9,637 research outputs found
Co-regularized Alignment for Unsupervised Domain Adaptation
Deep neural networks, trained with large amount of labeled data, can fail to
generalize well when tested with examples from a \emph{target domain} whose
distribution differs from the training data distribution, referred as the
\emph{source domain}. It can be expensive or even infeasible to obtain required
amount of labeled data in all possible domains. Unsupervised domain adaptation
sets out to address this problem, aiming to learn a good predictive model for
the target domain using labeled examples from the source domain but only
unlabeled examples from the target domain. Domain alignment approaches this
problem by matching the source and target feature distributions, and has been
used as a key component in many state-of-the-art domain adaptation methods.
However, matching the marginal feature distributions does not guarantee that
the corresponding class conditional distributions will be aligned across the
two domains. We propose co-regularized domain alignment for unsupervised domain
adaptation, which constructs multiple diverse feature spaces and aligns source
and target distributions in each of them individually, while encouraging that
alignments agree with each other with regard to the class predictions on the
unlabeled target examples. The proposed method is generic and can be used to
improve any domain adaptation method which uses domain alignment. We
instantiate it in the context of a recent state-of-the-art method and observe
that it provides significant performance improvements on several domain
adaptation benchmarks.Comment: NIPS 2018 accepted versio
3D Registration of Aerial and Ground Robots for Disaster Response: An Evaluation of Features, Descriptors, and Transformation Estimation
Global registration of heterogeneous ground and aerial mapping data is a
challenging task. This is especially difficult in disaster response scenarios
when we have no prior information on the environment and cannot assume the
regular order of man-made environments or meaningful semantic cues. In this
work we extensively evaluate different approaches to globally register UGV
generated 3D point-cloud data from LiDAR sensors with UAV generated point-cloud
maps from vision sensors. The approaches are realizations of different
selections for: a) local features: key-points or segments; b) descriptors:
FPFH, SHOT, or ESF; and c) transformation estimations: RANSAC or FGR.
Additionally, we compare the results against standard approaches like applying
ICP after a good prior transformation has been given. The evaluation criteria
include the distance which a UGV needs to travel to successfully localize, the
registration error, and the computational cost. In this context, we report our
findings on effectively performing the task on two new Search and Rescue
datasets. Our results have the potential to help the community take informed
decisions when registering point-cloud maps from ground robots to those from
aerial robots.Comment: Awarded Best Paper at the 15th IEEE International Symposium on
Safety, Security, and Rescue Robotics 2017 (SSRR 2017
Structure-function mapping of a heptameric module in the nuclear pore complex.
The nuclear pore complex (NPC) is a multiprotein assembly that serves as the sole mediator of nucleocytoplasmic exchange in eukaryotic cells. In this paper, we use an integrative approach to determine the structure of an essential component of the yeast NPC, the ~600-kD heptameric Nup84 complex, to a precision of ~1.5 nm. The configuration of the subunit structures was determined by satisfaction of spatial restraints derived from a diverse set of negative-stain electron microscopy and protein domain-mapping data. Phenotypic data were mapped onto the complex, allowing us to identify regions that stabilize the NPC's interaction with the nuclear envelope membrane and connect the complex to the rest of the NPC. Our data allow us to suggest how the Nup84 complex is assembled into the NPC and propose a scenario for the evolution of the Nup84 complex through a series of gene duplication and loss events. This work demonstrates that integrative approaches based on low-resolution data of sufficient quality can generate functionally informative structures at intermediate resolution
Precise multimodal optical control of neural ensemble activity.
Understanding brain function requires technologies that can control the activity of large populations of neurons with high fidelity in space and time. We developed a multiphoton holographic approach to activate or suppress the activity of ensembles of cortical neurons with cellular resolution and sub-millisecond precision. Since existing opsins were inadequate, we engineered new soma-targeted (ST) optogenetic tools, ST-ChroME and IRES-ST-eGtACR1, optimized for multiphoton activation and suppression. Employing a three-dimensional all-optical read-write interface, we demonstrate the ability to simultaneously photostimulate up to 50 neurons distributed in three dimensions in a 550âĂâ550âĂâ100-”m3 volume of brain tissue. This approach allows the synthesis and editing of complex neural activity patterns needed to gain insight into the principles of neural codes
Path Similarity Analysis: a Method for Quantifying Macromolecular Pathways
Diverse classes of proteins function through large-scale conformational
changes; sophisticated enhanced sampling methods have been proposed to generate
these macromolecular transition paths. As such paths are curves in a
high-dimensional space, they have been difficult to compare quantitatively, a
prerequisite to, for instance, assess the quality of different sampling
algorithms. The Path Similarity Analysis (PSA) approach alleviates these
difficulties by utilizing the full information in 3N-dimensional trajectories
in configuration space. PSA employs the Hausdorff or Fr\'echet path
metrics---adopted from computational geometry---enabling us to quantify path
(dis)similarity, while the new concept of a Hausdorff-pair map permits the
extraction of atomic-scale determinants responsible for path differences.
