17,389 research outputs found
A Rapidly Deployable Classification System using Visual Data for the Application of Precision Weed Management
In this work we demonstrate a rapidly deployable weed classification system
that uses visual data to enable autonomous precision weeding without making
prior assumptions about which weed species are present in a given field.
Previous work in this area relies on having prior knowledge of the weed species
present in the field. This assumption cannot always hold true for every field,
and thus limits the use of weed classification systems based on this
assumption. In this work, we obviate this assumption and introduce a rapidly
deployable approach able to operate on any field without any weed species
assumptions prior to deployment. We present a three stage pipeline for the
implementation of our weed classification system consisting of initial field
surveillance, offline processing and selective labelling, and automated
precision weeding. The key characteristic of our approach is the combination of
plant clustering and selective labelling which is what enables our system to
operate without prior weed species knowledge. Testing using field data we are
able to label 12.3 times fewer images than traditional full labelling whilst
reducing classification accuracy by only 14%.Comment: 36 pages, 14 figures, published Computers and Electronics in
Agriculture Vol. 14
Multiscale Markov Decision Problems: Compression, Solution, and Transfer Learning
Many problems in sequential decision making and stochastic control often have
natural multiscale structure: sub-tasks are assembled together to accomplish
complex goals. Systematically inferring and leveraging hierarchical structure,
particularly beyond a single level of abstraction, has remained a longstanding
challenge. We describe a fast multiscale procedure for repeatedly compressing,
or homogenizing, Markov decision processes (MDPs), wherein a hierarchy of
sub-problems at different scales is automatically determined. Coarsened MDPs
are themselves independent, deterministic MDPs, and may be solved using
existing algorithms. The multiscale representation delivered by this procedure
decouples sub-tasks from each other and can lead to substantial improvements in
convergence rates both locally within sub-problems and globally across
sub-problems, yielding significant computational savings. A second fundamental
aspect of this work is that these multiscale decompositions yield new transfer
opportunities across different problems, where solutions of sub-tasks at
different levels of the hierarchy may be amenable to transfer to new problems.
Localized transfer of policies and potential operators at arbitrary scales is
emphasized. Finally, we demonstrate compression and transfer in a collection of
illustrative domains, including examples involving discrete and continuous
statespaces.Comment: 86 pages, 15 figure
Predictability and hierarchy in Drosophila behavior
Even the simplest of animals exhibit behavioral sequences with complex
temporal dynamics. Prominent amongst the proposed organizing principles for
these dynamics has been the idea of a hierarchy, wherein the movements an
animal makes can be understood as a set of nested sub-clusters. Although this
type of organization holds potential advantages in terms of motion control and
neural circuitry, measurements demonstrating this for an animal's entire
behavioral repertoire have been limited in scope and temporal complexity. Here,
we use a recently developed unsupervised technique to discover and track the
occurrence of all stereotyped behaviors performed by fruit flies moving in a
shallow arena. Calculating the optimally predictive representation of the fly's
future behaviors, we show that fly behavior exhibits multiple time scales and
is organized into a hierarchical structure that is indicative of its underlying
behavioral programs and its changing internal states
Multivariate Approaches to Classification in Extragalactic Astronomy
Clustering objects into synthetic groups is a natural activity of any
science. Astrophysics is not an exception and is now facing a deluge of data.
For galaxies, the one-century old Hubble classification and the Hubble tuning
fork are still largely in use, together with numerous mono-or bivariate
classifications most often made by eye. However, a classification must be
driven by the data, and sophisticated multivariate statistical tools are used
more and more often. In this paper we review these different approaches in
order to situate them in the general context of unsupervised and supervised
learning. We insist on the astrophysical outcomes of these studies to show that
multivariate analyses provide an obvious path toward a renewal of our
classification of galaxies and are invaluable tools to investigate the physics
and evolution of galaxies.Comment: Open Access paper.
http://www.frontiersin.org/milky\_way\_and\_galaxies/10.3389/fspas.2015.00003/abstract\>.
\<10.3389/fspas.2015.00003 \&g
A nonparametric HMM for genetic imputation and coalescent inference
Genetic sequence data are well described by hidden Markov models (HMMs) in
which latent states correspond to clusters of similar mutation patterns. Theory
from statistical genetics suggests that these HMMs are nonhomogeneous (their
transition probabilities vary along the chromosome) and have large support for
self transitions. We develop a new nonparametric model of genetic sequence
data, based on the hierarchical Dirichlet process, which supports these self
transitions and nonhomogeneity. Our model provides a parameterization of the
genetic process that is more parsimonious than other more general nonparametric
models which have previously been applied to population genetics. We provide
truncation-free MCMC inference for our model using a new auxiliary sampling
scheme for Bayesian nonparametric HMMs. In a series of experiments on male X
chromosome data from the Thousand Genomes Project and also on data simulated
from a population bottleneck we show the benefits of our model over the popular
finite model fastPHASE, which can itself be seen as a parametric truncation of
our model. We find that the number of HMM states found by our model is
correlated with the time to the most recent common ancestor in population
bottlenecks. This work demonstrates the flexibility of Bayesian nonparametrics
applied to large and complex genetic data
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