practice,
Bayesian and bootstrap-based clustering tend to lead to similar estimates, they
often produce conflicting measures of confidence in clusters. The current study
proposes a new Bayesian phylogenetic clustering algorithm, which we refer to as
DM-PhyClus, that identifies sets of sequences resulting from quick transmission
chains, thus yielding easily-interpretable clusters, without using any ad hoc
distance or confidence requirement. Results. Simulations reveal that DM-PhyClus
can outperform conventional clustering methods, as well as the Gap procedure, a
pure distance-based algorithm, in terms of mean cluster recovery. We apply
DM-PhyClus to a sample of real HIV-1 sequences, producing a set of clusters
whose inference is in line with the conclusions of a previous thorough
analysis. Conclusions. DM-PhyClus, by eliminating the need for cutpoints and
producing sensible inference for cluster configurations, can facilitate
transmission cluster detection. Future efforts to reduce incidence of
infectious diseases, like HIV-1, will need reliable estimates of transmission
clusters. It follows that algorithms like DM-PhyClus could serve to better
inform public health strategies
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