17,152 research outputs found
Challenges in identifying and interpreting organizational modules in morphology
Form is a rich concept that agglutinates information about the proportions and topological arrangement of body parts. Modularity is readily measurable in both features, the variation of proportions (variational modules) and the organization of topology (organizational modules). The study of variational modularity and of organizational modularity faces similar challenges regarding the identification of meaningful modules and the validation of generative processes; however, most studies in morphology focus solely on variational modularity, while organizational modularity is much less understood. A possible cause for this bias is the successful development in the last twenty years of morphometrics, and specially geometric morphometrics, to study patters of variation. This contrasts with the lack of a similar mathematical framework to deal with patterns of organization. Recently, a new mathematical framework has been proposed to study the organization of gross anatomy using tools from Network Theory, so‐called Anatomical Network Analysis (AnNA). In this essay, I explore the potential use of this new framework—and the challenges it faces in identifying and validating biologically meaningful modules in morphological systems—by providing working examples of a complete analysis of modularity of the human skull and upper limb. Finally, I suggest further directions of research that may bridge the gap between variational and organizational modularity studies, and discuss how alternative modeling strategies of morphological systems using networks can benefit from each other
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
Dynamic Bayesian Combination of Multiple Imperfect Classifiers
Classifier combination methods need to make best use of the outputs of
multiple, imperfect classifiers to enable higher accuracy classifications. In
many situations, such as when human decisions need to be combined, the base
decisions can vary enormously in reliability. A Bayesian approach to such
uncertain combination allows us to infer the differences in performance between
individuals and to incorporate any available prior knowledge about their
abilities when training data is sparse. In this paper we explore Bayesian
classifier combination, using the computationally efficient framework of
variational Bayesian inference. We apply the approach to real data from a large
citizen science project, Galaxy Zoo Supernovae, and show that our method far
outperforms other established approaches to imperfect decision combination. We
go on to analyse the putative community structure of the decision makers, based
on their inferred decision making strategies, and show that natural groupings
are formed. Finally we present a dynamic Bayesian classifier combination
approach and investigate the changes in base classifier performance over time.Comment: 35 pages, 12 figure
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