Combined with clustering techniques, PSA facilitates the comparison of many
paths, including collections of transition ensembles. We use the closed-to-open
transition of the enzyme adenylate kinase (AdK)---a commonly used testbed for
the assessment enhanced sampling algorithms---to examine multiple microsecond
equilibrium molecular dynamics (MD) transitions of AdK in its substrate-free
form alongside transition ensembles from the MD-based dynamic importance
sampling (DIMS-MD) and targeted MD (TMD) methods, and a geometrical targeting
algorithm (FRODA). A Hausdorff pairs analysis of these ensembles revealed, for
instance, that differences in DIMS-MD and FRODA paths were mediated by a set of
conserved salt bridges whose charge-charge interactions are fully modeled in
DIMS-MD but not in FRODA. We also demonstrate how existing trajectory analysis
methods relying on pre-defined collective variables, such as native contacts or
geometric quantities, can be used synergistically with PSA, as well as the
application of PSA to more complex systems such as membrane transporter
proteins.Comment: 9 figures, 3 tables in the main manuscript; supplementary information
includes 7 texts (S1 Text - S7 Text) and 11 figures (S1 Fig - S11 Fig) (also
available from journal site
Simultaneous measurement of cosmology and intrinsic alignments using joint cosmic shear and galaxy number density correlations
Cosmic shear is a powerful method to constrain cosmology, provided that any
systematic effects are under control. The intrinsic alignment of galaxies is
expected to severely bias parameter estimates if not taken into account. We
explore the potential of a joint analysis of tomographic galaxy ellipticity,
galaxy number density, and ellipticity-number density cross-correlations to
simultaneously constrain cosmology and self-calibrate unknown intrinsic
alignment and galaxy bias contributions. We treat intrinsic alignments and
galaxy biasing as free functions of scale and redshift and marginalise over the
resulting parameter sets. Constraints on cosmology are calculated by combining
the likelihoods from all two-point correlations between galaxy ellipticity and
galaxy number density. The information required for these calculations is
already available in a standard cosmic shear dataset. We include contributions
to these functions from cosmic shear, intrinsic alignments, galaxy clustering
and magnification effects. In a Fisher matrix analysis we compare our
constraints with those from cosmic shear alone in the absence of intrinsic
alignments. For a potential future large area survey, such as Euclid, the extra
information from the additional correlation functions can make up for the
additional free parameters in the intrinsic alignment and galaxy bias terms,
depending on the flexibility in the models. For example, the Dark Energy Task
Force figure of merit is recovered even when more than 100 free parameters are
marginalised over. We find that the redshift quality requirements are similar
to those calculated in the absence of intrinsic alignments.Comment: 22 pages, 10 figures; extended discussion, otherwise minor changes to
match accepted version; published in Astronomy and Astrophysic
Coupled non-parametric shape and moment-based inter-shape pose priors for multiple basal ganglia structure segmentation
This paper presents a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. In biological tissues, such as the human brain, neighboring structures exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and inter-shape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm.
We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance (MR) images.
We present a set of 2D and 3D experiments as well as a quantitative performance analysis. In addition, we perform a comparison to several existent segmentation methods and demonstrate the improvements provided by our approach in terms of segmentation accuracy
Galaxy alignments: Observations and impact on cosmology
Galaxy shapes are not randomly oriented, rather they are statistically
aligned in a way that can depend on formation environment, history and galaxy
type. Studying the alignment of galaxies can therefore deliver important
information about the physics of galaxy formation and evolution as well as the
growth of structure in the Universe. In this review paper we summarise key
measurements of galaxy alignments, divided by galaxy type, scale and
environment. We also cover the statistics and formalism necessary to understand
the observations in the literature. With the emergence of weak gravitational
lensing as a precision probe of cosmology, galaxy alignments have taken on an
added importance because they can mimic cosmic shear, the effect of
gravitational lensing by large-scale structure on observed galaxy shapes. This
makes galaxy alignments, commonly referred to as intrinsic alignments, an
important systematic effect in weak lensing studies. We quantify the impact of
intrinsic alignments on cosmic shear surveys and finish by reviewing practical
mitigation techniques which attempt to remove contamination by intrinsic
alignments.Comment: 52 pages excl. references, 16 figures; minor changes to match version
published in Space Science Reviews; part of a topical volume on galaxy
alignments, with companion papers arXiv:1504.05456 and arXiv:1504.0554
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