350 research outputs found
Phytoplankton Hotspot Prediction With an Unsupervised Spatial Community Model
Many interesting natural phenomena are sparsely distributed and discrete.
Locating the hotspots of such sparsely distributed phenomena is often difficult
because their density gradient is likely to be very noisy. We present a novel
approach to this search problem, where we model the co-occurrence relations
between a robot's observations with a Bayesian nonparametric topic model. This
approach makes it possible to produce a robust estimate of the spatial
distribution of the target, even in the absence of direct target observations.
We apply the proposed approach to the problem of finding the spatial locations
of the hotspots of a specific phytoplankton taxon in the ocean. We use
classified image data from Imaging FlowCytobot (IFCB), which automatically
measures individual microscopic cells and colonies of cells. Given these
individual taxon-specific observations, we learn a phytoplankton community
model that characterizes the co-occurrence relations between taxa. We present
experiments with simulated robot missions drawn from real observation data
collected during a research cruise traversing the US Atlantic coast. Our
results show that the proposed approach outperforms nearest neighbor and
k-means based methods for predicting the spatial distribution of hotspots from
in-situ observations.Comment: To appear in ICRA 2017, Singapor
Establishing the impact of luminous AGN with multi-wavelength observations and simulations
Cosmological simulations fail to reproduce realistic galaxy populations
without energy injection from active galactic nuclei (AGN) into the
interstellar medium (ISM) and circumgalactic medium (CGM); a process called
`AGN feedback'. Consequently, observational work searches for evidence that
luminous AGN impact their host galaxies. Here, we review some of this work.
Multi-phase AGN outflows are common, some with potential for significant
impact. Additionally, multiple feedback channels can be observed
simultaneously; e.g., radio jets from `radio quiet' quasars can inject
turbulence on ISM scales, and displace CGM-scale molecular gas. However,
caution must be taken comparing outflows to simulations (e.g., kinetic coupling
efficiencies) to infer feedback potential, due to a lack of comparable
predictions. Furthermore, some work claims limited evidence for feedback
because AGN live in gas-rich, star-forming galaxies. However, simulations do
not predict instantaneous, global impact on molecular gas or star formation.
The impact is expected to be cumulative, over multiple episodes.Comment: Accepted for publication in IAU Symposium 378 Conference Proceedings
"Black Hole Winds at all Scales
Streaming Gaussian Dirichlet Random Fields for Spatial Predictions of High Dimensional Categorical Observations
We present the Streaming Gaussian Dirichlet Random Field (S-GDRF) model, a
novel approach for modeling a stream of spatiotemporally distributed, sparse,
high-dimensional categorical observations. The proposed approach efficiently
learns global and local patterns in spatiotemporal data, allowing for fast
inference and querying with a bounded time complexity. Using a high-resolution
data series of plankton images classified with a neural network, we demonstrate
the ability of the approach to make more accurate predictions compared to a
Variational Gaussian Process (VGP), and to learn a predictive distribution of
observations from streaming categorical data. S-GDRFs open the door to enabling
efficient informative path planning over high-dimensional categorical
observations, which until now has not been feasible.Comment: 10 pages, 5 figures. Published in Springer Proceedings of Advanced
Robotics, ISER 2023 Conference Proceeding
ShapeCodes: Self-Supervised Feature Learning by Lifting Views to Viewgrids
We introduce an unsupervised feature learning approach that embeds 3D shape
information into a single-view image representation. The main idea is a
self-supervised training objective that, given only a single 2D image, requires
all unseen views of the object to be predictable from learned features. We
implement this idea as an encoder-decoder convolutional neural network. The
network maps an input image of an unknown category and unknown viewpoint to a
latent space, from which a deconvolutional decoder can best "lift" the image to
its complete viewgrid showing the object from all viewing angles. Our
class-agnostic training procedure encourages the representation to capture
fundamental shape primitives and semantic regularities in a data-driven
manner---without manual semantic labels. Our results on two widely-used shape
datasets show 1) our approach successfully learns to perform "mental rotation"
even for objects unseen during training, and 2) the learned latent space is a
powerful representation for object recognition, outperforming several existing
unsupervised feature learning methods.Comment: To appear at ECCV 201
Learning Shape Priors for Single-View 3D Completion and Reconstruction
The problem of single-view 3D shape completion or reconstruction is
challenging, because among the many possible shapes that explain an
observation, most are implausible and do not correspond to natural objects.
Recent research in the field has tackled this problem by exploiting the
expressiveness of deep convolutional networks. In fact, there is another level
of ambiguity that is often overlooked: among plausible shapes, there are still
multiple shapes that fit the 2D image equally well; i.e., the ground truth
shape is non-deterministic given a single-view input. Existing fully supervised
approaches fail to address this issue, and often produce blurry mean shapes
with smooth surfaces but no fine details.
In this paper, we propose ShapeHD, pushing the limit of single-view shape
completion and reconstruction by integrating deep generative models with
adversarially learned shape priors. The learned priors serve as a regularizer,
penalizing the model only if its output is unrealistic, not if it deviates from
the ground truth. Our design thus overcomes both levels of ambiguity
aforementioned. Experiments demonstrate that ShapeHD outperforms state of the
art by a large margin in both shape completion and shape reconstruction on
multiple real datasets.Comment: ECCV 2018. The first two authors contributed equally to this work.
Project page: http://shapehd.csail.mit.edu
Inspecting spectra with sound: proof-of-concept & extension to datacubes
We present a novel approach to inspecting galaxy spectra using sound, via
their direct audio representation ('spectral audification'). We discuss the
potential of this as a complement to (or stand-in for) visual approaches. We
surveyed 58 respondents who use the audio representation alone to rate 30
optical galaxy spectra with strong emission lines. Across three tests, each
focusing on different quantities measured from the spectra (signal-to-noise
ratio, emission-line width, & flux ratios), we find that user ratings are well
correlated with measured quantities. This demonstrates that physical
information can be independently gleaned from listening to spectral
audifications. We note the importance of context when rating these
sonifications, where the order examples are heard can influence responses.
Finally, we adapt the method used in this promising pilot study to spectral
datacubes. We suggest that audification allows efficient exploration of
complex, spatially-resolved spectral data.Comment: 6 pages, 3 figures, accepted for publication in RASTI. Supplementary
data (including animated figure) available at
https://doi.org/10.25405/data.ncl.2281644
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